**1. Introduction**

From the physics point of view, the financial markets are considered as one of the most complex systems we observe in our world [1]. Not only they are characterised by all the properties such systems can typically show, but there is also an important intelligent component involved that is decisively responsible for their enormous complexity. Among the well-known features of the financial markets is their flexibility in the transition between the disordered and ordered phases. Such a transition is the key feature associated with the market crashes but it is also often observed on the level of whole markets when some so-far independent markets start to have their dynamics substantially coupled (or *vice-versa*). Exactly this kind of phenomenon has recently been experienced by the cryptocurrency market, which has lost its relative dynamical autonomy and become significantly tied to the traditional financial instruments. In this work, we present quantitative arguments in support of this statement.

Since the inception of Bitcoin in 2009, the cryptocurrency market has experienced a rapid surge. Although it used to be a niche and traded unofficially in its early years [2], trading takes place now 24/7 on more than 500 exchanges [3]. The current (October 2022) capitalization of the cryptocurrency market is approximately 1 trillion USD [3], which can

**Citation:** W ˛atorek, M.; Kwapie ´n, J.; Drozd˙ z, S. Cryptocurrencies Are ˙ Becoming Part of the World Global Financial Market. *Entropy* **2023**, *25*, 377. https://doi.org/10.3390/ e25020377

Academic Editors: Panos Argyrakis and José F. F. Mendes

Received: 9 November 2022 Revised: 5 December 2022 Accepted: 16 February 2023 Published: 18 February 2023

**Copyright:** © 2023 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/).

be compared to the largest US tech stocks. During these 12 years of Bitcoin history, there were bubbles and crashes [4–6]. In particular, the foundation of Ethereum in 2015, which allowed for a new application of the blockchain technology in the form of smart contracts, and the subsequent Initial Coin Offer bubble [7] in 2018 reshaped the cryptocurrency market and made it appear in the public eye. A recent bubble in 2021, which was related to the adoption of DeFi (Decentralized Finance) and DEX (Decentralized Exchanges) trading [8], ended with a peak in November 2021, when the total market capitalization was close to 3 trillion dollars. Although there are more than 10,000 cryptocurrencies [3], Bitcoin and Ethereum are currently the most recognizable, and their share in the capitalization of the entire market changed from over 80% in early 2021 to 60% in October 2022 [3].

During these 12 years of development, the characteristics of the cryptocurrency market have changed significantly [9,10]. The properties of the cryptocurrency price return time series are now close to those observed in mature financial markets such as Forex [11]. However, it has long been believed that the cryptocurrency market, which itself is strongly correlated [12–18], especially during the COVID-19 period [19–23], has dynamics that is separate from the traditional financial markets [24–28] and that bitcoin can even serve as a hedge or safe haven [29,30] with respect to the stock market, Forex or the commodity market. The hedging potential of bitcoin was even compared to gold [31–35]. However, the results of many recent studies have changed this paradigm [36–43]. They reported that during the COVID-19 pandemic outburst and the related crash in March 2020 [44,45] the cryptocurrency market and, in particular, bitcoin was highly correlated with the falling stock markets [46–52]. Some studies even noted that this connection still occurred in the market recovery phase in the second half of 2020 [15,53].

The studies referenced above brought therefore rather mixed results and have led to uncertainty as to whether cryptocurrencies can be used for hedging the financial investments. This uncertainty opens space for further research on this topic and our study proceeds exactly in this direction. Our aim is to clarify whether the loss of the cryptocurrency market independence was temporary and primarily caused by the pandemic turmoil or it was only a part of a more general trend towards coupling of this market with the traditional financial markets. We intend to determine how strongly the cryptocurrency price changes are associated with the price changes in the traditional financial markets. To achieve that, the detrended multiscale correlation of the two principal cryptocurrencies: bitcoin (BTC) and ethereum (ETH) versus the traditional financial instruments: stock indices, commodities and currency exchange rates are studied based on high frequency data covering the period from January 2020 to October 2022, which is an extension of the period before 2020 that was analyzed in our earlier study [47]. The year 2022 is particularly interesting, because since the BTC price peak in November 2021, there is a joint bear market on the US tech stocks and the cryptocurrencies for the first time in the existence of the latter. On the basis of this observation, it is likely that there will be some detectable correlation between both markets. The year 2022 is also unique in the history of cryptocurrencies due to the presence of high inflation in the world against which Bitcoin was intended to protect [54–57].

Compared to the other articles dealing with correlations between the cryptocurrency market and traditional financial markets, in our research, the main task is to measure these correlations quantitatively on various time scales and for the fluctuations of various size. It can broaden the market practitioners' perspective on the investing and hedging possibilities by incorporating the fluctuation size as an additional dimension that might be considered while making investment decisions.


2.1.4. CSR towards Employees

Employees are a vital resource of a company [44]. Employees' competence is necessary and can be considered a core value of an enterprise. In cases where employees are not satisfied with their jobs, work performance is affected. The quality and quantity of products and services are prone to be reduced. Taking on CSR towards employees is necessary for a company to maintain its business and improve its outcomes [45]. The foundations of CSR should focus on employees' wellbeing [46]. There are many frameworks, such as Social Accountability International's SA8000 standard, FLA Workplace Code, etc., which provide guidance on maintaining social practices in the workplace [36]. CSR towards employees can be considered as an enterprise paying adequate salaries to their employees [47], creating safe, decent working conditions, and providing professional development and promotion opportunities to their employees [48]. It is also worth mentioning that companies must treat their employees fairly (without region or gender discrimination or abuse) as well as offer their employees adequate medical insurance [28,38,39]. The following hypothesis is assumed:

**Hypothesis 4 (H4).** *CSR towards employees has a positive impact on overall CSR.*

#### 2.1.5. CSR towards Suppliers

There is an important link between suppliers and a company in a distribution chain and that connection is key in a company's business. The reason for this is that suppliers provide a company with materials with reasonable prices and quality. If a company does not fulfill its duties towards suppliers, the materials' quality may be unreliable. Consequently, its products and services can be affected. Therefore, embracing CSR towards suppliers is of importance for a company to run its business [49]. CSR towards suppliers can be considered as an enterprise providing fair terms and conditions for all suppliers. Furthermore, enterprises must communicate openly, honestly, and confidentially, as well as comply with contractual payment terms [28]. The following hypothesis is assumed:


*2.3. Optimization Procedure*

As the hybrid system operating variables have a nonlinear relation, it is required to find a nonlinear solving algorithm to catch the optimal design. Therefore, the Generalized Reduced Gradient (GRG) algorithm was used. This algorithm can figure out the capacities of the hybrid systems. The adoption of the optimization procedure is depicted in Figure 2. Moreover, the optimization objective, constraints, and decision variables are given in Table 1.

**Figure 2.** The optimization procedure of the hybrid system components.

**Table 1.** The components of the optimization problem.


**4. The rationale for studying the AFM in Zimbabwe Church**

According to Ndlovu ([2], pp. 1-4) is presently one of the fastest-growing Pentecostal churches in Zimbabwe. It is also considered to be the mother of Pentecostalism in Zimbabwe [2, 3]. AFM is an offshoot of the Apostolic Faith Mission of South Africa (AFMSA) (Hwata, 2015; [2–6]). Ndlovu ([2], p. 119) further argues that;

*Langton Kupara became the first A.F.M.Z.'s black Superintendent in 1983. According to Madziyire and Risinamhodzi (2016, p. 73) Pastor Gashwend officially handed over A.F.M. to local leadership in 1983. During the occasion Gashwend gave Langton Kupara a golden watch, making the complete transformation of A.F.M.S.A. into A.F.M.Z.*

Several studies that have been undertaken from within the context of the AFM have both directly and indirectly alluded to the leadership crisis that is becoming evident in the AFM [2, 3, 5–10]. It is also worth noting that right at the beginning of this work to this present day, AFM has been characterised by innuendos that suggest that the gigantic Pentecostal movement is embroiled in its worst power contestation ever since it became an indigenous church ([2], pp. 1-4; [3, 5, 8]). According to The AFM New Life Assembly Church of Chitungwiza, Harare, website: 'The work of the AFM in Zimbabwe is said to have begun in 1915 in Gwanda through the preaching of Zacharias Manamela, a convert of the AFM of South Africa.'1 Similarly, Chivasa [7] observes that, "It has long been recognised that the Apostolic Faith Mission (AFM) in Zimbabwe church is the mother of Pentecostalism in Zimbabwe (Hwata 2005, p. 27; [4], p. 18; [3], p. 31; [6], p. 2). In 2015, the AFM in Zimbabwe celebrated 100 years of active ministry across Zimbabwe (Madziyire & Risinamhodzi 2015, p. 105; Murefu 2015, p. 1) and the denomination claims to have more than two million members (http://www.pindula.co.zw) in a population of 13 million people ([4], p. 17)."

Additionally, several young churches look up to AFMZ as a role model of Pentecostalism that is not only pastoring its members only but it is pastoring other churches too. Machingura [4] further posits that, "The interest on AFM cannot be overlooked considering the number of followers that the church commands, yet surprisingly not much has been done by scholars in terms of research save to mention in passing its existence as one of the biggest Pentecostal churches" [4]. The motivation to conduct this analysis was sparked by two factors. The first was the prevalence of leadership succession-related power contestation in the AFM. The second was that no efforts to date have been made to examine the change of the traditional model of transferring power as the main contributing factor to the widespread power contestation that has engendered incessant schisms in the church under review. As such the current study attempts to fill this gap and it makes a case for the need to resurrect the traditional institutionalised

1 See, for example, [11].

*Organizational Conflict - New Insights*

leadership succession planning trend as well as advocates for the responsible management of appreciations as the panacea to the conflict of power that is currently rocking the AFM in Zimbabwe. To interrogate the conflict of power, this research was guided by the following three questions: What evidence is there that there is a conflict of power associated with leadership succession in the AFM? Which factor(s) mainly contributed to the widespread conflict of power in the AFM? How significant is the traditional model of transferring power and appreciations at the top level in the AFM?


