# **Collaborative Networks, Decision Systems, Web Applications and Services for Supporting Engineering and Production Management**

Edited by Leonilde Varela and Goran D. Putnik Printed Edition of the Special Issue Published in *Future Internet*

www.mdpi.com/journal/futureinternet

## **Collaborative Networks, Decision Systems, Web Applications and Services for Supporting Engineering and Production Management**

## **Collaborative Networks, Decision Systems, Web Applications and Services for Supporting Engineering and Production Management**

Editors

**Leonilde Varela Goran D. Putnik**

MDPI • Basel • Beijing • Wuhan • Barcelona • Belgrade • Manchester • Tokyo • Cluj • Tianjin

*Editors* Leonilde Varela University of Minho Portugal

Goran D. Putnik University of Minho Portugal

*Editorial Office* MDPI St. Alban-Anlage 66 4052 Basel, Switzerland

This is a reprint of articles from the Special Issue published online in the open access journal *Future Internet* (ISSN 1999-5903) (available at: https://www.mdpi.com/journal/futureinternet/ special issues/CNDSWAS SEPM).

For citation purposes, cite each article independently as indicated on the article page online and as indicated below:

LastName, A.A.; LastName, B.B.; LastName, C.C. Article Title. *Journal Name* **Year**, *Volume Number*, Page Range.

**ISBN 978-3-0365-5933-9 (Hbk) ISBN 978-3-0365-5934-6 (PDF)**

© 2022 by the authors. Articles in this book are Open Access and distributed under the Creative Commons Attribution (CC BY) license, which allows users to download, copy and build upon published articles, as long as the author and publisher are properly credited, which ensures maximum dissemination and a wider impact of our publications.

The book as a whole is distributed by MDPI under the terms and conditions of the Creative Commons license CC BY-NC-ND.

## **Contents**

## **About the Editors**

#### **Leonilde Varela**

Leonilde Varela is Assistant Professor at Department of Production and Systems, and an integrated member of the Algoritmi Research Centre, at University of Minho, Portugal, were she received her Ph.D. in Industrial Engineering and Management in 2007, and has worked since 1994, in teaching, researching, in addition to collaborating in management tasks, namely regarding the coordination of the industrial management and systems subgroup in which she has integrated since 1994, and coordinated from 2012 to 2021. She has also integrated the steering committee of the master's course in Systems Engineering, between 2016 and 2019, and currently, since 2021, she is the director of the Quality Engineering and Management master course. Her main research interests are in Manufacturing Management, Collaborative Management, Production Planning and Control, Optimization, Artificial Intelligence (AI), Meta-heuristics, Scheduling, Web based Systems, Services and technologies, mainly for supporting Engineering and Production Management, Collaborative Networks, Decision Making Models, Methods and Systems, and Virtual, Distributed and Networked Enterprises. She has published more than 200 refereed scientific papers in international conferences and in international scientific books and journals, conference proceeding books, indexed in the Web of Science and/or in the Scopus data bases, and other books. She coordinates several research projects, namely with financial support from the national Foundation for Science and Technology, along with Ph.D. and M.Sc. projects, in the area of Production and Systems Engineering, namely concerning the development of collaborative management models, methods, and tools, varying from more traditional decision support approaches to advanced intelligent systems and web-based platforms. She is a member of several international institutions and research groups, such as the Machine Intelligence Research Labs, Scientific Network for Innovation and Research Excellence (MirLabs), in the Euro Working Group of Decision Support Systems, in the Institute of Electrical and Electronics Engineers, in the Industrial Engineering Network, and in the Institute of Industrial and Systems Engineers, having served as organizing and publication or scientific chair, in many international conferences.

#### **Goran D. Putnik**

Goran D. Putnik was born in Belgrade, Serbia, in 1954. He received the B.S. and M.S. degrees in production engineering from the Faculty of Mechanical Engineering, University of Belgrade, and the DrSci (Ph.D.) in mechanical engineering from University of Belgrade, in 1993. After graduation, he joined the Motor Industry Rakovica, Belgrade, Serbia, and 3 years later joined the Department of Production Engineering at Faculty of Mechanical Engineering, University of Belgrade as Assistant Researcher. In 1993, he joined the University of Minho, Portugal, where he is currently Full Professor in the Department of Production and Systems Engineering, School of Engineering. His research and teaching interests are in the areas of production systems, languages, automata theory and production systems formal theory, and chaos and complexity management in organizations. He is the author of 13 books, more than 250 articles, and coordinator of a number of international projects. He is member of the IFIP WG 5.5—"COoperation infrastructure for Virtual Enterprises and electronic business—COVE", and of the International Academy for Production Engineering—CIRP, Paris.

## **Preface to "Collaborative Networks, Decision Systems, Web Applications and Services for Supporting Engineering and Production Management"**

This book addresses collaborative networks, decision systems, web applications and services for supporting engineering and production management, along with other social oriented services.

The main scope and purpose consists on presenting a general overview of some main issues related to collaborative approaches and practices, aiming at raising awareness about the importance of collaboration not just in engineering and manufacturing and management contexts, but also in other areas, namely in social science's domain, along with computer science and science and technology, in general.

The main motivation underlying the publication of this book is to provide a summarized set of representative work regarding the application of methodologies, approaches, tools and systems that enable us to put into practice collaboration among different kind of entities, varying from human- to machine-centered focus, and occurring in different kinds of industrial and social contexts.

This book is not just recommended to all readers that intend to achieve or clarify the importance of collaboration, but also to provide some concrete illustrative examples of its application domains.

The editors thank the important contribution provided by the following list of the co-authors that did enable the publication of this book, and to the fundamental support of the underlying institutions, namely regarding financial entities.

Angelica Fl ´ orez, Faculty of Systems and Informatics Engineering, Pontifical Bolivarian ´ University, Bucaramanga, Colombia.

Carlos Florez, Faculty of Civil Engineering, Francisco de Paula Santander University, C ´ ucuta, ´ Colombia.

Ciro Gamboa, Faculty of Electrical and Electronic Engineering, Pontifical Bolivarian University, Bucaramanga, Colombia.

Ekaterina V. Orlova, Department of Economics and Management, Ufa State Aviation Technical University, Ufa, Russia.

Giuseppe Mangioni, Dipartimento di Ingegneria Elettrica Elettronica Informatica, Universita di ` Catania, Viale Andrea Doria, Catania, Italy.

Goran Putnik, Department of Production and Systems, School of Engineering, University of Minho, Guimaraes, Portugal. ˜

Gustavo Ospina, Faculty of Civil Engineering, Pontifical Bolivarian University, Bucaramanga 680006, Colombia.

Inigo Pombo, Faculty of Engineering Bilbao, University of the Basque Country (UPV/EHU), ˜ Plaza Ingeniero Torres Quevedo, 1, 48013 Bilbao, Bizkaia, Spain.

Jacopo Fior, Dipartimento di Automatica e Informatica, Politecnico di Torino, Corso Duca Degli Abruzzi, Torino, Italy.

Jose Antonio Sanchez, Faculty of Engineering Bilbao, University of the Basque Country ´ (UPV/EHU), Plaza Ingeniero Torres Quevedo, 1, 48013 Bilbao, Bizkaia, Spain.

Julian Miranda, Faculty of Systems and Informatics Engineering, Pontifical Bolivarian ´ University, Bucaramanga 680006, Colombia.

Leire Godino, Faculty of Engineering Bilbao, University of the Basque Country (UPV/EHU), Plaza Ingeniero Torres Quevedo, 1, 48013 Bilbao, Bizkaia, Spain.

Leonilde Varela, Department of Production and Systems, School of Engineering, University of Minho, Braga, Portugal.

Luca Cagliero, Dipartimento di Automatica e Informatica, Politecnico di Torino, Corso Duca Degli Abruzzi, Torino, Italy.

Marco Grassia, Dipartimento di Ingegneria Elettrica Elettronica Informatica, Universita di ` Catania, Viale Andrea Doria, Catania, Italy.

Michele Malgeri, Dipartimento di Ingegneria Elettrica Elettronica Informatica, Universita di ` Catania, Viale Andrea Doria, Catania, Italy.

Miguel Altuve, Faculty of Electrical and Electronic Engineering, Pontifical Bolivarian University, Bucaramanga, Colombia.

Paolo Garza, Dipartimento di Automatica e Informatica, Politecnico di Torino, Corso Duca Degli Abruzzi, Torino, Italy.

Rafael Lizarralde, IDEKO, Arriaga Kalea, 2, 20870 Elgoibar, Gipuzkoa, Spain. Thirupathi Samala, Department of Mechanical Engineering, NIT Warangal, Warangal, India.

Vijaya Kumar Manupati, Operations and Supply Chain Division, NITIE Mumbai, Mumbai, India.

Vincenza Carchiolo, Dipartimento di Ingegneria Elettrica Elettronica Informatica, Universita di ` Catania, Viale Andrea Doria, Catania, Italy.

#### **Leonilde Varela and Goran D. Putnik**

*Editors*

## *Editorial* **Collaborative and Intelligent Networks and Decision Systems and Services for Supporting Engineering and Production Management**

**Leonilde Varela \* and Goran D. Putnik**

Department of Production and Systems, School of Engineering, University of Minho, 4804-533 Guimarães, Portugal

**\*** Correspondence<EMAIL_ADDRESS>
Collaborative networks and systems (CNS) have received much attention in recent decades to reach a competitive advantage. Many contributions have arisen from the industrial context to service-oriented companies, for instance, in the scope of artificial intelligence. Therefore, many contributions have been put forward related to collaborative and intelligent networks and systems [1–7].

Despite the wide range of existing work in this area, however, it continues to be imperative for companies to understand and anticipate the importance of CNS in manufacturing to enable them to reach a competitive advantage in the current global market and Industry 4.0-oriented manufacturing scenario [8,9].

These main topics strengthen the specific characteristics of CN through collaboration to deliver products and services; decentralize decision-making; and achieve inter- and intra-organizational integration to meet imposed performance requirements in competitive global markets [10–12].

Moreover, in the context of CNS, normalization is a crucial step in all decision models to produce comparable and dimensionless data from heterogeneous data [5]. Therefore, it is of upmost importance to use appropriate data-normalization techniques for each application scenario, for instance, according to the kind of multicriteria or multiobjective optimization methods or algorithms used for networked supply and operations management [2,5,13]. This is even more important in the upcoming increasingly digital era of I4.0, along with the perceived need for big data processing in terms of the need for the vertical and horizontal integration of data and manufacturing processes [6,10,14,15].

This Special Issue intends to contribute to collaborative and intelligent networks and systems supporting engineering and production management, as well as fill the gap in theories and practical applications supporting industrial companies through suitable methods and solutions.

Collaborative Engineering (CE) assumes an important role in Industry 4.0 (or I4.0) [16,17], namely, in the context of Collaborative Networks (CN), which includes a diverse set of companies, business partners, suppliers, and other stakeholders, including customers [7,18,19]. These entities are thus connected and communicate to enable CE practices and accomplish Collaborative Manufacturing and Management (CollM&M). CollM&M further implies sharing something between these entities, including some tangible or intangible asset, e.g., manufacturing resources and/or management information [7]. By doing so, the collaborating entities envision the co-creation of a product and/or service [6,7], for which I4.0's technologies are of utmost importance to enable and promote such joint practices, which include human–human, human–machine, and machine–machine interactions [6,12,20].

The widened set of I4.0 technologies permits the development and application of a varying range of management paradigms, approaches, and methods through the use of appropriate and diverse types of supporting tools, systems, and platforms [6,8,9,11,12,20–24], including:

**Citation:** Varela, L.; Putnik, G.D. Collaborative and Intelligent Networks and Decision Systems and Services for Supporting Engineering and Production Management. *Future Internet* **2022**, *14*, 318. https:// doi.org/10.3390/fi14110318

Received: 13 October 2022 Accepted: 27 October 2022 Published: 2 November 2022

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

The accomplishment this widened set of approaches, methods, tools, systems, and platforms implies the use of appropriate CollM&M paradigms, which are related to dynamic, distributed, integrated, intelligent/predictive, parallel, and real-time-based contributions [6,24–28].