Contact Angle Measurement

Static contact angle of NRL and composite films prepared by NOCNF and NRL were measured using the FDS-contact angle measurement instrument (Model no. OCA 15 EC). Films of NRL alone and composite films containing NRL and NOCNF were prepared by solvent casting method. A flat portion of film was cut and fixed onto sample holder. A 10 μL drop of deionized water was dropped onto film through a syringe needle operated automatically by syringe pump. The contact angle was measured 20 s after the drop casting to ensure that the water droplet reached its equilibrium position.


**Arthroplasty of Spine and Upper Extremity**

496 Recent Advances in Arthroplasty

[26] Vichinsky EP, Neumayr LD, Earles AN, Williams R, Lennette ET, Dean D, Nickerson B,

Study Group. N Engl J Med, 2000;342:1855–1865.

Orringer E, McKie V, Bellevue R, Daeschner C, Manci EA. Causes and outcomes of the acute chest syndrome in sickle cell disease. National Acute Chest Syndrome

**23**

*USA*

Bruce V. Darden

**Cervical Disc Arthroplasty**

For more than 50 years, anterior cervical discectomy and fusion (ACDF) has been the workhorse procedure for cervical degenerative pathology. (Bailey & Badgely, 1960; Cloward, 1961; Robinson & Smith, 1955) The procedure has yielded successful results clinically in multiple large series. (Bohlman et al, 1993; Gore & Sepic, 1984) Advances in allograft and cage techniques as well as the use of anterior plating systems have diminished complications in ACDF. However, concerns about adjacent segment degeneration (ASD) have tempered some enthusiasm for the procedure. Gore et al (Gore & Sepic, 1998) reviewed a series of 50 ACDF patients followed long term. Almost universally, the patients developed ASD. One-third of the cohort developed recurrent pain with half of the symptomatic group requiring additional surgery. Hilibrand et al (Hilibrand et al, 1999) evaluated a group of 374 patients undergoing ACDF. They showed a 2.9% per year risk of development of symptomatic ASD, with two thirds of the symptomatic patients requiring additional surgery. Goffin et al (Goffin et al, 1995) prospectively followed a series of ACDF patients who underwent the procedure for either a degenerative or traumatic condition. Follow-up was for five to nine years. Sixty percent of the patients developed ASD, equally distributed between the older degenerative population and the younger traumatic population, providing evidence that fusion may accelerate degenerative changes. Goffin et al (Goffin et al, 2004) reviewed a larger series of ACDF patients followed for an average of 8.3 years. In this group, 92% of the patients developed ASD, though they had a much lower rate of additional surgical procedures, 6.1% for the entire length of follow-up, distinctly lower than Hilibrand et al. Numerous cadaveric biomechanical studies (Eck et al, 2002; Pospiech et al, 1999) evaluating adjacent level intradiscal pressures and range of motion in simulated fusion models have shown that both increase after fusion. These altered biomechanics may

Against this background, centers began experimenting with cervical disc arthroplasty in the 1980s. Cummins and collaborators at the Frenchay Hospital, Bristol, England developed a metal-on-metal ball and socket arthroplasty and implanted it on a small series of patients in the 1990s. (Cummins et al, 1998) The arthroplasty underwent a number of design changes and is now known as Prestige. Bryan, in the US, developed a one piece metal-on-polymer device called the Bryan Cervical Disc Replacement, initially evaluated clinically in Europe. (Goffin et al, 2002) ProDisc-C arthroplasty is a metal-on-polyethylene implant adopted from the ProDisc-L lumbar disc arthroplasty developed by Thierry Marnay. (Delamarter & Pradhan, 2004) Since

**1. Introduction**

thus accelerate ASD.

 *OrthoCarolina Spine Center, Charlotte*


*Article* **Using Entropy for Welds Segmentation and Evaluation**

#### **Oto Ha**ff**ner \*, Erik Kuˇcera , Peter Drahoš and Ján Cigánek**

Faculty of Electrical Engineering and Information Technology, Slovak University of Technology in Bratislava, 841 04 Bratislava, Slovakia<EMAIL_ADDRESS>(E.K<EMAIL_ADDRESS>(P.D<EMAIL_ADDRESS>(J.C.)

**\*** Correspondence<EMAIL_ADDRESS>
Received: 24 October 2019; Accepted: 26 November 2019; Published: 28 November 2019

**Abstract:** In this paper, a methodology based on weld segmentation using entropy and evaluation by conventional and convolution neural networks to evaluate quality of welds is developed. Compared to conventional neural networks, there is no use of image preprocessing (weld segmentation based on entropy) or data representation for the convolution neural networks in our experiments. The experiments are performed on 6422 weld image samples and the performance results of both types of neural network are compared to the conventional methods. In all experiments, neural networks implemented and trained using the proposed approach delivered excellent results with a success rate of nearly 100%. The best results were achieved using convolution neural networks which provided excellent results and with almost no pre-processing of image data required.

**Keywords:** weld segmentation; local entropy filter; weld evaluation; convolution neural network; image entropy; Python; Keras; RSNNS; MXNet

#### **1. Introduction**

The Fourth Industrial Revolution (Industry 4.0) has opened space for research and development of new manufacturing methods, systems and equipment based on innovations such as computing intelligence, autonomous robots, big data, augmented reality, process simulation, quality management systems, etc. [1].

Weld evaluation is very important quality control process in many manufacturing processes. Without this technological process, it would be almost impossible to produce welded constructions with current efficiency—whether we are talking about time, price, or material consumption. It is therefore necessary to welds be inspected to meet the specified quality level. In order to detect the possible presence of different weld defects, proper sensing, monitoring and inspection methods are necessary for quality control. Very effective and non-destructive method for weld evaluation is visual inspection. Inspection process using this method can be in certain level automated and done by computer systems [2,3].

Visual inspection of a weld is an important non-destructive method for weld quality diagnostics that enables to check welded joint and its various parameters. This examination is carried out as a first examination and able to detect various defects [4].

In this paper, we focus on indirect visual evaluation due to which the evaluation process can be automated. Indirect inspection can be applied also in places that are not directly accessible, for example the inner surface of a pipeline, the interior of pressure vessels, car body cavities etc. It also eliminates errors of human judgment and removes errors caused by workers for such reasons as e.g., fatigue, inattention or lack of experience.

The improved beamlet transformation for weld toe detection described in [5,6] considers images which are corrupted by noise. The authors aim at detecting edge borders of welds. The dynamic thresholding is performed in one of the beamlet algorithm steps. The algorithm predicts the directional characteristics of the weld allows to filtrate unsuitable edges. Using this method, it is possible to directly extract weld seam edges from highly noisy welding images without any pre-processing or post-processing steps.

In [7], the authors work with pipeline weld images with a very low contrast and corrupted by noise; this causes problems to conventional edge detectors. At first, the image is noise-filtered using a morphological operation of opening and closing. Next, the improved algorithm of fuzzy edge detection is applied. Multi-level fuzzy image improvement is based on interactive searching of optimal threshold level and multi-directional edge detector which convolution kernel is 5 × 5 with 8 directions based on gradient searching. The result of the algorithm is compared with detectors as Sobel, canny FED and fast FED.

Edge detection and histogram projection are used in [8], where histogram projections of tested welds are compared with a specified similarity threshold used to evaluate quality of the tested welds. The loaded image pattern has the same specifications (width and position) as the tested image. Always one vertical line from the pattern and the tested images is compared. Line histograms of pattern and tested images are computed, the correlation degree of two histograms is computed using the Tukey HSD difference. A lower correlation degree than the specified correlation threshold indicates edge defects in this part of the examined image. The procedure is repeated over the entire width of image.

Evaluation of metal cans welds is dealt with in [9]. Can's weld defects may not be directly related to welding (they can be brought about by rest of glue, dust, etc.). Therefore, authors use probability evaluation of two evaluation methods; the Column Gray-Level Accumulation Inspection represents histogram projection in general. The histogram projections of the pattern and the tested weld are compared. The comparison of first derivation for making better results is also performed. This method can detect defects of wider surface. The overall evaluation is done using Dampster-Shafer theory of evidence.

In another work [10], the above authors deal with edge detection based on pixel intensity difference of the foreground and the background. The background pixels' intensity occurs with a maximum probability and the distribution of the background pixels fits the Gauss distribution.

The weld visual inspection process performed through image processing on the image sequence to improve data accuracy is presented in [11]. The Convolution Neural Network (CNN) as an image processing technique can determine the feature automatically to classify the variation of each weld defect pattern. A classification using CNN consists of two stages: image extraction using image convolution, and image classification using neural network. The proposed evaluation system has obtained classification for four different types of weld defects with validation accuracy of 95.83%.

A technique for automatic endpoint detection of weld seam removal in a robotic abrasive belt grinding process using a vision system based on deep learning is demonstrated in [12]. The paper presents results of the first investigative stage of semantic segmentation of weld seam removal states using encoder-decoder convolutional neural networks (EDCNN). The prediction system based on semantic segmentation is able to monitor weld profile geometry evolution taking into account the varying belt grinding parameters during machining which allows further process optimization.