Moreover, CollM&M has to ensure the fulfillment of an appropriate set of varying objectives or performance measures, which include companies' internal and external goals, and are frequently contradicting, thus further requiring the application of multicriteria and/or multiobjective and intelligent optimization methods, along with data acquisition, normalization techniques, and tools, to enable further incomplete and uncertain data processing, visualization, and analysis [2,5,13,25,29].

The cyber–physical (production) systems (C[P]PS) and smart factories, based on intelligent sensing systems, open and networked and distributed manufacturing systems, along with virtual organizations and extended manufacturing environments, play a fundamental role in I4.0 [22,24,29,30]. In such advanced manufacturing systems (AMS), integration, distributivity, virtuality, agility, servitization, digitalization, and decentralization are major issues for reaching suitable collaborative processes and practices in the I4.0. In this regard, the (Industrial) Internet of Things ((I)IoT), along with smart and ubiquitous networks based on cloud technology, enable large and complex networks and their digitalization [2–4,6,10,14] to carry out CollM&M [7]. In this regard, decisions and related actions must be taken promptly and be further supported by appropriate data-visualization systems [6,14,15,31,32].

Cloud-based M&M technology is, therefore, fundamental to enabling enhanced interoperability and collaborative practices (Varela et al., 2019c; Ferreira et al., 2022). Moreover, horizontal and vertical integration between companies, business partners, factories, suppliers, other stakeholders, and clients is fundamental in I4.0 [2,10,13,14,32,33].

Exponential technology, along with advanced processes, high-performance computing, and disruptive technologies (e.g., automation and robotics, autonomous and collaborative robots, advanced mechatronics, micro- and nano-manufacturing, and supercomputing) are key enablers for proper M&M in I4.0 [6,7,12,32,33].

Moreover, advanced interfaces, virtual, augmented, and mixed reality technology, along with simulation and digital twins, further promote and enhance CollM&M among collaborating entities. These technologies enable advanced and integrated decision-support systems (DSS) and databases (DB), along with knowledge engineering and knowledge bases (KB), automatic data acquisition, and a semantic web for enhancing collaboration [7].

In addition, data science, along with business intelligence, big data processing, and data analytics, are essential pillars of I4.0 supporting CollM&M practices [6,7].

All these issues are crucial for enabling advanced, integrated, and intelligent supply networks, projects, businesses, and their interoperable and fully supported implementation, to reach M&M while ensuring high-quality product development and M&M practices based on appropriate standards, means, and communication devices and protocols, to fully ensure appropriate extended supply network management strategies [5,8,10,15,32–34].

This Special Issue aims to provide new insights regarding CollM&M models and practices, aligned with the contemporary needs regarding the capability of co-creation actions supported by I4.0 technology [7].

In this regard, each of the six selected papers of this Special Issue makes a novel contribution to this purpose.

Pombo et al. present expectations and limitations of cyber–physical systems (CPS) for advanced manufacturing in the scope of the grinding industry. In their work, the authors refer to the importance of grinding technology in the manufacturing of high-added-value precision parts, accounting for approximately 20 to 25% of the whole machining costs in the industrialized world and relying heavily on the experience and knowledge of the operatives. Thus, the authors conclude that suitable approaches are needed to overcome these issues, and digital twin technology is promising in this regard by contributing to the reduction and possibly even the elimination of unnecessary trial-and-error strategies through intensive collaboration between all the involved agents from the university to the industry. The authors highlight the successful implementation of this technique in developing new and more realistic models for predicting wheel wear.

Miranda et al. propose a system model for offline seismic event detection in Colombia. The authors put forward an integrated model that includes five sub-models and is based on a machine-learning approach, and they highlight its suitability for identifying P-wave windows in historical records that permit detect seismic events. Their proposed model permits seeking, gathering, and storing seismic data, along with data reading, analysis, sampling, and classification. The authors further provide some recommendations regarding their model's implementation in developing a seismic-event detection system.

Samala et al. put forward a systematic literature review (SLR) about investigating degradation and upgradation models for flexible unit systems. In their work, the authors research the so-called flexible unit systems (FUS) in the current I4.0 era and the context of descriptive, predictive, and prescriptive analysis, aiming at integrating distributed and digitalized systems. The authors highlight that the existing literature mostly focuses individually on the descriptive, predictive, and prescriptive analysis paradigms. Moreover, the authors also claim that the literature is unclear about the integration of degradation and upgradation models for FUS. Thus, the authors carried out an SLR, through which it was possible to identify five main issues about degradation—residual life distribution, workload adjustment strategy, upgradation, and predictive maintenance—as major performance measures to investigate the performance of the FUS. In this study, it was understood that the degradation rate would affect the life and production rate of different configurations of FUS. Moreover, it was possible to realize that the considered upgradation model and predictive maintenance, along with advanced analytics procedures of the manufacturing systems, are valuable and enable the systems to run with higher production rates while

increasing the life of systems. Moreover, it was also possible to explore three research objectives related to system configuration flexibility to improve the proposed FUS and identify further research opportunities in this field.

Carchiolo et al. focus on co-authorship networks analysis to discover collaboration patterns among Italian researchers. Through their study about the analysis of behaviors of a large community of researchers and their correlations between the underlying environments, the authors determined a set of grouping rules by law or specific institutional policies that enabled conclusions about their performance and, importantly, affecting metrics to evaluate the quality of their research carried out. To this end, the coauthors created a procedure to craft a large dataset of Italian academic researchers by considering a set of performance indices and co-authorship information. Through their study, the authors could automate the association of profiles and the mapping of publications to reduce the use of computational resources. Moreover, the authors presented several examples of how the information extracted from the datasets can help better understand the dynamics influencing scientific performances.

Fior, Cagliero, and Garza refer in their work to leveraging explainable AI to support cryptocurrency. The authors clarify that this research area has been attracting the attention of many researchers and continues to be a very important research focus for private and professional traders and investors. They further mention that forecasting financial markets can be properly approached by using algorithmic trading systems based on AI models, which are becoming more and more developed. Moreover, the authors state that such approaches usually suffer from a lack of transparency, thus hindering domain experts from directly monitoring the fundamentals behind market movements. Additionally, they mention that this is particularly critical for cryptocurrency investors because studying the main factors influencing cryptocurrency prices, including the characteristics of the blockchain infrastructure, is crucial for driving experts' decisions. Thus, in their paper, the authors propose a new visual analytics tool to support domain experts in explaining AI-based cryptocurrency trading systems. To further describe the rationale behind AI models, their proposed approach exploits an established method, the SHapley Additive exPlanations, which, according to their results, allows experts to identify the most discriminating features and provides them with an interactive and easy-to-use graphical interface.

Orlova approached design technology and AI-based decision-making models for digital twin engineering. This study proposes comprehensive technology (methodological approach) for digital twin design to accelerate its engineering. The author clarifies that this kind of technology consists of design steps, methods, and models and provides systems synthesis of digital twins for a complex system (object or process) operating under uncertainty that can reconfigure in response to internal faults or environment changes and perform preventive maintenance. The author mentions that the proposed technology structure was developed based on a simulation model using situational "what-if" analysis and based on fuzzy logic methods. The author applied this technology to develop a digital twin prototype for a device at a creation life cycle stage to reduce the consequences of unpredicted and undesirable states. Through the study, it was possible to realize unforeseen problems and device faults during its further operation. According to the author, the proposed model identifies a situation as a combination of failure factors of the internal and external environment and provides an appropriate decision about actions with the device. Further, the authors mention that the practical significance of the research is the developed decision support model, which is the basis for control systems to solve problems related to monitoring the current state of technical devices (instruments, equipment) and supporting adequate decisions to eliminate their dysfunctions.

Undertaking this Special Issue, "Collaborative and Intelligent Networks and Decision Systems and Services for Supporting Engineering and Production Management", was a challenging and rewarding task for the Editors. The diversity of the manuscripts demonstrates the broad scope and relevance of the research theme in fostering performance and transformation for achieving collaborative practices in I4.0.

List of Contributions:

**Author Contributions:** Conceptualization, and methodology, L.V. and G.D.P.; original draft preparation and writing, L.V.; review and editing, L.V. and G.D.P. All authors have read and agreed to the published version of the manuscript.

**Funding:** This work was supported by national funds through the FCT—Fundação para a Ciência e Tecnologia, through the R&D Units Project Scopes: UIDB/00319/2020, UIDB/50014/2020, and EXPL/EME-SIS/1224/2021.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**

## *Review* **Investigation of Degradation and Upgradation Models for Flexible Unit Systems: A Systematic Literature Review**

**Thirupathi Samala 1, Vijaya Kumar Manupati 1, Maria Leonilde R. Varela 2,\* and Goran Putnik <sup>2</sup>**

**Abstract:** Research on flexible unit systems (FUS) with the context of descriptive, predictive, and prescriptive analysis have remarkably progressed in recent times, being now reinforced in the current Industry 4.0 era with the increased focus on integration of distributed and digitalized systems. In the existing literature, most of the work focused on the individual contributions of the above mentioned three analyses. Moreover, the current literature is unclear with respect to the integration of degradation and upgradation models for FUS. In this paper, a systematic literature review on degradation, residual life distribution, workload adjustment strategy, upgradation, and predictive maintenance as major performance measures to investigate the performance of the FUS has been considered. In order to identify the key issues and research gaps in the existing literature, the 59 most relevant papers from 2009 to 2020 have been sorted and analyzed. Finally, we identify promising research opportunities that could expand the scope and depth of FUS.

**Keywords:** flexible unit systems; degradation; residual life distribution; workload strategy; upgradation; predictive maintenance

#### **1. Introduction**

Recently, the manufacturing systems domain underwent a paradigm shift by introducing several key enabling technologies as a requirement of Industry 4.0 [1]. Keeping in mind clients' customized requirements and global manufacturers' personalized production, the current production and process capabilities need to be transformed. For example, recent requirements such as shorter product life cycles, high production rates, jobs complexity, quality products, and cost effectiveness are the most significant factors for any manufacturing industry [2]. Considering all the foregoing requirements, and, in addition, according with the current market demand and society requests, there is a need to enhance the system's capabilities by maintaining it under control from system breakdowns and several external forces that have not been considered as a highest priority in the past decade. To accomplish these challenges, there is a need for high machine availability, flexibility, configurability, and accessibility of manufacturing processes, as mentioned in [3–9]), along with another interesting contribution for emphasizing the necessity of increasing the level of flexibility of manufacturing systems, which can be seen in https://publications.muet.edu.pk/index.php/muetrj (accessed on 23 January 2021). However, various manufacturing systems available to fulfil the above-mentioned requirements have costs affairs and high maintenance. In this review paper, we introduced a special kind of configuration: i.e., flexible unit systems (FUS) with one degree of flexibility, two degrees of flexibility, semi flexibility, and highly flexible configurations, where the reconfiguration and upgradation of unit (machine) systems are easily achieved [10,11].

The common factors from different studies that affect FUS are identified as degradation rate, residual life distribution, workload strategy, upgradation, and predictive maintenance.