Utilizing computing intelligent using support vector machine (SVM) is presented in [13,14]. Authors developed real-time monitoring system to automatically evaluate the welding quality during high-power disk laser welding. Fifteen features were extracted from images of laser-induced metal vapor during welding. To detect the optimal feature subset for SVM, a feature selection method based on the SFFS algorithm was applied. An accuracy of 98.11% by 10-fold cross validation was achieved for the SVM classifier generated by the ten selected features. The authors declare the method has the potential to be applied in the real-time monitoring of high-power laser welding.

The authors of [15–18] deal with the development of a system for automatic weld evaluation using new information technologies based on cloud computing and single-board computer in the context of Industry 4.0. The proposed approach is based on using a visual system for weld recognition, and a neural network cloud computing for real-time weld evaluation, both implemented on a single-board low-cost computer. The proposed evaluation system was successfully verified on welding samples corresponding to a real welding process. The system considerably contributes to the weld diagnostics in industrial processes of small- and medium-sized enterprises. In [18], the same authors use a single-board computer able to communicate with an Android smartphone which is a very good interface for a worker or his shift manager. The basic result of this paper is a proposal of a weld quality evaluation system that consists of a single-board computer in combination with Android smartphone.

This paper deals with development of a software system for visual weld quality evaluation based on weld segmentation using entropy and evaluation by conventional and convolution neural networks. The evaluation of the performance results is compared to the conventional methods (weld segmentation based on entropy and evaluation using conventional neural networks with and without weld segmentation). Most experiments of proposed method apply on weld metal, however, one experiment with convolution neural networks applies also on weld adjected zones. 6422 real and adjusted laboratory samples of welds are used for experiments. The paper is organized in five sections: Section 2 deals with preparation of input data for the neural network. Section 3 describes configuration of used neural networks and their training process. In Section 4 the results of experiments are presented. In Section 5 we discuss the results.

#### **2. Preparation of Input Data for the Neural Network**

The input data for the proposed diagnostic system were represented in the form of grayscale laboratory samples of metal sheet welds in JPEG format. The samples were pre-classified as OK (correct) and NOK (incorrect) (Figures 1 and 2). Defective weld samples (NOK) include samples of various surface defects such as irregular weld bead, excess weld metal, craters, undercut, etc. Welds images are captured under the same illumination and have the same resolution 263 × 300 pixels. The total number of evaluated sample images was 6422.

However, for several reasons the image resolution 263 × 300 pixels is not suitable for a conventional neural network due to the necessity of large amount of allocated memory (about gigabytes for thousands of frames even in a relatively low resolution) and time-consuming network training time.

**Figure 1.** Laboratory sample of an OK weld.

**Figure 2.** Laboratory sample of NOK weld.

Several suitable options for data processing that eliminate the above problems are presented next. At first, the background weld segmentation is described. Segmentation provides two outputs - the weld mask and the segmented weld itself. Three transformations of the weld mask into a one-dimensional feature vector are described further. Feature vectors are useful as inputs for the multilayer perceptron (MLP)/radial basis function (RBF) neural networks. Finally, the size of the segmented/unsegmented weld image is reduced when applied in the conventional neural network (if CNN is applied, no size reduction is needed).

#### *2.1. Weld Segmentation*

The sample images depict the weld itself and the background—metal sheet. The background does not affect the evaluation of the weld and is masked from the images by the proposed algorithm. The simplified flowchart of the algorithm is shown in Figure 3.

**Figure 3.** The simplified flowchart of the segmentation algorithm.

After reading the images, local entropy of each pixel is computed according to [19]:

$$\sum\_{i=1}^{K} \sum\_{j=1}^{K} p\_{ij} \log\_2 p\_{ij} \tag{1}$$

where *pij* represents the probability function for the pixel [*i*, *j*].

This value contains information about the complexity/unevenness around the pixel. The neighbourhood radius was set to 8 pixels. To compute the entropy, the filters.rank.entropy function from the Python library scikit-image was used. The resulting local entropy matrix effectively finds the edges and texture complexity in the image. The results of filtering can be seen in Figure 4.

As the entropy resolution values were too detailed for our application, the blur filtering was applied. The anisotropic blur filter from the imager library was implemented, which removes noise/unimportant details while preserving edges better than other types of blur filters. The blur filter with an amplitude of 250 was applied (Figure 5).

(**a**) Input image (**b**) Local entropy matrix

(**a**) Local entropy matrix (**b**) Result of anisotropic blur filtering

**Figure 5.** Step 2—blur filtering.

**Figure 4.** Step 1—local entropy computing.

*Entropy* **2019**, *21*, 1168

The next step is thresholding. In the image matrix, the value 1 (white) represents weld pixels, the value 0 (black) represents background. Thresholding was implemented using the function threshold from the imager library. The optimal threshold value was computed automatically using the kmeans method (Figure 6).

(**a**) Input for thresholding (**b**) Output – a mask

**Figure 6.** Step 3—thresholding.

The thresholding result may have some imperfections—small blobs and unfilled areas. Unfilled areas are removed using the inverted output of the function bucketfill (imager library). It is applied on the background of the weld and it finds all pixels of the background. The remaining the pixels are filled with value 1 (white) (Figure 7a).

(**a**) filling holes (**b**) morphological simplification

**Figure 7.** Step 4—filling holes (**a**) and morphological simplification (**b**).

Very small blobs were removed using the function clean (imager library). This function reduces objects size using morphological erosion, and then increases it. This causes, that very small objects are removed and the shape of larger object is simplified (Figure 7b).

However, larger blobs were not removed in the previous step. To find the largest object in the image, the function split\_connected (imager library) was used (Figure 8).

**Figure 8.** Step 5—Finding the largest object.

The segmentation result—the mask and the masked weld can be seen in Figure 9.

(**a**) The resulting weld mask (**b**) The resulting segmented weld

#### *2.2. Vector of Sums of Subfields in the Mask*

The first representation of the mask is a vector which entries are sums of subfields. For input images of resolution 263 × 300 pixels, was selected a subfield of 50 × 50 pixels, which corresponds to 36 values. The function for vector calculation is shown in the Algorithm 1.

The function ceiling rounds a number to the next higher integer. Using division of the index (*i*, *j*) by the size of the subfield, and subsequently the function ceiling, we obtained *indI*/*indJ* for the selected index *i*/*j*. The function as.vector retypes the resulting two-dimensional array into a vector by writing the matrix elements column-wise into a vector. Example of retyping can be understood from Figures 10 and 11.

Graphs for OK and NOK welds (Figure 12) can be compared in Figure 13: the OK mask graph has every third value (representing the subfields in the image center) maximal. Values of the NOK weld graph are distributed into more columns and the values do not achieve maximum values. The main drawback of this representation is that it can be used only for images with the same size. The benefit is a multiple reduction of input data (number of mask pixels in our case has been reduced 502-times).

**Figure 10.** Two-dimensional array of sums.

**Figure 11.** Resulting vector of sums.

(**a**) OK weld mask (**b**) NOK weld mask

**Figure 12.** Weld masks.

**Figure 13.** Graphs of vector of sums of subfields in the mask for OK (**a**) and NOK (**b**) weld.

#### *2.3. Histogram Projection of the Mask*

A histogram projection is a vector containing sums of columns and rows of the input image matrix (Figure 14). In the case of an image mask, these are amounts representing numbers of white pixels. Thus, the length of the vector corresponds to the vector of the height and width of the image.

In the graphs (Figures 15 and 16) showing the histogram projection of the mask, the difference between correct and wrong welds is visible. The projection of the correct weld mask is more even, the sums by columns have an even increase and slope, and the sums per line have small variations. On the other hand, the histogram projection of the wrong weld mask has a lot of irregularities. The disadvantage of this representation consists in that it cannot be used for input images of different resolutions. The resulting projection vector is much larger than other representations. The advantage is easy implementation and calculation.

(**a**) OK weld mask (**b**) NOK weld mask

**Figure 15.** Graph of histogram projection of an OK weld.

**Figure 16.** Graph of histogram projection of a NOK weld.

#### *2.4. Vector of Polar Coordinates of the Mask Boundary*

A next representation of a weld mask in this paper is the vector of polar coordinates of the mask boundary. To transform weld masks, an algorithm has been proposed and implemented. Its main steps are described below.

The first step is to find the *x*, *y* coordinates of the mask boundary using the function boundary (imager library). Then, coordinates of the center of the object [*cx*, *cy*] are calculated according to:

$$c\_x = \frac{\max(\mathbf{x}) - \min(\mathbf{x})}{2} + \min(\mathbf{x}),\tag{2}$$

$$c\_{\!y} = \frac{\max(y) - \min(y)}{2} + \min(y), \tag{3}$$

In the next step, the position of the object is normalized (the center is moved to the position [0, 0]) according to the found coordinates. Then, for each boundary point, the coordinates are converted from Cartesian to polar [*r*, α] (i.e., distance from center, angle). According to the Pythagorean theorem, the distance is calculated as follows:

$$
\sigma = \sqrt{\mathbf{x}^2 + \mathbf{x}^2},\tag{4}
$$

Calculation of the angle is realized by Algorithm 2:

**Algorithm 2.** Calculation of angle from Cartesian coordinates

**procedure** Angle(x, y) z ← x + 1i \* y a ← 90 - arg(z) / π \* 180 return round(a mod 360) **end procedure**

If the resulting number of coordinates is less than 360, the missing angle values are completed and the corresponding distances are calculated from the surrounding values by linear interpolation using the na\_approx function (zoo library). The result is a vector with 360 elements, which indices correspond to the angle values in degrees, and the value is the distance r. The resulting graphs of OK and NOK weld masks (Figure 17) are in Figures 18 and 19.