**Citation:** Samala, T.; Manupati, V.K.; Varela, M.L.R.; Putnik, G. Investigation of Degradation and Upgradation Models for Flexible Unit Systems: A Systematic Literature Review. *Future Internet* **2021**, *13*, 57. https://doi.org/10.3390/fi13030057

Academic Editor: Stefano Rinaldi

Received: 24 January 2021 Accepted: 19 February 2021 Published: 25 February 2021

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

To improve the health status of the system and to make the manufacturing functions effective and efficient, system-level health monitoring is new thinking to which nowadays researchers are paying attention. Therefore, the degradation rate at the system level is of the highest priority. Studies have shown that manufacturing systems are subjected to degradation both with age and usage, including wear, cracking, and fatigue, among others; whereas the residual life of a machine was characterized as remaining useful till its level of degradation arrives at a predefined failure threshold [12]. Real-time production data from complex systems produce a huge variety and volume of data. Handling this kind of dataintensive system with conventional statistical tools may be insufficient when firms seek to strategically conceal the data [13]. Hence, there is a need for advanced analytics such as descriptive, predictive, and prescriptive analytics to analyze the machine's historical data to improve the efficiency of the system by knowing the health condition at every stage.

Given this scenario, towards summarizing the status of present research and to stimulate the future investigations, the main aim of this paper is to carry out a Systematic Literature Review (SLR) with respect to the degradation and upgradation models for FUS. Hence, a review of manufacturing systems in the context of three analytics has been considered, particularly with flexibility as a key common word. The analysis of the reviewed literature enabled us to develop a comprehensive conceptualization as shown in (Figure 1). It is the conceptualization that was used to classify the findings and it was also referenced for future research.

**Figure 1.** Framework addressing the topics affecting flexible unit systems (FUS).

The paper is structured as follows. In Section 2, a detailed research methodology is used, which follows SLR's five-step approach. Effectiveness of degradation and upgradation models on the FUS and findings have been presented in Section 3. Discussion and

Future research agenda is explained in Section 4. Conclusions and future work directions are pointed out in Section 5.

#### **2. Research Methodology**

This research followed the SLR as a basic scientific activity that delivers a clear and comprehensive overview compared to descriptive literature reviews. The formation of a basic framework for an in-depth analysis and a scientific process can be possible by using this SLR. The systematic literature followed a sequence of five steps, as mentioned in [10], which are as follows.

#### Step 1. Formation of questions:

Research Question 1. What is the role of degradation, residual life distribution, workload strategy, upgradation, and predictive maintenance on flexible unit systems?

Research Question 2. How to integrate the degradation and upgradation models to the flexible unit systems?

#### Step 2. Finding the studies:

This step concerns how to find and choose the bibliographic database or search engine, and additionally the search strings. The research questions have been considered in this search for literature reviews. Following similar literature reviews [14–16] and three bibliographic databases, i.e., Web of Science, Scopus, and Science Direct, a remarkable quantity of published literature on degradation rate, residual life distribution, workload strategy, upgradation, and predictive maintenance, including very relevant and important journals in this area, has been considered. Additionally, also considered were advanced analytics, like descriptive, predictive, and prescriptive ones, to analyze the machine's historical data for improving the efficiency of the system.

Tables 1–3 show the search strings searched in the data bases and the results obtained using the three mentioned databases. However, sorting the selected research articles and selecting the publication title between 2009–2020 shows 603 articles for the search string "Flexible unit systems" (or) "Flexible machine systems" and "Degradation" (or) "Degradation rate", 167 articles for the search string "Flexible unit systems" (or) "Flexible machine systems" and "Residual Life Distribution" (or) "Residual life", 140 articles for the search string "Flexible unit systems" (or) "Flexible machine systems" and "workload strategy" (or) "workload adjustment", 104 articles for the search string "Flexible unit systems" (or) "Flexible machine systems" and "Upgradation", and 243 articles for the search string "Flexible unit systems" (or) "Flexible machine systems" and "Predictive Maintenance", respectively.

**Table 1.** Search string and number of results from Web of Science.

**Table 2.** Search string and number of results from Scopus.

**Table 3.** Search string and Number of Results from Science direct.

Step 3. Study preference and Evaluation:

In this step, filtering criteria were explicated, to choose only relevant studies to add in the review, in which the studies actually addressed the research questions. From 1995 to 2008, articles were excluded because they were just consigned to the small percentage of the examples. 11 years (2009–2020) of related studies were performed to focus on recent studies, methodologies, and technologies. The article journals of document type were sorted from the search results and the best articles distributed in peer-reviewed journals in English were contemplated. Colicchia et al. [17] argue that restricting the search to peer-reviewed journals enables one to reach better results due to the rigorous reviewing processes inherent to such articles before their publication.

This exercise reduces the number of journal articles to 198. After checking the duplicates (initially in each search string and after, taking into consideration all search strings set together), titles and abstracts of the selected journal articles were analyzed for relevance, which enabled us to further reduce the number of articles to 106. Articles qualified for review had to fulfil the five major criteria: (i) articles related to finding the Degradation level of manufacturing systems, (ii) articles related to finding the residual life of manufacturing systems, (iii) articles related to adjustment strategy of workload to reduce the degradation level of manufacturing systems, (iv) articles related to upgradation of manufacturing systems, and (v) articles focused on predictive maintenance of manufacturing systems. At this step, the number of articles for investigation was 106. At last, a more examined analysis of the 66 articles was made with the full gratified review.

Step 4. Investigation and Combination:

In this step, the content of each paper was analyzed to identify the key issues. Through full-content review, different articles were excluded, which were not as per the specified research focus of this study. In this way, the number of definite articles for the investigation was reduced to 59, as recorded in Table 4.

**Table 4.** Summary of articles preferences and evaluation.

Step 5. Reporting and using the results:

The data contained in 59 articles were summarized, then prepared with connected categories, for example, methodologies used in their research and various key findings. Table 5 shows the list of journals related to the number of articles published as well as the year of publication. *Reliability Engineering and Systems Safety*, *International Journal of Advanced Manufacturing Technology*, *IIE Transactions on Automation Science and Engineering*, *Journal of Intelligent Manufacturing*, *IFAC online*, *CIRP Annals: Manufacturing Technology*, and *IEEE Transactions on Reliability* contributed to 55% of the total articles published

related to factors (degradation, residual life distribution, workload strategy, upgradation, and predictive maintenance) related to manufacturing systems. Other journals like the *Journal of Computers & Industrial Engineering*, *IEEE Transactions*, *Journal of Manufacturing Systems*, *Procedia Manufacturing*, *European Journal of Operations Research*, and a few other journals contributed to 45% of the total journal articles published related to factors affecting manufacturing systems.

**Table 5.** List of journals related to the parameters related to the flexible unit systems.

#### **3. Findings**

The relevant data were collected and studies arranged dependent on five factors, mentioned in the research methodology. The detailed description of these five factors and their relevance under study is as follows.

#### *3.1. Prognostics and Health Management (PHM) for Unit Systems*

In recent years, PHM has emerged as an essential approach in the global competitive market, achieving advantages over others by improving system maintainability and reliability. However, the application of PHM to flexible unit systems is a challenging task as systems are more complex. Specifically, small and medium-sized ventures experienced difficulty in applying PHM, because of the lack of resources and time for research and development.

Shin et al. [18] explored how the Prognostics method is an intelligent answer for enhancing the availability of unit systems and fault prognosis to evaluate residual life. A PHM model for manufacturing systems integrated with different online sensors with different flexible structures has been developed by [19], and Hao et al. [12] proposed a contemporary sign partition as well as prognostics structure for multi-section systems with non-resolute segment signals, and Fang et al. [20] developed a prognostic procedure that uses multi-stream signals for predicting the residual life of partially degraded manufacturing systems.

#### 3.1.1. Throughput Rate

The throughput rate is significant for the design and the activity of manufacturing systems. A remarkable quantity of throughput rate related research has been developed to estimate the throughput of manufacturing systems by creating analytical methods with various unreliable machines. Hao et al. [12] characterized the "throughput rate" of a manufacturing system, which is equivalent to summing up all the workloads from each unit. Table 6 shows the literature related to degradation of manufacturing systems. In FUS, this performance measure is considered one of the important expected outputs due to its direct relevance for capacity. For example, if a FUS consists of three different machines with different capacities, then the maximum throughput is considered as the summation of all three machines. If the expected demand is less than the capacity of the system, the throughput rate is equal to the demand, otherwise the throughput rate is equal to the total capacity.

**Table 6.** Literature review on degradation rate related to flexible unit systems.

#### 3.1.2. Degradation

Degradation is a stochastic process, which will occur through random shocks and also through the components being worn in manufacturing processes. Degradation rate plays a significant role in the life of FUS because the impact of the degradation process on different types of manufacturing systems are observed on the failure severity. A Degraded machine impacts on the nature of the parts manufactured where the defectives rely upon the production rate, as has been mentioned in [28]. Zied et al. [27] worked on the degradation of the unit as stated by the rate of production. Hajej et al. [26] explained that their examination is to investigate the impact of the production rate on the degradation level and machine availability. Through this diverse literature, it was shown that the degradation process was grouped in two ways, i.e., continuous degradation and discrete degradation.

Zhenggeng et al. [21] explained about multiple degradation methods, which involve continuous degradation, as well as that discrete degradations have been modelled through various stochastic processes, for example, Markov renewal and gamma processes. Zhang et al. [29] proposed that the conventional Wiener process-dependent degradation is an important degradation model technique for manufacturing systems. With this, the past research on the degradation of manufacturing systems showed that efforts have been made to characterize the relation between degradation rate and workload adjustment strategy by using a Bayesian approach to find the residual life distribution literature, as is mentioned below in Table 7.

**Table 7.** Literature review on residual life distribution related to flexible unit systems.

#### 3.1.3. Residual Life Distribution

A machine's or a component's residual life estimation during its operation based on its present condition is very important in order to find its health condition. Li et al. [30] proposed a remaining useful life prediction by introducing the degradation rate changing to transition function, and it jumps the degradation signals towards the measurement function. For example, in the manufacturing industry, the usage of a prognostic health management system for deciding the residual life of a milling cutter in a high-speed milling machine depends on externally measured conditions, as has been mentioned in [33].

Bian et al. [32] introduced how prediction of the life of a complex manufacturing system needs an exact estimation of degradation conditions of its constituent parts as well as an adequate understanding of how these stages progress in the future. Si et al. [34] proposed s degradation method to anticipate the remaining useful life of machines utilizing a recursive channel calculation. Zhang et al. [29] surveyed modelling improvements of the Wiener process strategies for degradation information examination, remaining useful life estimation as their implementation in the empirics of the health management of manufacturing systems. Mosallam et al. [31] presented two stages of an information-driven strategy for remaining useful life prediction. It is noted that based on the residual life of a manufacturing unit, a workload adjustment strategy will be helpful to maintain the production rate mentioned in Hao et al. [12]. The various literature related to workload strategy has been mentioned below in Table 8.

**Table 8.** Literature review on workload strategy related to flexible unit systems.

#### 3.1.4. Workload Strategy

A dynamic workload adjustment technique has been proposed by [36] to locate the most extreme workload machinery. In their work, the highest degraded machines were identified to satisfy the production necessities on parallel configurations. With various benchmark instances, simulation tests have been conducted to assess the degradation rate. Li et al. [35] explored the effects of various workload adjustment methodologies on

a system agent-based simulation approach. To prevent the overlap of machine failure within a period of time, Hao et al. [39] developed a method to control the degradation and predicted failure time of each machine by adjusting the workload. Similarly, the allocation of buffer capacity is especially important in order to obtain an acceptable throughput and work-in-progress, as mentioned in [40].

#### 3.1.5. Descriptive and Predictive Model Management

The arrangement of the present smart manufacturing systems is subjected to the capacity for (a) sensibly modelling the production system, (b) predictable plant information, (c) solving issues proficiently with computational attempts, and (d) including feedback to raise the decision-making on top of time. Hence, enabling descriptive and predictive analytics for the estimation of manufacturing systems performance is a greater concern in the current information and digital age.

#### 3.1.6. Resource Management

Resource management is the way towards planning, scheduling, and allocating resources in the best possible way. More observation is on future manufacturing, where resource management is a greater concern and it must handle more proficiently. Particularly different manufacturing, as well as automotive industries, are advancing towards utilization of resources to improve proficiency and profitability without trading off the current manufacturing capacity. De Ryck et al. [41] proposed a methodology that makes resource management in automated guided vehicle systems more effective. The resource management aims at providing robust strategies in manufacturing systems to accomplish the resource allocation and to solve related issues, for example, resource levelling, and production layout adjustment in production planning.

#### *3.2. Diagnostics for Unit Systems*

Present manufacturing systems are outfitted with different sensors that provide continuous checking and diagnosis, but sensors cannot be equipped across all the parts in the manufacturing system due to big data challenges. These outcomes in non-observable parts limit our capacity to help successful and continuous real-time monitoring and fault diagnosis activities. The exact diagnosis is the most significant step because the fault is the primary cause of a manufacturing system's failure in the fault treatment. Among a wide range of possible faults in a manufacturing system, operative faults occur most often (about 70%). Djelloul et al. [42] solved maintenance optimization issues in manufacturing systems by considering the diagnosis and suggested a hybrid neural network technique focusing on developing a diagnosis system. Qin et al. [43] proposed that a fault identification, as well as a diagnostic module, is depicted dependent on an internal programmable logical controller. Generally, manufacturing industries have a large number of machines with different old programmable logic controllers that can benefit from an upgrade to new technology. The literature related to the upgradation of manufacturing equipment is mentioned below in Table 9.

**Table 9.** Literature review on upgradation related to flexible unit systems.

#### 3.2.1. Upgradation

According to [48], there are four motivations for the upgradation of manufacturing equipment. They are support, cost performance, reliability, and need for change. Pavlov et al. [47] considered the issues of choosing and upgrading equipment for making and upgrading manufacturing systems on facilities with discrete manufacturing. An example has been taken and it solved an excess of ten equipment choice test issues for the plan and upgrade of manufacturing systems. Garcia-Garza et al. [44] present a strategy to identify and upgrade a data preparation unit to make it viable with an extensive system as it advances into an Industry 4.0 condition.

Grohn et al. [46] investigated the upgradation of a production system model with mechanical capacities, and the experimental study incorporates changing of a mechanical plant and relocation of computerization system programming to another, more distributed machinery configuration. Xingyu et al. [23] present a reconfigurable manufacturing systems decision-making model to ideally decide and alter operational activities continuously considering demand fulfilling, system health, and maintenance cost. Furthermore, predictive maintenance will help to maintain the system's health in an efficient way. The literature related to predictive maintenance of the flexible unit systems is mentioned below in Table 10.

3.2.2. Predictive Maintenance

Nowadays, predictive maintenance is considered as the key point for many manufacturing industries because of a major part of the operational cost and system failure impacts on product quality and equipment availability. Menezes et al. [55] explained that predictive maintenance considers close past information for predicting future tendencies, biases, behaviors, etc. through correlation. He et al. [53] introduced that predictive maintenance is an analytic technique to eliminate prospective failures and improve the mission dependability of production systems. Consequently, a coordinated predictive maintenance procedure considering item degree and mission dependability state was proposed from reasoning of prediction and manufacturing. Spendla et al. [52] proposal focused on predictive maintenance of manufacturing systems to improve the production process quality.

Dong et al. [19] have attempted to work on a flexible structure of a versatile manufacturing system to satisfy different needs and item varieties and to build up a PHM structure for assembling with different online sensors and flexible structures utilizing different sensors-based degradation data for registering and predicting each machine's time to failure. For example, Traini et al. [49] discussed the execution of predictive maintenance of

milling cutting tool information, and the collection as validation of a structure, and [56,57] presented a model-driven approach using embedded artificial intelligence strategies by the development and implementation of a quality monitoring framework, and also presented a sensory system for high precision monitoring, applicable to all machining and milling operations on conductive materials. Kevin et al. [58] proposed a sensory updated degradation based predictive maintenance strategy. Their proposed maintenance strategy used degradation methods that combine part-specific continuous degradation data obtained during activity to predict the remaining useful life distribution. Yildirim et al. [54] expanded an adaptive predictive generator maintenance model that has been presented. From the different literature, on predictive maintenance, it can be concluded that the predictive maintenance of the machines allows extending of the machine's life and the lowering of maintenance costs by addressing the problems before they cause machine failures.

#### 3.2.3. Data Management

The need for more flexible and efficient data management in manufacturing systems is necessary to secure the maximum productivity for many manufacturing organizations. The systems require precise and current information as ongoing activity to meet users' expectations. For example, information and communication technology take part in a significant role in the factors of Industry 4.0, and data management becomes a major problem for different types of manufacturing systems. The related literature, such as Song et al. [59], focused on data management that explains the defective data generated by the unsuitable operation of cyber components of a manufacturing cyber-physical system. Similarly, Liu et al. [60] proposed an application of a Digital Twin technology in the manufacturing area to show a significant effect in enabling the manufacturing data management.

#### 3.2.4. Prescriptive Model Management

The prescriptive maintenance empowers manufacturers to resolve their own maintenance needs without the need for a vast array of experts, as mentioned by Brian Brinkmann. Menezes et al [55] explained that prescriptive analytics finds the best route to operate (outputs) in the view of given information and models (inputs). Similarly, Lepenioti et al. [61] said that prescriptive analytics tries to locate the best action for the future in the manufacturing industry and it is frequently considered as the subsequent stage towards improving data analytics maturity for business execution improvement. Moreover, prescriptive analytic strategies, such as decision optimization, can handle profoundly complex issues running from hundreds to a large number of limitations that would never be analyzed manually, and Matyas et al. [62] proposed a prescriptive maintenance methodology for manufacturing systems analysis, as well as simulation tools, that have been utilized to analyze past data, i.e., machine failure data and product quality data, to guarantee a high level of process flexibility and the quality of the product.

#### 3.2.5. Operations Management

The job of operations management is to oversee the process of converting resources into goods and services. Hashemi-Petroodi et al. [63] focused on the challenges of the interactions between machine robots and humans in order to find the effective contributions of operation management's methods to improve the working condition of hybrid manufacturing systems. Kozjek et al. [64] research focused on investigating manufacturing data collected from a manufacturing system during various operations conducted in an engineer-to-order enterprise, and developed tools for scheduling of operations.

#### **4. Discussion and Future Research Agenda**

This paper presents the SLR using different articles to discuss degradation and upgradation models for flexible unit systems life. Some significant issues from the review are talked about in this section. Moreover, there is an opportunity to identify the number of research gaps, with suggestions for future work. The discussion follows the conceptualization that appeared in Figure 1. First, the 5 keywords that have been taken into consideration are (1) Degradation, (2) Residual life distribution, (3) Workload adjustment, (4) Upgradation, and (5) Predictive Maintenance. The keywords have helped us to find related journal articles by searching in the three databases in the selected research area. Authors such as [43,65,66] discussed different analytic techniques, for example, descriptive, predictive, and prescriptive, to analyze manufacturing data for achieving competitive benefits for the manufacturing industries.

Authors Hao et al. [12], Ben-Salem et al. [24], Peng et al. [67], Bian et al. [68], and Hajej et al. [26] worked on the degradation of different configurations, for example, series and parallel configuration manufacturing systems. Zhenggeng et al. [21] worked on degradation models and various stochastic processes like gamma process and Markov renewal process to find the degradation rate of manufacturing equipment. Zhang et al. [29] proposed conventional Wiener process-based degradation as one of the most important degradation model techniques among different degradation techniques. Naipeng et al. [30], Das et al [33], Si et al. [34], Zhang et al. [29], and Bian et al. [32] worked on finding the relationship between degradation rate and the residual life of a machine. The prediction of the manufacturing unit's residual life will be helpful to reduce the degradation rate by adjusting the workload to maintain the maximum production rate.

Adam Robinson [48], Pavlov et al. [47], Garcia-Garza et al. [44], Grohn et al. [46], Du et al. [45], Menezes et al. [55], and Dong et al. [19] investigated upgradation of a manufacturing system, which will help to enhance the performance and reliability of manufacturing equipment. Spendla et al. [52], Dong et al. [19], Fang et al. [20], and Kaiser et al. [69] present the predictive maintenance of machines using sensors degradation data for calculating the time to failure of various machines. Traini et al. [49], Zhang et al. [50], and He et al. [53] worked on predictive maintenance analytics by considering recent past data to eliminate prospective failures and also to improve the mission dependability of production systems.

#### *4.1. Research Opportunity 1: How Can Residual Life Be Predicted in FUS to Improve Systems Efficiency*

Degradation is an unavoidable characteristic, which it requires the utmost attention to pursue. However, a lot of literature is already available to handle the degradation rate at the component level. A limited number of papers (Hao et al. [12]; Manupati et al.) [38] have considered system level degradation, especially in the manufacturing systems context. A recent paradigm shift has forced the use of the Internet of Things (IoT) in almost every stage of the product life cycle. In addition, process industries have highly benefitted from the key technologies that emerged from this shift (Varela et al., [70] Varela and Ribeiro,) [71]. To make these processes effective and efficient, system-level health monitoring is a new thinking among researchers paying attention to these issues. To improve the health status of the system, an individual system's degradation rate needs to be decreased, which in turn improves the residual life of the machine. Here, the residual life of a machine was characterized as remaining useful time till its level of degradation arrives at a predefined failure threshold. The degradation and residual life follow different distributions depending on the order requirement and system status. Hence, this is a challenging work one can take into consideration to explore further.

#### *4.2. Research Opportunity 2: How to Deal with Heterogeneous Data Obtained from Various Sensory Sources for Predicting the Degradation Rate of FUS?*

Heterogeneous data includes multiple internal and external databases generated from different sources obtained in various dimensions (Varela and Silva, 2008 [72], Zhang and Gregorie, 2016) [73]. Real-time production data from complex systems produce a huge variety and volume of data. Handling these kinds of data-intensive systems with conventional statistical tools may be insufficient when firms seek to strategically conceal the data [13,74–76]; Hence, to handle the heterogeneous data in FUS and predict the degradation rate, improving the residual life advanced analytics is essential. This area opens wider challenges for the researchers to explore.

#### *4.3. Research Opportunity 3: How to Develop FUS for Real-Life Problems?*

In this section, we propose four different configurations derived from the real-life examples: i.e., one degree, two degree, semi-flexible, and fully flexible, shown in Figure 2a–d. Where one degree configuration is represented, it handles the requirements to process it in sequential order. The open braces (1, 1) represent the position and stage of the machine, e.g., (1, 4) in Figure 2a. Consequently, for two degrees of flexibility, the configuration is shown in Figure 2b, through which, after the jobs arrived and processed in the first machine are chosen for the next operation to process on the second machine, it has a flexibility of alternative machines available in the second position at the second stage. Hence, it has position flexibility, routing flexibility, and machine flexibility to execute the operations. Figure 2c,d represents the semi-flexible and fully flexible unit system, wherein in the semiflexible configurations, the second operation can be processed on more than 2 machines unlike the restrictions presented in the previous systems. In the fully-flexible systems, the machines have the flexibility to process any operation at a time.

**Figure 2.** Flexible unit system with different configurations.

#### **5. Conclusions**

A significant amount of literature related to manufacturing systems has been made available during the last decade to conduct various investigations. However, regardless of growing interest in these investigations, the existing literature does not bring clarity on the degradation and upgradation strategies, and models on recently emerging FUS. Despite the availability of many manufacturing systems, the arrangement of machines according to demand is of crucial importance, along with the capability of simultaneously adjusting the machines with different flexibilities to compensate the workload, and, in turn, for reducing the degradation of the system. Moreover, an integrated approach using predictive, prescriptive, and descriptive analytics and the parameters required to understand the performance of the system in line with the mentioned advanced analytics are also not much explored. To overcome this gap, this paper presented a systematic literature survey on the proposed FUS to identify the key factors that greatly affect system performance.