**Figure 19.** Graph of polar coordinates vector of a NOK weld mask.

The representation in the form of polar coordinates for the OK weld visibly differs from the NOK one. The big jumps and variations on the graph are caused by large irregularities in the weld shape. The advantage of such representation is that it can be used for any input mask resolution. The disadvantage is a complicated calculation. Generally, mask representations contain information only about the shape of the weld, which can be considered as a disadvantage because texture information is important input data for the neural network.

#### *2.5. Data Preparation for Neural Network*

Weld images and feature vectors were stored in two data structures of type list. The first list represented welds classified as NOK (incorrect); the second list welds classified as OK (correct). For neural networks, it was necessary to combine data, i.e., to transform and randomly mix them. For MLP and RBF networks, each input vector has to have assigned a classification value 0 (incorrect) or 1 (correct). Then, the vectors were merged together and with randomly mixed elements. Next, the L2-normalization was applied to the data. Finally, 85% of training and 15% of test samples were selected randomly. For convolution neural networks, the images were 5-times reduced, then the data type was converted to a three-dimensional array data structure. In the arrays, the dimensions were transposed to represent to correspond to the following structure: [*number o f images*∗*length* ∗ *height*]. The vector of zeros with the same length as the first dimension corresponded to the first array (array of NOK welds). The vector of ones corresponded to the second array (array of OK welds). The arrays and vectors were merged into a common list and their elements were mixed randomly. Then, 77% of training samples, 15% of test samples and 8% of validation samples were selected.

#### **3. Configuration and Training of Neural Networks**

Several neural network architectures were configured for comparison and testing. Their parameters were changed during the experiments and the experiment results were compared and evaluated. Both RBF and MLP networks were configured in The Stuttgart Neural Network Simulator for R language - RSNNS library, the MLP networks were configured in the Keras library, and the convolution networks were configured in the Keras and the MXNet libraries.

#### *3.1. RBF Network*

To implement the RBF network, the RSNNS library was chosen (just in this one the RBF network template is available). Three RBF networks were configured using the function rbf (RSNN library). The set parameters were the number of units in the hidden layer and the number of epochs, the initial parameters had default values. The best configurations were chosen experimentally. Configuration details are in Figures 20–22.

**Figure 20.** Settings for RBF network—for the vector of sums of subfields in the mask.

**Figure 21.** Settings for RBF network—for the histogram projection vector.

#### *3.2. MLP Network*

Experiments with training and testing of MLP networks showed, that a one-layer architecture is sufficient for our data representation. The performance of the network was very good and the difference from multiple hidden layers was negligible. To keep the objectivity, MLP networks had the same configuration in both libraries. The sigmoid activation function and the randomize weights initialization functions were used. For the NN training, the error backpropagation algorithm with learning parameter 0,1 was used.

The implementation in the RSNNS library uses the mlp function for configuration and training. Configuration details are in Figures 23–25.

**Figure 23.** Settings for MLP network—for vector of sums of subfields in the mask.

#### **Figure 24.** Settings for MLP network—for histogram projection vector.

**Figure 25.** Settings for MLP network - for the polar coordinates vector.

The implementation of the MLP network in the Keras library required a detailed list of layers in the code. Two layer\_dense layers were used; the first one defines the hidden layer with the ReLU activation function, and the second one defines the output layer with the size 2 (two output categories) using the softmax activation function (Figure 26).

**Figure 26.** MLP network architecture—for vector of sums of subfields in the mask.

#### *3.3. Convolution Neural Network*

For an objective comparison of the Keras and MXNet libraries, the same convolution network architecture in both libraries was used at first, however in the MXNet library, training such a neural network was too slow. Thus, we designed our own architecture with a better learning time performance. The discussion about the results is provided in the next Section 4.

The architecture of the convolution network 1 is shown in Figure 27 and visualized in Figure 28. The architecture includes a list of all layers and the size of output structures for both NN. Two pairs of convolution and pooling layers were used, the convolution being applied twice before the first pooling layer. The input image size was 56 × 60. The number of convolution filters was 32 at the beginning, in further convolution filters it rose to 64. A dropout was used between some layers to prevent overtraining of the neural network by deactivating a certain percentage of randomly selected neurons. At the end, the flatten layer was used to convert the resulting structure into a one-dimensional vector used as an input for a simple MLP network with one hidden layer containing 256 neurons.

**Figure 27.** Architecture of the convolution neural network 1.

**Figure 28.** Architecture visualization of the convolution neural network 1.

Parameters of individual layers are shown in the diagram in Figure 28. For example, the convolution layer (red) contains a list of 3 × 3 - filter size, 3 × 3 - stride, 32 - number of filters.

The architecture of the convolution network 2 is visualized in Figure 29. Two pairs of convolution and pooling layers were used, however in this case a double convolution occurs only in the second layer. There is also a difference in the design of the convolution, where the parameter stride (step of the filter) is 3,3. Dropout was used only in two places.

**Figure 29.** Architecture visualization of the Convolution neural network 2.

#### **4. Results**

This chapter presents results of code profiling, weld segmentation and evaluation of neural networks.

#### *4.1. Code Profiling*

Profiling was done using the profvis library at the level of the code line. The output is an interactive visualization using memory listing in MB and computing time in ms for each code line. The example can be seen in Figure 30.

**Figure 30.** Example of profiling output using profvis.

*Entropy* **2019**, *21*, 1168

Profiling was performed on a desktop computer with parameters listed in Table 1 (the graphic card was not used).

**Table 1.** Technical specifications of PC.

#### *4.2. Results of Data Preparation and Segmentation*

Segmentation was successful for all tested weld samples. For some NOK defective welds which consisted of several parts or contained droplets, only the largest continuous weld surface was segmented, which was considered to be a correct segmentation for proposed methodology. Segmentation examples are shown in Figure 31.

**Figure 31.** *Cont.*

*Entropy* **2019**, *21*, 1168

**Figure 31.** Examples of weld segmentation results (**a**–**p**).

The segmentation time is an important indicator in comparison of results. Results of profiling different parts of the segmentation process can be seen in Figure 32. Code profiling was carried out using a computer with the technical specification shown in Table 1.

**Figure 32.** Results of segmentation process profiling.

Segmentation was performed by concatenating the outputs from functions load.image, grayscale, entropyFilter, createMask, and segmentWeld. Almost all functions in this section of the program were performed very quickly (within 30 ms) except for the entropyFilter function, which took an average of 158 ms to be completed. This function is the most important part of the segmentation algorithm; the time was acceptable. The average time to complete the whole segmentation was 194 ms. The average

amount of memory allocated was 74.76 MB. For MLP and RBF networks, the next step was to transform masks into feature vectors. The profiling results of functions performing three types of transformations can be seen in Figure 33.

**Figure 33.** The profiling results of data transformation.

The results show that these functions are optimal, taking up minimal memory and time. The mean values for computing the vector of sums of subfields in the mask are 16 ms and 0.1 MB; for the histogram projection vector, it is less than 10 ms and less than 0.1 MB (estimation of profiling tool, real values are immeasurably small). Values for the polar coordinates vector are 18 ms and 7.56 MB. Presented results are also shown in Table 2.

**Table 2.** Algorithms results for transform masks into feature vectors.

#### *4.3. Criteria for Evaluation of Neural Network Results*

As the main criterion for results evaluation the confusion matrix was chosen. The main diagonal of the confusion matrix contains the numbers of correctly classified samples, the antidiagonal contains the numbers of incorrectly classified samples; the smaller values in the antidiagonal, the more successful the prediction model. In a binary classification this matrix contains four values (Figure 34): TP—true positive; FP—false positive; FN—false negative; TN—true negative.

**Figure 34.** Confusion matrix.

The accuracy was computed from the confusion matrix and is expressed as the ratio of correctly classified samples to all samples, see Equation (5) [20].

$$Accuracy = \frac{\sum TP + \sum TN}{\sum all\ samples} \,\,\,\tag{5}$$

Accuracy is an objective criterion only if the FN and FP values are similar.

A more objective criterion for comparing results is the F-score. The *F-score* is calculated as the harmonic average of the precision and the recall (sensitivity) values [20], the best score corresponds to F-score = 1:

$$Precision = \frac{\sum TP}{\sum TP + \sum FP'} \tag{6}$$

$$Recall = \frac{\sum TP}{\sum TP + \sum FN'} \tag{7}$$

$$F-score = \frac{2 \ast Recall \ast Precision}{\sum TP + \sum FN \, Recall + Precision} \tag{8}$$

To visualize the success of neural network classification, the ROC (Receiver operating characteristics) curve was chosen. It shows the recall (sensitivity) value depending on the value 1-specificity at the variable threshold [20] (Figure 35):

$$Specificity = \frac{\sum TN}{\sum TN + \sum FP'} \tag{9}$$

**Figure 35.** ROC curves: excellent (blue); good (green); worthless (red).

The ROC curve for the best possible classifier is rectangular with the vertex [0,1].

#### *4.4. Results of Neural Network Classificaton*

We configured and tested neural networks for all data representations (in total 15 experiments). For a better clarity, the experiments results are labelled using labels from Table 3.

**Table 3.** Labels of neural network experiment.

The first tests were carried out for RBF and MLP networks with input data formats according to Table 3. Resulting confusion matrices for RBF networks are as follows:

$$\begin{aligned} rrbf - rsn - sum01 &= \begin{bmatrix} 502 & 14\\ 15 & 433 \end{bmatrix},\\ rbf - rsn - lpr02 &= \begin{bmatrix} 434 & 0\\ 83 & 447 \end{bmatrix},\\ rbf - rsn - pol03 &= \begin{bmatrix} 435 & 0\\ 82 & 447 \end{bmatrix} \end{aligned} \tag{10}$$

From the matrices (10) it is evident that the RBF network performed bad when classifying NOK welds—they are often classified as OK. ROC curves of trained RBF networks are depicted in Figure 36.