The review of this study was conducted based on SLR, and 59 articles were deeply analyzed after removing the duplicates. In this paper, from the observations, five key parameters, i.e., degradation, residual life distribution, workload strategy, upgradation, and predictive maintenance, were identified and their individual contributions were analyzed in the context of FUS. From this study, it is understood that the degradation rate will affect the life and production rate of different configurations of FUS. Moreover, the upgradation model and predictive maintenance, along with advanced analytics procedures of the manufacturing systems, are valuable and enable the systems to run with higher production rate, while increasing the life of a system. Furthermore, this study analyzed different existing research and established three research objectives to explore and improve the proposed FUS. The authors hope that this research can serve as a guideline for more research and discussion of FUS towards degradation and upgradation models.

**Author Contributions:** Conceptualization, V.K.M. and M.L.R.V.; methodology, V.K.M. and M.L.R.V.; investigation, T.S.; writing—original draft preparation, T.S.; writing—review and editing, T.S., V.K.M., and M.L.R.V.; visualization, T.S., V.K.M., and M.L.R.V.; supervision, G.P.; project administration, V.K.M. and G.P. and M.L.R.V.; funding acquisition, V.K.M. and G.P., and M.L.R.V. All authors have read and agreed to the published version of the manuscript.

**Funding:** The project is funded by the Department of Science and Technology, Science & Engineering Research Board (DST-SERB), Statutory Body Established through an Act of Parliament: SERB Act 2008, Government of India with Sanction Order No ECR/2016/001808, and also by FCT—Fundação para a Ciência e Tecnologia through the R&D Units Project Scope: UIDB/00319/2020.

**Acknowledgments:** We thank the Associate Editor and three anonymous referees for their thoughtful comments, which have greatly aided in the improvement of the manuscript. We also are grateful for the financial support of the Department of Science and Technology, Science & Engineering Research Board (DST-SERB), Statutory Body Established through an Act of Parliament: SERB Act 2008, Government of India with Sanction Order No ECR/2016/001808, and of the FCT—Fundação para a Ciência e Tecnologia through the R&D Units Project Scope: UIDB/00319/2020.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**

## *Article* **Design Technology and AI-Based Decision Making Model for Digital Twin Engineering**

**Ekaterina V. Orlova**

Department of Economics and Management, Ufa State Aviation Technical University, Ufa 450000, Russia<EMAIL_ADDRESS>
**Abstract:** This research considers the problem of digital twin engineering in organizational and technical systems. The theoretical and methodological basis is a fundamental scientific work in the field of digital twins engineering and applied models. We use methods of a system approach, statistical analysis, operational research and artificial intelligence. The study proposes a comprehensive technology (methodological approach) for digital twin design in order to accelerate its engineering. This technology consists of design steps, methods and models, and provides systems synthesis of digital twins for a complex system (object or process) operating under uncertainty and that is able to reconfigure in response to internal faults or environment changes and perform preventive maintenance. In the technology structure, we develop a simulation model using situational "what-if" analysis and based on fuzzy logic methods. We apply this technology to develop the digital twin prototype for a device at the creation life cycle stage in order to reduce the consequences of unpredicted and undesirable states. We study possible unforeseen problems and device faults during its further operation. The model identifies a situation as a combination of failure factors of the internal and external environment and provides an appropriate decision about actions with the device. The practical significance of the research is the developed decision support model, which is the basis for control systems to solve problems related to monitoring the current state of technical devices (instruments, equipment) and to support adequate decisions to eliminate their dysfunctions.

**Keywords:** digital twin; methods for modeling digital twins; system design of digital twins; artificial intelligence methods

#### **1. Introduction**

Under the fourth industrial revolution, the driver of innovative development in high-tech enterprises is the technology of a "digital twin" (DT), or a virtual replica of a cyber-physical system, a virtual prototype of real processes or products. DTs, and more generally, computer-aided design and simulation technologies, are intended to meet the great challenge of creating complex engineering structures and technical systems that are optimal by the setting of conflicting performance criteria. The potential benefits of DT include the ability to perform what-if analyses and provide decision support by fastverifying designs and digital tests used in changing the product, process or its components. DT applications contribute to the growth in the competitiveness of manufactured products by enhancing the market processes by reducing the time for product development, testing and implementation.

However, the organizational and methodological maintenance for DT development is still not fully developed from the point of the monitoring and coordination of physical objects. Such a methodology requires a systematic approach for an object's design taking into account various aspects—functions performed, including the identification and solution of operational problems, complexity, purpose, life cycle stages and other object features.

The aim of this paper is to develop organizational and methodological maintenance for organizational and technical systems with a DT design, which provides a systematic

**Citation:** Orlova, E.V. Design Technology and AI-Based Decision Making Model for Digital Twin Engineering. *Future Internet* **2022**, *14*, 248. https://doi.org/10.3390/ fi14090248

Academic Editor: Leonilde Varela

Received: 25 July 2022 Accepted: 22 August 2022 Published: 24 August 2022

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2022 by the author. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

synthesis of design stages, tools (methods and models) and results aimed at accelerated DT engineering. To achieve the goal, the following problems are solved:

This article is organized as follows. The second chapter examines and critically summarizes methods for modeling digital twins. The advantages and disadvantages of the methods are described, and the scope of their applicability is determined. The third chapter presents the developed methodology for systems engineering and technology for the designing of organizational and technical systems' DTs. The fourth chapter reflects a numerical example of the implementation of the developed technology and considers the digital twin of a medical device. The fifth chapter is devoted to the decision making model based on the fuzzy logic method, which is part of the digital twin. The results obtained by the model are discussed. The conclusions give the main results of the study and outline their theoretical and practical significance.

#### **2. Literature Review**

There are three groups of approaches and methods used for DT designs:

Features of the above methods are given in Table 1, which summarizes modeling approaches by certain characteristics.

**Table 1.** Comparative analysis of the models for DTs of organizational and technical system designs.

#### **Table 1.** *Cont.*

A generalized modeling method is the mathematical modeling of the physics (functioning principles) of an object/process. The physical model provides for the computer modeling of physical processes as well as a description of their functioning and relationships with the external environment. The construction of such models in practice is associated with the mathematical programming methods (operations research) [12], and simulation modeling based on its various paradigms and approaches—system-dynamic, discrete-event or agent-based modeling [13].

Mathematical modeling is a key component of the digital transformation. To create models that are used to create system models, the DT can use the results of detailed three-dimensional numerical calculations performed using interdisciplinary CAE solvers. Depending on the purpose, mathematical models can be descriptive or optimizing. The purpose of descriptive models is to establish the laws of change in model parameters. The optimization model provides a search for the function's extreme value under restrictions using numerical methods. Depending on the certainty of the initial information degree and taking into account the time factor, linear, non-linear, stochastic programming, gametheoretic and fuzzy logic methods can be used.

Simulation models as a subclass of mathematical models are divided into static and dynamic; deterministic and stochastic; discrete and continuous. In continuous simulation models the variables change continuously, the state of the simulated system changes as a continuous function of time, and as a rule, this change is described by systems of differential equations. In discrete simulation models, variables change discretely at certain moments in simulation time. The dynamics of discrete models are a process of transitioning from the moment of the next event to the moment of the next event.

Data-driven modeling includes data mining, artificial intelligence, big data and advanced analytic methods. Each of these methods imposes special requirements on the necessary computing resources. For example, data mining methods require large-scale storage with high bandwidth for collecting and accessing analytical data, as well as a high scalability for the computing system for processing them; machine learning methods require nodes with installed graphics accelerators.

Models based on data mining are used to discover knowledge in the data previously unknown, non-trivial, or practically useful and open to interpretation, necessary for making strategically important decisions. Artificial intelligence and machine learning are effectively used in digital warehouse forecasting. The use of these methods makes it possible to achieve a level of predictive accuracy higher than that based on traditional simulation methods [13].

The use of "big data" has its limitations, associated with incomplete or noisy data, and difficulties in predicting rare events. Extrapolation methods do not allow for such predictions. For some products, sensors are expensive to install and maintain, sensors are prone to errors, failures can give incorrect readings, and the results can overwhelm users with redundant information.

Without a structural (mathematical, physical) model, it is difficult to determine the areas of technical devices where it is advisable to locate sensors. Collecting raw data from sensors is only part of the modeling process. At the stage when the inverse problem appears, that is, when it is necessary to restore the picture of what is happening on the basis of data received from sensors without a mathematical model, this problem turns out to be intractable, since most of the collected data are unusable "garbage", of which it is very difficult to select a meaningful part that adequately describes the object (process).

At the same time, the mathematical modeling of objects (processes) in combination with data-based models provides more opportunities for forecasting than models based only on machine learning technologies. Data-driven modeling can be applied at the operational stage of an object's (product) life cycle, when it is possible to obtain feedback from it. Mathematical models based on physical processes are more promising for problems with a situational analysis and for decision making under "what-if?" condition analysis. In addition, hybrid models can be used in non-recurring situations where there are not enough data to apply statistical methods.

On the basis of the additional information obtained during the operation stage, the level of adequacy of the hybrid model increases, that is, the DT is trained and makes it possible to further predict the level of possible deviations from normal modes and damage to equipment, or evaluate its residual life [10].

It should be noted that at different stages of DT design, there is a different amount of data about object. At the development stage, there are no data from a real object, since there is no physical product (product) itself, and data about an object can only be obtained on the basis of modeling physical processes that determine the creation and functioning of a future product. As product data accumulate, the latter can increasingly be used to build analytical models. A mathematical model based on physical processes can be created before the stage of creating a real object, and can predict its behavior over a wide range when the boundary conditions of the numerical simulation problem change.

A DT with a high level of adequacy should combine both physical process models and data-driven models. Smart digital twins with intelligent controls should combine both of these approaches, enhancing the benefits of each of them.

The use and scope of digital twins is very wide. They are used not only in heavy engineering [14–16], in the automotive industry [17] and building [18], in the field of nuclear energy [19], in the aerospace [20,21] and oil and gas industries [22], in architectural design and creating smart cities [23], and agriculture, but they are also used to improve operational efficiency in the production of consumer goods [5], the accuracy of diagnostics and decision making in healthcare [24], to attract customers and customize services in the financial sector [25] and in retail [26], for organizing logistics processes and supply chains [27], and in regional and municipal management [28,29].

Demand for and the range of application of DTs is expanding. Since their development and implementation are based on a number of rapidly developing technologies, the development of digital storage directly depends on the growth of the capabilities of these technologies. This is due to:

• The development of quantum technologies and the increase in the speed of computing systems [30,31]. If general-purpose quantum computing is ever realized, there would be a qualitative leap in the speed of hardware systems. This will make it possible to perform numerical analyses based on already existing (and more complex) models in a time acceptable for the operational interaction of a physical object and its digital copy. Today, companies are working to develop and use quantum algorithms to model complex physical processes. The transition to such technologies will speed up the solution of problems based on numerical modeling, providing for the required accuracy of algorithms under the conditions of the available computing resources (problems of multi-parameter optimization, etc.);

#### **3. Methodology for Systems Engineering and Technology for Digital Twin Design**

Following [37], and dividing the behavior types of a real system into Predicted Desirable (PD), Predicted Undesirable (PU), Unpredicted Desirable (UD), and Unpredicted Undesirable (UU), in this work, we build a Digital Twin Prototype that describes the prototypical physical artifact. It contains the necessary components to describe and produce a physical version that twins the virtual version. We consider the "create" life cycle stage of the system (physical object). At the create life cycle stage of the system, the problem is to foresee its possible states and develop a decision support system to neutralize the consequences of unforeseen events.

While the traditional approaches have been to verify and validate the requirements, or the predicted desirable (PD), and to eliminate the problems and failures, or the predicted undesirable (PU), the Digital Twin prototype can help to identify and eliminate the unpredicted undesirable (UU) states. This problem is solved on the basis of changing the simulation parameters within the possible range, and investigates a variety of different situations; it is possible to explore the variety of behavioral patterns of the system that can lead to serious catastrophic problems. Such modeling will allow for designing the physical object in a virtual space with a number of possibilities, and will significantly reduce the consequences of UUs.

The purpose is to develop an approach for digital twin engineering of a physical object (device) that will minimize undesirable unpredictable behavior. This will mitigate or eliminate the negative consequences of such risks. To do this, we propose the following methodology.

The process of DT construction is a multi-stage process and consists of the design and engineering stage, digital modeling and technological testing (Figure 1). In this paper, we consider only the first stage of design and engineering.

**Figure 1.** Digital twin and stages for its development.

DT is defined as a system consisting of a physical object digital model and twoway information links with the physical object or its components. DT is based on a digital model in the form of mathematical and computer models, as well as documents that describe the structure, functionality and behavior of a newly developed or operated product (object) at various stages of its life cycle, for which, based on the results of digital or other tests, an assessment of compliance with the requirements for the product was carried out. In this case, a digital model is created using computer simulation software and describes the structure, functions and behavior of the product being developed. The content and functionality of the digital model depends on the stage of the product's life cycle. The conformity assessment of a digital product model generally includes verification and validation procedures for mathematical and computer models. A computer model is implemented in a computing environment and is a collection of data and program code required to work with data. The computer model is based on a mathematical model, that is, a model in which information about the modeling object is presented in a formalized form.

The organizational requirements to create a digital twin include the use of a software and technological platform for digital twins, which should include: (a) computer modeling software controls; (b) project management tools: (c) tools for collecting, processing, analyzing, visualizing, cataloging, storing, transferring computer models and computer simulation results; (d) means of tracking all changes in design, technological solutions and modifications of computer models, and options for engineering calculations; (e) means of reporting results; (f) means of data protection and organization of joint work of project participants in accordance with access rights; (g) computer simulation tools for planning the usage of an object (product) for its intended purpose; (h) maintenance and repair support.

The organizational and methodological support of DT is not fully developed in terms of the coordination of modeling and management processes including structural, functional and informational modeling. To fill this gap, we propose to form a work plan by its stages like triads: problem–content–results. These require a systems approach and design for all life cycle stages of a physical object, including the identification and solution of emerging problems in the process of its operation.

We develop the following technology, which provides organizational and methodological support for the development and operation of DT for the organizational and technical system and is presented in Figure 2. The proposed technology combines the stages of its design, methods and models, and provides for the accelerated engineering of DT.

**Figure 2.** Technology for digital twin development (stage "Design and Engineering").

The proposed technology allows, firstly, to carry out a system analysis of a physical object, taking into account the uncertainty of the external environment on the basis of heterogeneous tools for qualitative and quantitative analysis. Secondly, it forms an adequate mathematical model of the physical object, taking into account the results of the conceptualization stage, and develops a computer model and implements a test of it. Thirdly, it can be the basis for DT engineering and forming a decision support system.

The operational scheme of the proposed technology consists of five steps. First, it is necessary to identify the problems and describe the contradictions that arise in the development and implementation of digital twins in the industry. Next, we should determine the goals of the DT implementation, set problems based on the goal and describe the project. At the second step, the decomposition (scanning) of physical object takes place. The functions and properties as well as the technical parameters of the considered system (equipment, device) are described. Further, its structural and functional model is built. The third step is devoted to the analysis of the external environment of the functioning of the technical system. Using STEP (Social–Technological–Economic–Political) and SWOT (Strengths–Weaknesses–Opportunities–Threats) analysis, we determine important internal and external factors and expertly evaluate their impact on the effectiveness of DT. At the fourth step, mathematical and computer modeling tools and methods for DT designing are chosen. In addition, a decision support system is being built based on the selected mathematical model, and simulation experiments are being carried out.

#### **4. Empirical Results**

As an example of the implementation of the proposed technical device at the creation life cycle stage—a neonatal intensive care incubator with microprocessor controls for monitoring the parameters of temperature, oxygen concentration, air humidity, temperature and body weight is considered. Application of the technology is described by the steps defined in Figure 2.

Step 1. The incubator is designed for the nursing and intensive care of newborns, including premature babies with critically low weight (from 500 g). The incubator provides an adjustable heat supply, the required air humidity and oxygen concentration in the children's module, and body weight control.

Step 2. First, we form the structural and functional models of the device. The block diagram is shown in Figure 3 and consists of a sensor system, control system, temperature control system and oxygen supply. The observed parameters of the device are: (a) air temperature; (b) skin temperature; (c) relative air humidity; (d) oxygen concentration; (e) body weight.

The construction of a functional model allows for clearly fixing what processes are carried out—what information objects are used when performing functions of various levels of detail. The model shows the areas of responsibility of the process executors and the course of the process itself, the relationships between processes and the results. The functional model is the basis for identifying problems and weaknesses in the operation of the device.

The functional model of the incubator is formed on the basis of the notation of system modeling of business process IDEF0. Figure 4 shows the first level of decomposition and reflects the main functions.

**Figure 3.** Block diagram for the technical device.

**Figure 4.** Functional diagram for the device's operation (first decomposition level).

Step 3. At the next step, the diagram of cause-and-effect factor relationships is formed to identify possible causes of failures. Based on a qualitative analysis of similar devices of this class [38], it can be assumed that the following six factors are the main sources of incubator malfunction: (1) medical personnel; (2) technical staff; (3) external environment; (4) sensor system; (5) control system; (6) power system. The diagram shows the decomposition of these factors in the form of problems that, acting in isolation or together, can lead to incubator failure (Figure 5).

**Figure 5.** Diagram of cause-and-effect factor relationships for the appearance of possible malfunctions.

#### *Decision Support Model for Diagnosing the Technical Condition of Equipment Based on Fuzzy Logic Methods*

Steps 4, 5. Identified problems and possible causes of equipment failure show that a number of factors and causes are described not in quantitative, but in qualitative form. Therefore, a decision support system should use possibilities to use different measurement scales of the simulated device properties. Problems of this type can be solved using artificial intelligence methods and fuzzy logic models [39–41].

The category of technical condition depends on many factors, both qualitative and quantitative. The developed model considers fault factors that are related to the internal technical condition of the device itself, that is, faults associated with the baby module engine, power cable, oxygen connection system, valve impeller system, humidity sensor, temperature sensor of the main and additional, etc.

The implementation of the fuzzy modeling process is carried out by using the Fuzzy Logic Toolbox module of the MATLAB software tool. Fuzzy inference is implemented based on the Mamdani algorithm.

To diagnose the technical condition of a device, its qualitative description is made using linguistic expressions (logical rules). Twenty-five input variables are used, reflecting the state of the equipment subsystems, determining one of the four possible decisions on the action with the equipment (output variable)—to take out of service, to repair, to conduct additional preparation for operation, to keep in service (Table 2).

**Table 2.** Description of input and output variables.

**Table 2.** *Cont.*

Linguistic variables and the range of their possible values are described in Table 3.

The accumulation of the conclusion according to all the rules is carried out using the operation of max-disjunction. The center of gravity method is used for defuzzification. By implementing a fuzzy inference system at the defuzzification stage, we obtain a decision about operation modes under conditions of known input data.

The rule base for making decisions on the technical condition and action with the device in the event of a malfunction is determined using a set of production rules of the "If-Then" type (selectively):

#### **5. Discussion of Results**

Using the developed technology and model, we conduct the situational "what-if" analysis, and identify a device's technical conditions and its possible faults. The model delivers decisions in various situations, determined by a combination of input variables. We consider multivariate situations with different input factor combinations. The following types of situations are tested: (1) light network indicator is on, continuous signal sounds; (2) red alarm indicator flashes, signal "3" sounds; (3) baby module does not function. Results of the situational analysis are presented in Tables 4–6.

**Table 4.** Modeling results on Situation 1.

**Table 5.** Modeling results on Situation 2.

**Table 6.** Modeling results on Situation 3.

In the situation when the indicator is "network" and the sound signal is "continuous", a fusible link is "burnt out", and the appropriate decision made by model is "to repair". In the situation when the indicator is "flicker", the sound signal is "3" and the main skin temperature sensor is "connected", the secondary skin temperature sensor is "not connected", the decision is "to conduct additional preparation for operation". If the position height mechanism of the baby module is "not turn off", the baby unit switch mechanism is "fault", and the baby module engine is "fault", then the decision is "to take out of service".

The developed decision making model for eliminating device malfunctions as a part of its DT takes into account internal and external factors and allow for identifying the malfunction problem and suggesting the appropriate decision to provide a regular mode of device operation. In the framework of decision support systems of DT, the model provides a reduction in device downtime, reduced repair costs and improved operational efficiency.

#### **6. Conclusions**

The focus of this paper is the complex process of DT construction. In this study, we have given a comprehensive analysis of approaches and methods for organizational and technical systems' DT design. DT construction requires multi-stage technology, and consists of design and engineering stage, digital modeling and technological testing. In this paper, we consider the design and engineering stage.

The design and engineering stage is quite time consuming and includes steps to study object properties and its connections with the external environment, describe its structure and functioning, and identify possible problems in the process of functioning. In order to organize the work at this stage and describe the operation of the device, its structure and possible failures in the operation, this study proposes an approach to organizing the design process. As a numerical example, we use the device at the design stage of its life cycle; we study possible unforeseen problems during its further operation. Possible combinations of failure factors of the internal and external environment are modeled, and the model based on fuzzy rules is proposed for making management decisions in such situations.

It is shown that for complex organizational and technical systems functioning under uncertainty, there is no comprehensive and universal methodological approach for

organizing a DT design and its accelerated engineering. We consider the digital twin prototype for a device in the creation life cycle in order to reduce the number and consequences of unpredicted undesirable states. The new theoretical results have been obtained over investigation:

The practical importance of the developed technology and the model is that they are the foundation for decision support systems to observe the current state of technical devices (instruments, equipment) and to develop adequate decisions to eliminate its malfunctions.

**Funding:** This research received no external funding.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Not applicable.

**Conflicts of Interest:** The author declares no conflict of interest.

#### **References**

*Article*

## **Expectations and limitations of Cyber-Physical Systems (CPS) for Advanced Manufacturing: A View from the Grinding Industry**

#### **Iñigo Pombo 1, Leire Godino 1, Jose Antonio Sánchez 1,\* and Rafael Lizarralde <sup>2</sup>**

Received: 14 July 2020; Accepted: 19 September 2020; Published: 22 September 2020

**Abstract:** Grinding is a critical technology in the manufacturing of high added-value precision parts, accounting for approximately 20–25% of all machining costs in the industrialized world. It is a commonly used process in the finishing of parts in numerous key industrial sectors such as transport (including the aeronautical, automotive and railway industries), and energy or biomedical industries. As in the case of many other manufacturing technologies, grinding relies heavily on the experience and knowledge of the operatives. For this reason, considerable efforts have been devoted to generating a systematic and sustainable approach that reduces and eventually eliminates costly trial-and-error strategies. The main contribution of this work is that, for the first time, a complete digital twin (DT) for the grinding industry is presented. The required flow of information between numerical simulations, advanced mechanical testing and industrial practice has been defined, thus producing a virtual mirror of the real process. The structure of the DT comprises four layers, which integrate: (1) scientific knowledge of the process (advanced process modeling and numerical simulation); (2) characterization of materials through specialized mechanical testing; (3) advanced sensing techniques, to provide feedback for process models; and (4) knowledge integration in a configurable open-source industrial tool. To this end, intensive collaboration between all the involved agents (from university to industry) is essential. One of the most remarkable results is the development of new and more realistic models for predicting wheel wear, which currently can only be known in industry through costly trial-and-error strategies. Also, current work is focused on the development of an intelligent grinding wheel, which will provide on-line information about process variables such as temperature and forces. This is a critical issue in the advance towards a zero-defect grinding process.