**Figure 36.** *Cont.*

(**c**) ROC curve for rbf-rsn-pol03

**Figure 36.** ROC curves for experiments with RBF networks.

ROC curves for MLP networks are depicted Figure 37 and Resulting confusion matrices are as follows:

$$\begin{aligned} mlp - rsn - sum04 &= \begin{bmatrix} 516 & 1 \\ 1 & 446 \end{bmatrix} \\ mlp - rsn - lpr05 &= \begin{bmatrix} 5017 & 0 \\ 0 & 447 \end{bmatrix} \\ mlp - rsn - pol06 &= \begin{bmatrix} 514 & 1 \\ 3 & 446 \end{bmatrix} \\ mlp - krr - sum07 &= \begin{bmatrix} 517 & 15 \\ 17 & 446 \end{bmatrix} \\ mlp - krr - lpr08 &= \begin{bmatrix} 511 & 2 \\ 23 & 459 \end{bmatrix} \\ mlp - krr - pol09 &= \begin{bmatrix} 522 & 13 \\ 12 & 448 \end{bmatrix} \end{aligned} \tag{12}$$

(**a**) ROC curve for mlp-rsn-sum04 (**b**) ROC curve for mlp-rsn-hpr05

**Figure 37.** *Cont.*

**Figure 37.** ROC curves for experiments with MLP networks.

The results show that the MLP implementation in the RSNNS library was more successful compared with the Keras library. The networks had no problem to classify correct (OK) or incorrect (NOK) welds. FP and FN values were approximately similar. The resulting calculated accuracy and F-scores shown in Table 4 describe the performance of the trained neural networks.

**Table 4.** Accuracy a F-score for RBF and MLP networks.

The results show that MLP networks are much more successful. Using default RBF initialization weights the RBF network less successful. From a practical point of view, MLP networks are more suitable for weld evaluation.

It was hard to compare the results for MLP networks, they provided similar results for all data representations. The RBF network achieved significantly better results in the vector of sums of subfields in the mask data representation.

It was found out, that using the same network configuration in the two libraries yields slightly different results. The implementation in the RSNNS library was almost 100% successful and therefore it was considered as the best candidate for practical use.

Training profiling for RSNN library was done next. Although training in the Keras library allocated less memory, the training time was several times longer than in case of the RSNNS library. Using vector of sums of subfields in the mask, the MLP network training time in RSNNS took less than one second, while using the Keras library was tens of seconds. The list of training profiling results is shown in Table 5.

**Table 5.** Profiling of RBF and MLP networks training.

Comparison of convolution neural nets was again based on the confusion matrices, ROC curves, accuracy and F-scores. The input of the networks were just images of welds without any filtration and masked welds without background (black background). Confusion matrices are as follows:

$$\begin{aligned} cmn - kcr - ori10 &= \begin{bmatrix} 534 & 1 \\ 0 & 460 \end{bmatrix}' \\ cmn - mrxn - ori12 &= \begin{bmatrix} 559 & 8 \\ 1 & 431 \end{bmatrix}' \\ cmn - mrxn - ori14 &= \begin{bmatrix} 498 & 0 \\ 0 & 460 \end{bmatrix}' \\ cmn - kcr - seg11 &= \begin{bmatrix} 534 & 0 \\ 0 & 461 \end{bmatrix}' \\ cmn - mrxn - seg13 &= \begin{bmatrix} 558 & 0 \\ 2 & 439 \end{bmatrix}' \\ cmn - mrxn - sg15 &= \begin{bmatrix} 498 & 0 \\ 0 & 460 \end{bmatrix}' \end{aligned} \tag{14}$$

Classification error in convolution neural networks was minimal, therefore the ROC curve was evaluated as ideal for all experiments with indistinguishable differences. For all neural nets, the ROC curve was the same (Figure 38).

The resulting accuracy and F-scores along with the number of epochs needed to train the networks are listed in Table 6.

**Figure 38.** ROC curve for all convolution nets.

**Table 6.** Accuracy and F-scores for convolution neural network experiments.

For convolution networks, changes of accuracy after each epoch for both training (blue line) and validation data (green line) are shown in Figure 39. The charts show that training with non-segmented weld images started at a lower accuracy and the learning was slower (Figure 40).

**Figure 39.** Progress of accuracy for cnn-ker-ori10.

**Figure 40.** Progress of accuracy during epochs for cnn-ker-seg11.

The progress of training for the Keras library was more uniform, without steps. The graphs can be seen in Figures 41 and 42.

**Figure 42.** Progress of accuracy during epochs for cnn-mxn-seg15.

The success rate for all networks was higher than 99%. The decisive factor for comparison were the code profiling results shown in Table 7.

**Table 7.** Code profiling results for designed convolution neural networks.

It can be concluded, that the network with the architecture shown in Figure 29 in Section 3.3 implemented using the MXNet library was the fastest. With a training time 12.170 ms and a 100% success also for non-segmented data it is considered the best choice for practical use.

Although the MLP network (mlp-rsn-sum04) was similarly successful and several times faster in training, the preparation of the representation in the form of the vector of sums of subfields in the mask took considerably more time. The number of training samples was approximately 5400, the average time to obtain a mask of one sample was 164 ms, and the vector calculation was 16 ms, in total 972 ms.

#### *4.5. Profiling Single Weld Diagnostics*

In practice, neural network training is not a frequent process. Usually, the network is trained once and then implemented for prediction. Therefore, at the end we decided to evaluate the prediction of one weld for the most successful models. The provided results represent the average of five independent tests. The list can be seen in Table 8 along with the average image preparation time and memory required to prepare the weld input image for the specific diagnostic model.

**Table 8.** Profiling results for single weld diagnostics.

The diagnostic profiling results confirmed that the best solution was the classification of the weld using the convolution net with the architecture shown in Figure 29 in Section 3.3. The average image loading time and its 5× reduction took only 14 ms on average, and evaluation time was 14 ms.

#### **5. Discussion**

The aim of this paper was to develop a neural network based methodology to evaluate quality of welds. Several types of neural networks implemented in several software libraries were compared with respect to performance. It was necessary to prepare the data (images of welds) into a format suitable for neural network processing. For some types of networks (convolution) the input data preparation was minimal (segmentation or no segmentation), while for other networks (MLP, RBF), a sophisticated data preprocessing was required (filtering, equalizing and segmenting the image based on entropy). Each library required its own input data format which also had to be taken into account during programming. The main result of the paper is confirmation, that the convolutional neural networks can be used for weld quality evaluation without using image preprocessing and in case of using no segmentation, they can be used for evaluation not only weld metal but also adjected zones.

Neural networks were configured experimentally to achieve the best performance and the obtained results were compared. In all cases, neural networks implemented and trained using the proposed approach delivered excellent results with a success rate of nearly 100%. Thus, we can recommend any of the tested libraries to solve the weld quality evaluation problem. The best results were achieved using convolution neural networks which provided excellent results and with almost no pre-processing of image data required. The longer training time of these networks is acceptable in practical usage.

In summary, based on achieved experimental results, convolution neural networks have shown to be a promising approach for weld evaluation and will be applied in the future research dealing with evaluation of images in the real welding processes. The convolutional neural networks can be used for weld quality evaluation without using image preprocessing.

**Author Contributions:** O.H. proposed the idea in this paper and prepared data; O.H., E.K., P.D. and J.C. designed the experiments, E.K. and P.D. performed the experiments; O.H., and E.K. analyzed the data; O.H. wrote the paper; E.K., P.D. and J.C. edited and reviewed the paper; All authors read and approved the final manuscript.

**Funding:** This research was supported by the Slovak research and Development Agency under the contract no. APVV-17-0190, by the Scientific Grant Agency of the Ministry of Education, Science, Research and Sport of the Slovak Republic under the grant VEGA 1/0819/17, and by the Cultural and Educational Grant Agency of the Ministry of Education, Science, Research and Sport of the Slovak Republic, KEGA 038STU-4/2018.

**Acknowledgments:** We would like to thank to Alena Kostu ˇnová for helping with programming the implementation.

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

#### **References**

© 2019 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 (http://creativecommons.org/licenses/by/4.0/).


*2.3. Genome Analysis, Annotation, and Phylogenetic Analyses*

The completeness of the draft genome assembly was evaluated with BUSCO v3.0.2 using 1315 core genes of the Ascomycota dataset [15]. Genomic similarity comparisons between *A. resinae* and other strains were performed using orthoANI [16]. After the draft genome was analyzed, the gene locations were predicted using Maker (v2.31.8) [17]. tRNA and rRNA genes were predicted using tRNAscan (v1.4) and barrnap (v0.7, https: //github.com/tseemann/barrnap, accessed on 1 November 2018), respectively [18]. The functions of the predicted genes were annotated using Protein BLAST+ (v2.6.0), after which the annotated genes were classified according to KOG analysis [19]. Gene clusters related

to secondary metabolism were analyzed using antiSMASH Fungi v6.0 and secondary metabolite regions were identified using a "relaxed" strictness [20]. Lastly, Signal P5.0 was used to predict the presence of the signal peptide in translated products from the putative genes [21].