**Keywords:** cyber-physical systems; digital twin; advanced manufacturing; grinding process; grinding wheel

#### **1. Introduction and Literature Review**

Digital solutions for improving the performance of high added-value manufacturing industries are paving the way towards the digitalization of industrial companies. Machine-tool manufacturers have taken the lead in the development of tools such as Celos or Savvy [1], which provide excellent platforms for digitalization. Digitalization of the manufacturing sector is running parallel to current societal and economic developments. In a survey conducted in 2020, Eurostat states that the manufacturing sector employed more than 28.5 million people in the EU in 2017 [2], with this sector occupying third place in the employment rankings [3]. CECIMO (The European Association of the Machine Tool Industries), in its annual survey on machine-tool production, states that since January 2017, industrial production

has grown by 3.0% in the EU [4], emphasizing the importance of the manufacturing sector in current European society.

With regard to the development of manufacturing technologies in the EU, in a recent survey carried out in 2019 [5], it is stated that "the production processes will become increasingly digital and less mechanical", whilst also pointing out that "Digitisation is expanding possibilities to: design and test products or processes virtually (simulation); repair industrial apparatus remotely; and automate the constant fine-tuning of processes." A recent paper [6], studied in detail the main aspects and technologies that will enable this revolution, along with the main application domains.

The grinding process is key in the manufacturing of high added value parts in many industrial sectors such as aerospace, the automotive industry, and energy production [7]. Its characteristics make it the best choice when smooth tolerances and good surface quality must be achieved in difficult-to-machine materials. This fact is of critical importance in the previously mentioned industrial sectors in which new materials are being developed every day, and the precision requirements continue to become more demanding [8]. Thus, together with high-quality ground parts, the grinding process is required to be highly productive whilst also meeting sustainability demands [9].

Grinding will also play a leading role in facing some of the challenges related to the development of e-mobility (according to Bloomberg NEF, in 2040 there will be more electric vehicles than combustion vehicles), the need to expand aircraft fleets (which, according to the Aerospace and Defense Industries Association of Europe, is a sector that is expected to see considerable growth up until 2032) and the new challenges to be met in terms of ensuring clean and sustainable energy systems [10]. Grinding is adapting successfully to the current situation and, according to a recent report [11], the grinding wheel market is predicted to show an annual growth rate of around 2.83% until 2025, particularly in the niche market sectors such as those mentioned previously. This fact reflects the industrial importance of the process and highlights the necessity for industrial companies to optimize their grinding processes in order to be competitive in the 21st century global society. In spite of these encouraging data, it is important to note that the global situation of the industry is continuously changing [12] and that grinding technology must be prepared to be competitive within this framework.

Cyber-physical systems (CPS) will be critical for facing these new challenges. The digital twin (DT) concept is now key for optimizing capabilities of manufacturing processes. In [12], this concept is widely analyzed and applied to the manufacturing processes and it has been defined as "a mirror of the real world that provides a means of simulating, predicting and optimizing physical manufacturing systems and processes". Usually, the DT is thought to be the same as a model or a theoretical simulation of a given process, but it is, in fact, much more: A digital twin is a high-fidelity representation of the operational dynamics of its physical counterpart, enabled by almost real-time synchronization between the cyberspace and physical space [13]. In a recent work [14], a digital twin-based design platform was validated with a case study of the hollow glass smart manufacturing system. The application to lean production is addressed in [15], comparing the performance of three pull control strategies by simulation. In both cases results prove the efficiency of the approach. However, no references have been found to put the focus on the process itself, which is particularly critical in the case of the grinding industry.

The selection of the grinding process is based on the fact that it is one of the most complex machining processes, that involves a large number of variables that, to some extent, cannot be directly controlled. For instance, tool wear in milling or turning is a well-known fact, with good predictive models being currently available. It is also possible to know tool wear in turning or milling by using a pre-setting tool station. However, the mechanisms of wheel wear in grinding are extremely complex because of the composite nature of the grinding wheel. In fact, wheel wear is only indirectly known in grinding, because of its effect on part damage. Also, problems such as grinding burn (because of excessive contact temperatures) do not affect turning or milling. Many other aspects such as part finishing, spark-off operations, etc. can be also cited. In summary, grinding is more heavily based on

experience than other machining processes, for which models are available; therefore, it is an optimal technology to test the impact of digital twins in the machining industry.

According to this, a number of elements must be included within a DT such as process modelling, the characterization of grinding wheel mechanical behavior, and process monitoring.

#### *1.1. Process Modelling*

Process modelling is a classic research topic. One of the most popular modelling areas concerns the thermal aspects of the grinding process due to their importance for impacting process performance. In the 1970s Malkin and Anderson [16] developed the first systematic analysis of the thermal elements of the grinding process, including simple analytical models based on classic heat conduction mathematical solutions. Several analytical developments were made by various authors such as Snoeys et al. [17], Lavine et al. [18,19], Ueda et al. [20], Rowe et al. [21,22], or even in the subsequent works of Malkin et al., summarized in [23]. The most recent research investigations into modelling the grinding process have usually been based on numerical methods because these have a greater capacity to represent reality in comparison with analytical approaches. In this case, these methods are not only focused on thermal issues [23,24], but also on the characterization of material removal mechanisms [25,26], dressing process performance [27] or methods to advance wheel surface modelling [28]. Doman et al. [29] conducted a complete review of the most recent advances in grinding modelling. Although not a very recent study, it perfectly summarizes the work that has been done, along with the work that needs to be carried out in the future. The development of sound models for the grinding process is of critical importance, because they allow a deeper understanding of the physical phenomena and the interactions among complex process variables. However, it is a fact that process models commonly fail when they are transferred to industrial workshops. In many cases, the causes for this situation is the complexity of the models (that limits their practical application) and the lack of actual real-time information of the machine and the process. Because of this, integration of such models in industrial practice requires a new and complete approach.

#### *1.2. Wheel Characterization*

When developing a theoretical model of a real phenomenon, one of the key issues is the characterization of the behavior of the modelled object. Classic models focus on the ground part and traditional metal characterization methods are employed to characterize both thermal and mechanical properties. Whilst this characterization is very useful for analyzing the influence of the process parameters on the ground part, on several occasions this information is shown to be insufficient, such as, for example, when wheel wear needs to be modelled or the behavior of a single abrasive grain must be reproduced. In spite of the importance of characterizing the grinding wheel mechanical behavior (e.g., for a wheel wear model) relatively few studies have addressed this issue. In regard to including material characteristics in the models, Young's modulus is used to characterize the elastic behavior of the wheel. This classic approach involves associating the hardness of the wheel with its Young's modulus [30]. However, classic studies suggest that this hypothesis is not completely correct [31] and using the same grade for different wheels can lead to variations in Young's modulus values. There is a lack of knowledge about this important issue in wheel modelling. Additionally, relatively few studies can be found regarding the fracture behavior of the grinding wheel. Given the nature of the grinding wheel structure, it should be regarded as a quasi-brittle material. In recent years, various research groups have focused their efforts on developing discrete element method (DEM) solutions to characterize the fracture behavior of these types of materials [32–34] Furthermore, in [35,36] the authors characterize the fracture behavior of the grinding wheel using a DEM model, they propose an experimental procedure for quantifying mechanical parameters (Brazilian test applied to grinding wheels) [35], and they describe tests for characterizing the grinding wheel material [36]. Unfortunately, this approach has not been validated for the classical problem of wheel wear. It is

therefore not possible to know if the models developed for the bond material correctly represent the wear behavior of the wheel. Extensive work must still be done in this direction.

#### *1.3. Process Monitoring*

In the case of the grinding process, this is a complex issue due to the characteristics of the process, including accounting for the small contact area between the tool (grinding wheel) and the ground part, high temperature gradients in the contact region (103–104 K/s) [37], poor accessibility to the contact zone, and the large quantity of grinding fluid over the working zone [23]. The easiest parameter to be acquired is the power consumption of the grinding wheel spindle during the process. Although this is a very useful parameter, it only reveals a relatively limited amount of information about the process; thus additional sensors must be used in order to extract useful data from the process. In this regard, a number of attempts have been made to measure grinding temperatures, in both classic and new research works. In particular, the first attempts to measure temperatures with thermocouples were carried out in the 1950s [38], but until the 1990s no reliable data were extracted [39]. Later, several developments were reported in various studies [40,41]—every thermocouple solution presents an unsolvable problem, that is, the difficulty in following the high temperature gradients in the grinding zone. In [42] a state-of-the-art classification of the thermal measuring devices used and methods for material removal processes are presented. Considering these issues, a recent study by Urgoiti et al. [37] developed a new two-color pyrometer-based optical fiber system for measuring the temperature of the ground part. In [43] a new possibility was proposed for the in-process measurement of workpiece temperature in cylindrical grinding. This last work, together with [44], in which the authors propose wireless data transmission, could form the basis of future temperature acquisition devices. Whilst the reviewed studies indicate that considerable efforts have been made to accurately measure the grinding temperatures, there is no suitable technology for implementing this process in an industrial setting. The importance of this issue is shown in [45], where a complete review was carried out on the detection of grinding burn. Moreover, in their work, Teixeira et al. [46] propose an additional method for detecting thermal damage in ground parts, which reinforces the current need for thermal monitoring within the industry.

Another interesting parameter to be measured is wheel wear—traditionally defined by the G-ratio—which represents the volumetric wear of the grinding wheel compared with the amount of ground material. Although the information provided by this parameter is quite useful, again, additional information about the evolution of the grinding wheel surface characteristics is required for a full evaluation of the process performance. There have been considerable developments in recent years in optical devices and software analysis [47–50] for evaluating the evolution of wear flat, and usually the percentage of apparent wear flat area has been used as the wear parameter [23]. In [50] an example of an image analysis application for detecting abrasive grain wear flat was presented. Several attempts have been made to analyze the wear of new types of abrasives [51], which is a key issue in the profile of modern grinding processes [52]. According to the works analyzed, in-machine wheel wear measuring devices will be a fundamental aspect to be studied in future research works, particularly for heavy-duty profile ground parts such as those to be analyzed in this project proposal.

It is also important to analyze the dynamic control of the grinding process. The influence of the dynamic behavior on the quality of the ground part is quite important, since this is the main factor responsible for the appearance of leads and micro leads in the ground surface, or the presence of long wavelength surface marks. When analyzing dynamic behavior, the main problem to arise concerns the difficulty to position the accelerometers near to the grinding zone, which limits the possibility of achieving a highly accurate analysis, particularly when there is a need to analyze low amplitude vibratory phenomena. Several works can be found in the literature, particularly in relation to the avoidance of vibratory effects such as chatter [53–56] and the appearance of surface marks [56,57]. Accelerometers are still usually positioned relatively far away from the contact point between wheel and

workpiece. Research work and technology development must be done to try to analyze displacement, velocity and acceleration as close as possible to the contact zone.

Finally, in the field of process monitoring, acoustic emission analysis is worth noting; it represents a powerful tool for extracting information about the performance of the process. In [58] a review was conducted of studies regarding acoustic emission during monitoring of the grinding process. Although this review is quite old, recently published works show that the use of acoustic emission in grinding is a very useful tool to monitor the state of wheel wear [58], dressing tool behavior [59] or the avoidance of thermal damage [60].

Analysis of the state-of-the-art and of the needs of the current grinding industry showed that DT technology could be a very powerful tool to analyze, design and optimize industrial grinding processes [12]. In [61], the authors made the first attempt to use a DT applied to the grinding process, which focused on minimizing the environmental impact of the process by analyzing the wheel dressing cycles. However, in this work there is not concern for critical issues such as wheel wear, occurrence of part damage because of grinding burns, etc. It is therefore, a very limited approach to the complex technology of grinding.

The main contribution of this work is that, for the first time, a complete digital twin (DT) for the grinding industry is presented. The required flow of information between numerical simulations, advanced mechanical testing and industrial practice has been defined, thus producing a virtual mirror of the real process. The underlying hypothesis is that process knowledge is distributed between very different agents (wheel manufacturer, machine-tool builders, end-users of the process, and fundamental research groups), and it is necessary to effectively share this knowledge. The aim is to produce a virtual mirror of the real process, integrating innovative monitoring systems (intelligent grinding wheel) and original theoretical and experimental approaches for the grinding processes. First, the local ecosystem in which the DT is being developed is presented. Section 3 presents the global structure of the DT. This structure integrates four different high-tech layers. Each layer is described in detail, and interactions between the layers are discussed. In this paper, an open platform is proposed that is capable of integrating the knowledge generated in the other layers, thus becoming the actual user interface of the DT.

#### **2. A Local Ecosystem for a Viable DT for the Grinding Industry**

The development of a transferable DT for industry can only be accomplished with the participation of all agents involved in the research, development and industrialization of the technology. This section describes the umbrella under which the concept of a DT for the grinding industry will be developed. The DT is being developed in the Basque Country under the initiative of the Basque Digital Innovation Hub (BDIH). The BDIH is a connected network of advanced manufacturing assets and services infrastructure available to companies for training, research, testing and validation. This initiative offers technological solutions, primarily for SMEs (Small and medium-sized enterprises), in order to meet the challenges of Industry 4.0. To this end, a digitally linked network is created, which consists of a public–private collaboration involving R&D infrastructures, pilot plants and specialized know-how in different areas of advanced manufacturing. The aims of the network are to develop R&D projects, the scaling of industrial projects, and the exhibition of cutting-edge technologies, whilst also serving as a resource for training and acceleration of start-ups.

The BDIH is divided into six work areas that are classified according to knowledge and technology, among which the Smart and Connected Machines—Digital Grinding Node is particularly noteworthy. Grinding is a critical manufacturing process which combines the technological complexity of the process with the high demand for quality and fine precision of the process. Moreover, grinding is usually the last process in the manufacturing chain, which ensures the final quality of the manufactured parts. In the Basque Country there are numerous companies that use the grinding process, along with many grinding machine manufacturers. Therefore, in research centers and universities, various research groups are focused on grinding, including the Grinding Process Research Group (UPV/EHU) and the

IDEKO technological center. In this sense, the mission of the Digital Grinding Node is to collect and coordinate the knowledge and assets of the main agents that develop their activity in the areas of knowledge associated with grinding. Thereby, the Digital Grinding Node forms a multidisciplinary distributed space in which Basque companies—particularly SMEs—can find solutions to their concerns and needs for knowledge; the companies can take advantage of numerous developments within the field of grinding that cover its multiple variants, including cylindrical, surface, centerless, vertical, and horizontal grinding, and different types of workpiece materials, grinding wheels, and coolants.

#### **3. A Proposed Transferable DT for the Grinding Industry**

Due to its heavy dependence on non-systematic knowledge, which means that the process relies on extensive trial-and-error strategies, the grinding sector needs to advance towards a complete, transferable and ready-to-use digital twin (DT) through the integration of advanced models of the grinding process, original simulative mechanical tests, and state-of-the-art process sensors.

Since at present grinding technology is linked to high-added value products and markets (e.g., the aerospace, automotive, and railway sectors), by achieving this goal grinding companies (machine-tool builders, wheel manufacturers and end-users of the process) will directly benefit from zero-defect production strategies and reduced setup times in a process that is highly dependent on trial-and-error experimental approaches.

The present work proposes a down-top approach composed of different layers of the DT. The underlying hypothesis is based on the fact that process knowledge is distributed between very different agents (wheel manufacturer, machine-tool builders, end-users of the process, and fundamental research groups), and it is necessary to effectively share this knowledge. The aim is to produce a virtual mirror of the real process, integrating innovative monitoring systems (intelligent grinding wheel) and original theoretical and experimental approaches to grinding processes. The structure of the DT is composed of four layers, which integrate: (1) scientific knowledge of the process (advanced process modeling and numerical simulation); (2) characterization of materials through specialized mechanical testing; (3) advanced sensing techniques, to provide feedback for the process models; and (4) knowledge integration in a configurable open-source industrial tool. Each layer is described in detail in the following paragraphs, while Figure 1 illustrates the general concept underlying the DT.

**Figure 1.** Structure and interaction between layers in the digital twin (DT) for the grinding industry.

#### *3.1. Layer 1: Numerical Modeling of Mechanical Behavior of the Grinding Wheel Using Advanced Process Simulation*

One of the main reasons for the lack of systematic knowledge about the behavior of the grinding process itself, and in particular, the behavior of the abrasive tool (the grinding wheel) lies in the fact that grinding wheels are manufactured by mixing abrasive and bonding agents along with certain other components. Since the specification is not fully standardized, and there are no standard mechanical tests for wheel characterization, the result is that it is very difficult to predict the behavior of a grinding wheel for a given part. Although general rules can be followed, the industrial practice of leading wheel manufacturers (Tyrolit, Saint-Gobain, etc.) for the optimum selection of a grinding wheel is absolutely dependent on each specific application. Due to this fact, wheel manufacturers are continuously developing new types of grinding wheels, mixing different grain shapes and sizes, inducing porosity in an artificial way or changing the bonding material properties in order to meet the requirements of new materials to be ground. The development of adequate grinding wheels is particularly important when profile wheels must be used. In this case, whilst it is possible to find not only an optimal material removal rate and good surface quality, it is also important to take into account minimum volumetric wear of the wheel in order to achieve optimal geometrical accuracy. Although this is one of the biggest challenges faced by the current industrial environment, very little information can be found in the scientific literature regarding the link between grinding wheel characteristics and performance. Consequently, in industrial practice, extensive trial-and-error experiments must be carried out in order to determine the optimum parameters for a given application.

In order to meet this challenge, scientific process models can be considered as the deepest layer of a DT of the process. As shown in the literature review, a number of different theoretical models of the process are available. Unfortunately, few of these can be generalized to industrial practice due to the unique specifications of each grinding wheel structure. Thus, a model of the process must consider the interactions between the different components of the grinding wheel, namely abrasive grits, bonding agents, and porosity, along with the interaction between the grinding wheel and the part to be machined.

In the present study, the discrete element method (DEM) has been selected as the optimum numerical tool for the modeling and simulation of the grinding process. DEM models allow for reproducing the granular structure of the grinding wheel and the mechanical behavior of the bonding bridges. Likewise, the development of cohesive beam models allows for the simulation of continuous bodies using DEM [62]. The mechanical properties of the bonding agent can be replicated at a microscopic level in such a way that the complete behavior of the wheel body can easily be reproduced, regardless of its composition. The interactions with the part material can also be modelled; by doing so, it is possible to predict critical process data such as the contact forces, temperatures and power consumption. The effect of wheel rotation can also be included in the model, which is an interesting contribution, particularly for high-speed grinding processes. These possibilities are supported by preliminary investigations conducted by our research group, such as those published in [63,64]. Nonetheless, intensive research is currently being carried out to optimize DEM models and their application to the DT of the grinding process. Figure 2 shows a DEM model of a high-performance alumina grinding wheel. This model is being developed in collaboration with the wheel manufacturer UNESA (Abrasivos UNESA, S.A.,Hernani, Spain) and the ENSAM of Bordeaux. In the present DEM model the real grinding process is modelled. To this end, not only real grinding parameters are considered, but also the composition of real grinding wheels. Firstly, a discrete grinding wheel is built in order to characterize the mechanical behavior of the wheel, especially the behavior of the bond. To this end the size of the discrete elements (DEs) are equal to the real abrasive grain, 350 μm. This hypothesis allows the isolation of a bond fracture from the other types of wheel wear. In order to minimize the computational cost of the model, instead of modelling the real grinding wheel, which has an actual diameter of 400 mm, a wheel with a reduced diameter of 30 mm is modelled. Likewise, the width and thickness are also reduced, and are modelled with a width of 5 mm and a wheel thickness

of 10 mm. The modification of the external diameter of the grinding wheel also affects different process parameters, such as the grinding wheel speed or the contact length, therefore the corresponding parameter correlations are also performed. Moreover, the real contact length and the real contact time is considered in the DEM model, therefore, a specific workpiece shape is designed. Finally, the real force generated during grinding is introduced in the model with the aim of reproducing the mechanical behaviour of the grinding wheel during the grinding process.

**Figure 2.** The discrete element method DEM model of a high-performance alumina grinding wheel.

Layer 1 only makes sense by interacting with the rest of the elements of the digital twin. Thus, mechanical characterization of the properties of both the bonding agent and the abrasive grits requires the development of advanced and specific mechanical tests for the wheel, which is considered a quasi-brittle material. Layer 2 focuses specifically on those tests (see below), and the knowledge generated in Layer 2 must immediately be made available to the DEM models that constitute Layer 1. Moreover, the model can also be fed with actual data inputs from the industrial process (see Layer 3 in this Section). Finally, the DT must address the classic problems of computationally heavy numerical models, which are barely applicable to production-oriented industrial workshops. The integration with Layer 4 is therefore a key issue for the DT.

#### *3.2. Layer 2: Advanced Testing for Mechanical Characterization of Alumina Grinding Wheels*

As previously mentioned, one of the drawbacks for mechanical characterization of grinding wheels is their composition. The wheels are composed of abrasive grains, bonding agents and pores, and each of these elements have different mechanical properties. Moreover, during grinding wheel manufacturing, high temperatures are reached, which also modifies the mechanical properties of the body. Whilst there are studies in the literature that have conducted in-depth analyses to determine the mechanical properties of abrasive grains, there is no consensus regarding the values of such properties. Moreover, the most recent trend has been to manufacture customized grinding wheels depending on the particular needs of each case. Thus, for each application, there are differences in abrasive and bonding materials and grain shape and wheel porosity, which hinders establishing a common

characterization of grinding wheels. Therefore, following a review of the literature regarding the mechanical properties and behavior of grinding wheels and composites, it is concluded that the best option is to characterize the mechanical behavior of these grinding wheels as a concrete, and thus, quasi-brittle material.

Quasi-brittle materials show a nonlinear response, combining a moderate strain hardening (which is a characteristic of metallic materials) with sharp softening responses representative of brittle materials. With regard to the behavior of the wheel binder during grinding, among the various mechanical properties, particular attention is paid to the ultimate principal tensile strength σ*max* which will be used as an input parameter in the DEM model proposed in Layer 1. Likewise, Young's modulus E and the Poisson coefficient ν are the two other input mechanical properties in the DEM model. The development of precise experimental methods for determining these variables also becomes a primary objective when developing the DT of the process. The behavior laws of the grinding wheel material, assumed to be a quasi-brittle material, must be determined by mechanical tests such as the Brazilian test, which is described in the following paragraphs.

In the reviewed literature, the Brazilian tests are carried out using a disk, as shown in Figure 3a. However, this specimen configuration does not take into account the shape of the grinding wheel, which has a central hole. Moreover, the shape of the specimen determines the beginning of crack propagation. Therefore, new specimens are designed and manufactured as shown in Figure 3b. The specimen presents a central hole and flat surfaces to secure the specimen and to impose the load.

**Figure 3.** A disk specimen configuration used for (**a**) conventional Brazilian tests and (**b**) modified Brazilian tests.

The modified Brazilian tests need to include digital image correlation (DIC) analysis and fractography in order to thoroughly analyze crack propagation together with the quantification of ultimate principal tensile strength σ*max*. The values of ultimate tensile strengths varied between 60 and 85 MPa for the different specimens that were tested. The values were archived, as this information is critical for feeding the DEM simulation model presented in Layer 1. The ultimate tensile strength determines the fracture of the bonding agent and thus the wear of the grinding wheel.

#### *3.3. Layer 3: A New Generation of Grinding Process-Monitoring Systems: Intelligent Grinding Process*