The internal transcribed spacer (ITS) region was selected for phylogenetic analysis of the selected fungus. The ITS region was amplified using the ITS1F (5 -CTT GGT CAT TTA GAG GAA GTA A-3 ) and LR3 (5 -CCG TGT TTC AAG ACG GG-3 ) primers. Polymerase chain reaction (PCR) was performed on a Bio-Rad MyCycler (Hercules, CA, USA) with the following protocol: initial denaturation at 95 ◦C for 5 min; 34 cycles at 95 ◦C (30 s), 55 ◦C (30 s), and 72 ◦C (30 s); final 5-min extension at 72 ◦C. DNA sequencing was carried out by Macrogen (Seoul, Korea) using the Sanger method with a 3730xl DNA analyzer (Life Technologies, Carlsbad, CA, USA). The ITS sequences were deposited in the GenBank database under accession numbers JN033458.2. The obtained ITS sequences were proofread and edited using reference sequences obtained from the GenBank database using MEGA v7.0, after which multiple alignments were conducted using MAFFT v7.130 [22,23]. The sequence alignments were manually modified when necessary. Additionally, a phylogenetic tree was constructed based on the ITS sequences of *A*. *resinae* and *Cladosporium*-like species using the "randomized accelerated maximum-likelihood" (RAxML) model coupled with the GTR+G evolution model and 1000 bootstrap replicates [24,25].


**5. Conclusions**

The results of the present study suggest that tissue composition (fat, lean, and bone), either from primal cuts or full carcass sides, and yield percentages of LMY and RCY of mature cows can be accurately predicted by CVS or DXA technologies by applying partial least square regression statistical analysis.

Although DXA results showed higher accuracy, precision, and robustness than results from CVS technologies, DXA technology is still in development for cattle and would require further design adjustments for full implementation and integration into commercial slaughter plants with moving carcass lines.

On the contrary, CVS technologies (HCC and CCC cameras) are widely implemented in North America. In the present study, predictions using HCC variables led to similar or even better results (higher R2 and lower MSPE values) than those from CCC. The implementation of HCC technology for the carcass composition estimations of mature cows has the benefit that knife ribbing of the carcasses is not required, not even for RCY (%) or LMY (%) estimations. Furthermore, the combination of both CVS technologies

led to significant improvements in the tissue predictions of primal cuts and total carcass composition, particularly for lean/fat ratios, suggesting this approach as an alternative for the enhancement of the prediction accuracy on primals and carcass composition of cull cows.

**Author Contributions:** Data curation, I.L.L.; formal analysis, J.S. and I.L.L.; funding acquisition, J.L.A.; investigation, J.S., N.P. and Ó.L.-C.; project administration, Ó.L.-C.; supervision, Ó.L.-C.; writing—original draft, J.S.; writing—review and editing, J.L.A., N.P., M.J. and Ó.L.-C. All authors have read and agreed to the published version of the manuscript.

**Funding:** This study was funded by Agriculture and Agri-Food Canada (AAFC) as part of their AgriInnovation Program Stream B, grant number AIP-P335.

**Institutional Review Board Statement:** The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the AAFC-Lacombe Research and Development Centre Animal Care committee. The code for this study was 201702.

**Acknowledgments:** The authors express their gratitude to the AAFC-Lacombe Research and Development Centre (AB, Canada) Beef Unit and Meat Centre staff for animal care and management, animal slaughter and carcass fabrication, and technical collection and compilation of the research data. José Segura Plaza gratefully acknowledges the support from the Canada Sustainable Beef and Forage Science Cluster, through funding provided by the Canadian Cattlemen's Association and Agriculture and Agri-Food Canada. The authors also sincerely and gratefully acknowledge the cooperation and assistance of the Canadian Cattlemen's Association.

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


**5. Conclusion**

140 Fossil Fuel and the Environment

Fig. 14. Sensitivity analysis of fuel cost and investment rate on the COE.

Fig. 15. Sensitivity analysis of fuel cost and investment rate on the payback period.

year as given in Table 3.

The extra cost of the PV array and WTG in the system is obtained as the difference between the system cost of the PV-wind-diesel-battery system and the diesel-battery system from Table 2 and the rate of savings per year is obtained from the savings in the cost of fuel per A model called HARPSim was developed in MATLAB Simulink to demonstrate that the integration of WTGs and PV arrays into stand-alone hybrid electric power systems using DEGs in remote arctic villages improves the overall performance of the system. Improved performance results from increasing the overall electrical efficiency, while reducing the total fuel consumption of the DEG, the energy costs, and emissions.

The LCC cost analysis and the percentage annualized cost from the Simulink® model were comparable to those predicted by HOMER. The Simulink® model calculates the CO2, NOx and the PM10 emitted to the atmosphere over the period of one year. These results can also be utilized to calculate the avoided costs of emissions.

Distributed or hybrid energy systems which result in more economical and efficient generation of electrical energy could not only improve the lifetime and reliability of the diesel-electric generation systems in remote communities, but could also help to extend the future of non-renewable energy sources.


**4. Conclusions**

In this study, 70% of the examined antimicrobials in both wastewater and sewage sludge collected from two Polish WWTPs were detected. To the best of our knowledge, this is the first report on antimicrobial occurrence in Polish WWTPs. The removal efficiency, antimicrobial resistance risk, and ecological threat (RQs) were examined according to the obtained data. The WWTPs removed ~50% of selected antimicrobials with good efficiency, above 50%. The level of antimicrobials in both untreated and treated wastewater, river as well as sewage sludge, poses a risk of resistance selection as shown by RQ calculations. Moreover, influents and river waters posed high and medium risk, particularly for cyanobacteria and eukaryotes due to the presence of ciprofloxacin, macrolides, and sulfamethoxazole. Following the obtained results, the watch list of substances for Union-wide monitoring in the field of water policy (already includes macrolides and ciprofloxacin) should be extended with sulfamethoxazole. Our study also indicates the need for evaluation of antimicrobials' concentrations not only in treated wastewater, but also in sewage sludge because of its usage in the fertilization process, which is environmentally sustainable options for re-use of the WWTP by-products. Several antimicrobials tested were present at levels that have been suggested to promote resistance development in sludge-amended soils (predicted concentrations).

The most important observation made is a possible pressure for the development of antimicrobial resistance in the WWTPs. WWTPs can be considered as potential hot spots for the dissemination of antimicrobial resistance. Leakage of antimicrobials can select for increased resistance among environmental bacteria and influence the virulence of antimicrobial-sensitive bacterial infections directly by reducing the infective dose and transmission [27,29]. Therefore, additional studies on the characterization of wastewater treatment plants' microbial communities and the profiles of antimicrobial-resistant genes are necessary. Our study also highlights the lack of sufficient data to evaluate or predict the risk of resistance development and environmental threat. In fact, data on risk assessment of wastewater and sludge in other European countries is also scarce.

**Author Contributions:** All authors contributed equally to the development of the manuscript in all aspects: Investigations and conceptualization, J.G., G.N.-J., D.K.; writing—original draft, J.G., G.N.-J., G.P.; writing—review and editing, J.G., G.N-J., E.K., M.H., G.P.; funding acquisition, G.P. and J.G. All authors have read and agreed to the published version of the manuscript.

**Funding:** This study was done under the project No 2017/26/M/NZ9/00071 funded by National Science Center, Poland. LC-MS/MS analyses were carried out with the use of the CePT infrastructure financed by the European Union, the European Regional Development Fund within the Operational Program "Innovative economy" for 2007–2013.

**Acknowledgments:** The authors would like to thank Marcin Giebułtowicz for drug sales data analysis and Ryszard Marszałek for his technical support during LC-MS/MS measurement. The authors are also very grateful to Bartosz Kózka and Bartłomiej Sankowski for their contribution in the conduct of the analytical study. We gratefully acknowledge the technical support provided by the WWTPs.

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

**Table A1.** The comparison of the predicted (PLoad) and measured load of antimicrobial into WWTP1.

1 PLoad, predicted load of a compound calculated on the basis of sales data; 2 LoadW, load of a compound calculated on the basis of measured concentration in a water phase of an

influent; 3 LoadS, load of a compound calculated on the basis of measured concentration in a primary sludge; 4 LoadW+S, total load of a compound (water phase and a primary sludge); 5 S summer; A, autumn; W, winter sampling. 6 The pharmaceuticals used only in hospitals and frequently used in veterinary medicine were not presented

**Table 2.** Mean values of sorption coe fficients (log Kd) calculated for sludge and the percent of the total mass of the compound sorbed in sludge to its total daily mass load to WWTP.

**Table 3.** Predicted antimicrobial concentration in sludge-amended soil (single sludge application).

1 WWTP-PS, sludge after mechanical treatment; WWTP-ES, sludge after gravity treatment; WWTP-FS, sludge after an open fermentation pool; WWTP-RS, sludge for management.

**Table 4.** NOEC/EC50 (μg/L) and PNEC (ng/L) used for the risk assessment; nd, no data available. Abbreviations in parentheses indicate the most sensitive taxa: MA, microalgae; MP, macrophytes; IN, invertebrates. The data based on the review of Le Page [27].

**Table 5.** Predicted no e ffect concentration values for resistance selection of microbial community in wastewater, sludge, and soil.

1 According to Bengtsson-Palme and Larsson [28]; 2 calculated based on Kd obtained for sludge; 3 calculated according to [26,40,42,62].


Stochastic versus Ray Tracing Wireless Channel Modeling for 5G and V2X Applications: Opportunities and Challenges

*Sulaiman Tariq, Hussain Al-Rizzo, Md Nazmul Hasan, Nijas Kunju and Said Abushamleh*


*Review* **Potential of New Sustainable Green Geopolymer Metal Composite (GGMC) Material as Mould Insert for Rapid Tooling (RT) in Injection Moulding Process**

**Allice Tan Mun Yin 1 , Shayfull Zamree Abd Rahim 1,2,\*, Mohd Mustafa Al Bakri Abdullah 2,3, Marcin Nabialek 4 , Abdellah El-hadj Abdellah 5 , Allan Rennie 6, Muhammad Faheem Mohd Tahir 2,3 and Aurel Mihail Titu 7**

**Abstract:** The investigation of mould inserts in the injection moulding process using metal epoxy composite (MEC) with pure metal filler particles is gaining popularity among researchers. Therefore, to attain zero emissions, the idea of recycling metal waste from industries and workshops must be investigated (waste free) because metal recycling conserves natural resources while requiring less energy to manufacture new products than virgin raw materials would. The utilisation of metal scrap for rapid tooling (RT) in the injection moulding industry is a fascinating and potentially viable approach. On the other hand, epoxy that can endure high temperatures (>220 ◦C) is challenging to find and expensive. Meanwhile, industrial scrap from coal-fired power plants can be a precursor to creating geopolymer materials with desired physical and mechanical qualities for RT applications. One intriguing attribute of geopolymer is its ability to endure temperatures up to 1000 ◦C. Nonetheless, geopolymer has a higher compressive strength of 60–80 MPa (8700–11,600 psi) than epoxy (68.95 MPa) (10,000 psi). Aside from its low cost, geopolymer offers superior resilience to harsh environments and high compressive and flexural strength. This research aims to investigate the possibility of generating a new sustainable material by integrating several types of metals in green geopolymer metal composite (GGMC) mould inserts for RT in the injection moulding process. It is necessary to examine and investigate the optimal formulation of GGMC as mould inserts for RT in the injection moulding process. With less expensive and more ecologically friendly components, the GGMC is expected to be a superior choice as a mould insert for RT. This research substantially impacts environmental preservation, cost reduction, and maintaining and sustaining the metal waste management system. As a result of the lower cost of recycled metals, sectors such as mould-making and machining will profit the most.

**Keywords:** rapid tooling; geopolymer metal composite; additive manufacturing; injection moulding process

**Citation:** Yin, A.T.M.; Rahim, S.Z.A.; Al Bakri Abdullah, M.M.; Nabialek, M.; Abdellah, A.E.-h.; Rennie, A.; Tahir, M.F.M.; Titu, A.M. Potential of New Sustainable Green Geopolymer Metal Composite (GGMC) Material as Mould Insert for Rapid Tooling (RT) in Injection Moulding Process. *Materials* **2023**, *16*, 1724. https:// doi.org/10.3390/ma16041724

Academic Editor: Andrea Sorrentino

Received: 19 November 2022 Revised: 13 February 2023 Accepted: 16 February 2023 Published: 19 February 2023

**Copyright:** © 2023 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/).

#### **1. Introduction**

Time to market is a crucial aspect of a product development strategy, and speed is frequently compared to other factors such as functionality, creativity, or performance [1–3]. With numerous new technologies, worldwide rivalry for product creation is soaring. Furthermore, companies are always looking for cutting-edge technologies that are costeffective, capable of manufacturing goods in tiny quantities while maintaining excellent performance, and able to meet sustainability goals. This has driven the development of rapid tooling (RT) techniques, which are needed in today's market to replace traditional techniques with rapid product innovation and improve manufacturing processes, particularly mould-making [4–6].

As shown in Figure 1, RT provides quicker manufacturing for completing tests and starting final production, minimises costs, and reduces project time [7].

**Figure 1.** A Review of the Application of Rapid Tooling in Manufacturing [8].

Every industry, regardless of size, experiences a time when rapid tooling is required to address particular problems. Additionally, an improved tooling system is required for creating a limited number of functional prototypes to assess the product development cycle [8–10]. A small quantity of items is often utilised as a marketplace trial, evaluation need, and manufacturing process design [9,10].

Before mass manufacturing, functioning tools or prototypes must be launched for every scientific study [11–14]. These are not made available in large quantities to consumers but rather in limited amounts to researchers. RT is highly advantageous in this circumstance since it allows for the rapid introduction of items. Furthermore, the uses of production tools allow mass production to be obtained at a lower price because manufacturing costs are cheap. For this reason, many brand-new businesses and even big organisations prefer this technology to boost their profits and obtain a market advantage over their rivals [1–3].

Prototype companies or mould producers typically employ mild steel or aluminium for the mould inserts in RT. Production toolmaking is time-consuming and costly, and machining involves the same computer numerical control (CNC), electrical discharge machining (EDM), and electric discharge machining (wire EDM) procedures [15,16]. Recently, additive manufacturing (AM) has been employed to create mould inserts for RT [13,16]. For a limited number of prototypes, RT often uses models or prototypes made by AM as templates for manufacturing mould inserts or uses the AM process directly [4–6]. Numerous RT technologies are available on the market, such as a hybrid technique combining RT and AM to shorten RT production time.

RT can be categorised as either an indirect or direct technique and differs from traditional tooling in that the amount of time needed to create the tooling is significantly reduced [17,18]. Automated manufacturing methods use the AM process to generate mould inserts without the requirement for values to be predicted. Direct tooling includes processes such as additive manufacturing (AM), stereolithography (SLA), jet photopolymerisation (PolyJet), fused deposition moulding (FDM), and selective laser sintering (SLS) [5,6,19]. Al-

ternatively, the AM project as a master model is considered a secondary approach to create moulds for casting or plastic moulding processes. This technique combines the 3D KelTool process, metal casting, plastic casting, elastic moulding, and other comparable procedures to create injection moulding inserts [5,6,15,20]. Inserts constructed from epoxy-acrylate and utilising material for injection moulding by homo-polypropylene are used for quick tooling applications for 3D-printed injection moulds [21]. The mould insert constructed from steel and copper for hybrid prototype mould applications is created using a mix of laser powder bed fusion (L-PBF) and casting. In a study, L-PBF printed the steel shell with conformal cooling channels, and the shell was cast with copper [22]. For a material jetting (PolyJet) mould, a mould insert constructed from epoxy-acrylate resin is used and its in-mould behaviour is compared to a guidance mould insert fabricated from aluminium [23].

Failure in the moulding process in generating RT mould inserts is common for technologies with poor thermal and mechanical quality, such as SLS, FDM, SLA, PolyJet, and AM [5,6,19]. Zakzewski et al. [24] presented the bucking π-theorem, which was modified to analyse and characterise the poor surface quality Ra issue of SLS/SLM-processed samples, as well as the existence of porosity, a material structure defect. Furthermore, the use of SLA models is physically restricted. These constraints can be solved by developing procedures that use SLA parts as the "master blueprint" for the silicone mould process. In comparison to mechanical techniques, the DMLS process is inefficient for the design of basic plastic components. Furthermore, DMLS imposes a few constraints for a specific feature design for complicated components. According to previous studies on RT mould inserts, the stress applied to the mould insert during the injection cycle has a significant influence on the mould life [11,15,19,20]. Nowadays, a combination of RP technique and production tooling helps produce RT more quickly but faces dimensional accuracy and surface finish issues [17,18]. Moreover, the injection moulding process faces a cooling time issue where most of the mould inserts fabricated using RP techniques have very low thermal conductivity; thus, increasing the cooling rate will undoubtedly influence the cycle time for producing the components [4,16,25,26]. One of the RT options to increase competitiveness is using metal epoxy composite (MEC), which provides greater heat conductivity as mould inserts in RT application and lowers tooling production costs and lead times by 25% and 50%, respectively [3,6]. Using optimisation methods to determine the optimal composition for materials, as recommended in the linked literature, can be considered for future research, such as determining the best amount of Al or Cu to mix with epoxy resin for desirable mechanical properties [27–37]. The use of MEC mould inserts for RT in the injection moulding process, which uses pure metal filler particles combined with epoxy resin, has attracted the attention of many researchers [20,29,38–40].

The use of MEC materials as mould inserts offers better thermal and mechanical properties as compared to mould inserts produced using AM technologies [40–43]. However, dimensional accuracy and surface quality still need to be improved, so after the fabrication of mould inserts using MEC material and casting techniques, the mould inserts need to go through a secondary process (machining) to improve mould dimensional precision and surface quality in terms of cavities, especially for precision of plastic products (±0.05 mm).

Secondary (recycled) materials compete with primary materials in the metals business. Primary materials require the use of finite resources. Producing scrap materials or processed secondary metals can sometimes be more cost-effective than producing new primary materials, provided that the cost of collecting the waste is not prohibitively expensive [42]. Due to its spherical morphology and manageable particle size dispersion, gas-atomised (GA) powder is the most frequent feedstock for AM. However, much energy and inert gas are required to make GA powders [43]. Water-atomised powder is another option when increased powder solidification rates and reduced manufacturing costs are the priorities [29,44,45]. In contrast, melting the metal before ejection from the atomisation nozzles is energy-intensive because of the significant enthalpy difference between the liquid and solid states [45]. In metal AM, the feedstock powder is often remelted. This repeated melting is a costly and inefficient process [46]. As it can reduce materials of varying

sizes to powder, mechanical milling offers a chance for environmentally friendly powder production [31,47–49]. For mechanical milling, ambient or cryogenic temperatures are typically used [49,50]. The aforementioned considerable energy input is no longer required to reach atomisation temperatures [50]. Due to these potential benefits, mechanical milling is being used to reduce metal machining chips to powders that can be used in AM [46].

On the other hand, Davidovits' geopolymer technology is one of the groundbreaking innovations resulting in an affordable and greener binder alternative. The silica and aluminium in geosource materials such as metakaolin (calcined kaolin), and maybe techniques such as fly ash and bottom ash, are combined with the alkaline liquid to generate a geopolymer, an alkali-activated binder [1,11]. As a result, it reduces not only CO2 emissions but also recycles industrial waste, specifically using an aluminium–silicate mix to create products of higher value [9,10]. MEC using pure metal filler particles is beginning to be used by some researchers to investigate mould inserts in the injection moulding process [4,5,7]. However, a type of epoxy that can withstand high temperatures (>220 ◦C) is hard to find and still costly.

Additionally, besides municipal solid waste, coal combustion production (CCP) has been identified as the second-largest pollutant in the world. In 2011, about 130 metric tonnes (MT) of CCP were generated, with only 56.57 MT (43.50%) effectively used [51].

The four forms of solid waste created in substantial amounts by the CCP are boiler slag, bottom ash, fly ash, and flue gas desulphurisation (FGD) material [40–42]. One hundred and thirty metric tonnes of CCP included around 59.9 MT of fly ash. Fly ash was disposed of in surface impoundments covered with compacted clay soil, a plastic sheet, or both for the remaining 22.9 MT (38.36%) in landfills or surface impoundments [51–53]. The Environmental Protection Agency (EPA) of the United States (US) is now investigating the positive uses of fly ash [53–57]. This eliminates major health concerns associated with heavy metals and radioactive elements accumulated from fly ash disposal over time. Geopolymers derived from environmentally friendly materials, such as slag, or industrial by-products and used as a binding material are known as "green material".

One interesting property of geopolymer is that it can withstand temperatures up to 1000 ◦C. Nevertheless, geopolymer only has a compressive strength of 60–80 MPa (8700–11,600 psi), while epoxy has a compressive strength of 68.95 MPa (10,000 psi) [29,58]. However, employing geopolymer material has similar issues to using epoxy resin, which necessitates determining the optimal strength, accuracy, acceptable surface finish, and good thermal characteristics.

Early strength of geopolymer can be obtained as early as 1 day, with compressive strength up to 15 MPa, and continues increase up to 40–50MPa within 7 days, which is comparable with the strength offered by epoxy. Nevertheless, the optimum strength of geopolymer material can be obtained by 28 days (80 MPa) and the strength will keep on increasing over time [59].

It was recognised that the filler's interlaminar strength controls the bond strength of geopolymer reinforced with filler. The fact that filler with a bigger particle size has a lower binding strength is also well known. In addition, compared to epoxy resin, geopolymer showed high bond strength for both wet and dry interface surface conditions [59].

On the other hand, as the need for an environmentally friendly society grows, the quantity of waste material must be continually decreased. Hence, in order to achieve zero emissions, the idea of recycling metal waste from factories and workshops needs to be examined (waste free) [60–63]. Metal recycling helps to conserve natural resources while requiring less energy for manufacturing new products than would be required for virgin raw materials. Waste-free recycling reduces the emission of carbon dioxide and certain other harmful gases while also saving money and enabling industrial companies to reduce their production costs [64,65].

Through a Google Patents search (https://patents.google.com/ accessed on 10 February 2023), six patents granted/published that make use of (1) metal composite and composites made of (2) geopolymer and (3) metal were located. Table 1 lists the search terms for

this review's related field, rapid tooling. Fibre-reinforced metal composites (aluminium matrix composites) were developed by Yamamoto et al. [65] using aluminium alloy with 6–11 wt. % nickel as the metal matrix and reinforcing fibres. To mass produce complex parts with near-net shapes, Behi et al. [66] proposed using steel tooling in an injection moulding machine. In comparison to more traditional methods, this approach to producing complicated metal tooling is relatively cost-effective, making it possible to rapidly fabricate complex shaped parts using normal metal, ceramic, and plastic processing. According to the metal matrix composite introduced by Shaikh et al. [67], the fibre to metal or alloy ratio ranges from about 9:1 to less than about 1:1, and the fibres have an average diameter of approximately eight micrometres with a coating. Amaya and Crounse [68] discovered rapid manufacturing of mould inserts by employing blank die inserts formed from material typically used in the metal injection moulding process of complex shaped components to achieve high machinability rates, time and cost savings, extended tool life, and material savings. The dry-mix composition, as proposed by Nematollahi and Sanjayan [69], includes (a) an aluminosilicate material rich in silica and alumina and (b) a powdered alkali activator. Moreover, the dry-mix composition is chosen so that (i) the SHGC may be generated at ambient temperature without liquid activator, and (ii) strain-hardening behaviour and multiple cracking behaviours are observed. A strain-hardened, ambient temperature-cured geopolymer composite (SHGC) is generated by adding water and using a method of manufacturing an ambient temperature-cured SHGC. Qiang et al. [70] proposed a geopolymer composite material that is a type of 3D print as well as their preparation technique and applications, which included blast furnace slag powder accounting for 20~25% of the total composition weight, steel-making slag powder accounting for 10~15%, fly ash accounting for 0~5%, mine tailing machine-made sand accounting for 33~45%, exciting composite agent accounting for 3~5%, high molecular weight polymer accounting for 2.5~3%, volume stabiliser accounting for 1~3%, thixotropic agent accounting for 1~2%, defoamer accounting for 0.05~0.1%, and mixing water accounting for 13.9~12.45%. Each component is stirred, and subsequently pumped into the 3D printer applications for construction. The present invention's geopolymer composite material demonstrates good caking property, strong stability, good go-out pump from holding capacity and adhesive property, excellent form, and volume stability, resulting in the construction of buildings with good overall stability and safety during use. The six patents granted/published from 1990 to February 2023 are listed in Table 1.

RT is a cost-effective solution in the transition phase from new product development to mass production in the manufacturing industry [71,72]. RT, often referred to as bridge tooling, prototype tooling, or soft tooling, is a fast way to preproduce hundreds or even thousands of plastic parts prior to mass production, for design optimisation, functional testing, or preproduction verification, which can be a bridge between rapid prototyping (RP) and mass production. Shape, fit, and function prototype components are frequently made using RP technology, such as additive manufacturing [71,73,74]. Recycled metal waste such as mild steel, aluminium, copper, and brass after machining processes are as shown in Figure 2.

However, since 3D material qualities vary from those used in injection moulding, 3D-printed samples cannot provide a thorough evaluation of an injection-moulded part's functional performance [18,59], making RT extremely crucial for the manufacturing industry.

*Materials***2023**, *16*, 1724

**Figure 2.** (**a**) Metal scraps from turning; (**b**) metal scraps from grinding; (**c**) metal scraps from milling.

Using fly ash (waste from coal combustion) as the raw material, the metal scraps from the machining process are ground using a ball mill machine into a small and uniform size and mixed with geopolymer material to create green geopolymer metal composite (GGMC) material as in Figure 3. Then, this material can be used as mould inserts for RT applications which is expected to reduce tooling production costs and lead periods by up to 25% and 50%, respectively. The effect of GGMC material as mould inserts for RT in an injection moulding process and its relationship with compressive strength and thermal conductivity should be examined accordingly. Therefore, this research aims to determine whether geopolymer material may be used as RT mould inserts in the injection moulding process. The process by which GMCs are used as a new material for mould inserts is depicted in Figure 4.

**Figure 3.** Planetary Mono Mill Pulverisette 6 can be used in the ball mill process.

**Figure 4.** Graphical representation of GGMC as new material for mould inserts.

A power plant is a structure that produces waste geopolymer and generates electric energy from another form of energy. The geopolymer material is then combined with filler particles (waste from machining after a ball milling process to form a powder filler). The ratio of geopolymer and powder filler is evaluated accordingly in terms of thermal conductivity and compressive strength. Next, the optimised ratio is used to fabricate the GMC mould inserts. Then, GMC mould inserts are machined accordingly to fit the insert size and assembled in the mould base. Following the examination of the GMC mould inserts, the GMC mould is assembled in the injection moulding machine to mould out the specimen for further evaluation of the mould parts' quality in terms of shrinkage and warpage, including the cooling time required, which is definitely influenced by the thermal conductivity of the GMC mould inserts. The reliability of the GMC mould inserts is evaluated accordingly in terms of the number of shots (specimens) that can be produced before the mould starts to crack or wear.

#### **2. Injection Moulding Process**

#### *2.1. Important Processing Parameters in the Injection Moulding Process*

Processing parameters are essential to produce good-quality moulded parts in the injection moulding process. Previously, the trial-and-error approach to determine processing parameters relied upon a plastic injection moulding process. However, the trial-and-error approach is ineffective for complex manufacturing processes [75–77]. Therefore, many studies had been carried out over the years to minimise shrinkage and warpage defects by optimising the processing parameters [77–80]. In addition, it has also been observed that various critical processing factors, including packing pressure, melt temperature, packing shrinkage duration, mould temperature, and cooling time, have an impact on the quality of the moulded components produced (warpage) [77,81–83].


**4. Simulation Experiments**

Based on the actual data of an aluminum plant, this section compares the control effect of the traditional feeding control method and the distributed subspace predictive control method through simulation results. The simulation includes the control effect under the condition of no interference and inaccurate feed quantity.


**Author details**

Francisco Javier Lara-Medina1,2\*, Olivia Esteban1,2, Isabel Bartolomé1,2, C. Ispa1,2, Javier Mateo1,2 and Francisco Javier Ascaso1,2,3

1 Department of Ophthalmology, Hospital Clínico Universitario "Lozano Blesa", Zaragoza, Spain

2 Aragon Health Research Institute (IIS Aragon), Zaragoza, Spain

3 School of Medicine, University of Zaragoza, Zaragoza, Spain

\*Address all correspondence to<EMAIL_ADDRESS>
© 2019 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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