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## **Meet the editor**

Dr. Eng. Ancuţa Păcurar is a lecturer at the Department of Manufacturing Engineering from the Faculty of Machine Building at the Technical University of Cluj-Napoca and is a member of the National Research Center for Innovative Rapid Manufacturing. She holds a PhD title in the Industrial Engineering field, with more than 50 articles presented at different international scientific

conferences or indexed in significant international databases or ISI, and is involved as a member within several national and international projects. Her areas of expertise are manufacturing engineering, assembly technologies, maintenance system, additive manufacturing and design for environment. She is a member of the University Association of Manufacturing Engineering, Romania.

Contents

**Preface VII**

Chapter 1 **Introductory Chapter: Applications of Design for Manufacturing and Assembly 1**

**Case Study in Yield Improvement 27**

Chapter 4 **Design and Programming of a Wire Winder Device for**

Chapter 5 **Additive Manufacturing Applied to the Design of Small Satellite Structure for Space Debris Reduction 59**

Chapter 6 **Multiple Criteria Evaluation of Assembling Buildings from Steel**

Jonathan Becedas and Andrés Caparrós

Chapter 2 **Application of Design for Manufacturing and Assembly:**

Ilesanmi Afolabi Daniyan and Khumbulani Mpofu

Chapter 3 **Application of Six Sigma in Semiconductor Manufacturing: A**

**Development of a Multifeedstock Biodiesel Processor 5**

**Extrusion Activity in the Metal-Mechanical Industry 45** Martha Roselia Contreras Valenzuela, Alejandro David Guzmán, Diana Lagunas, Gerardo Vera Dimas, Alina Martínez Oropeza, Viridiana León-Hernández, Alber Eduardo Duque Alvarez and Roy

Ancuţa Carmen Păcurar

Prashant Reddy Gangidi

López Sesenes

**Frame Structures 77** Ruta Miniotaite

## Contents

### **Preface XI**

Preface

which it has been designed.

assembling technology.

optimization of parts or subsystems.

The manufacturing of industrial products comprises the manufacturing processes of compo‐ nent parts, the assembly process of individual parts in the assembled state, and mounting the finite product right at the end, this product being required to fulfill all the functions for

The quality of the products and their competitiveness depend on the quality of the compo‐ nent parts, the efficiency of the manufacturing processes, and the right solutions for the as‐ sembly process of a newly developed product in close connection with the efficiency of the

Designing competitive products must also take into consideration the technological design (Design for Manufacturing concept), as well as the assembly design (Design for Assembly concept), and functional aspects of the new developed product at the end. Design for As‐ sembly (DFA) is the method of product design for ease of part assembly, which involves

Design for manufacturing and assembly (DFMA) is the method for process and cost optimi‐ zation of subsystems. Using the principles of DFMA, some efficiencies in assembling and

The book aims to present applicable research in the field of design, manufacturing, and as‐ sembly realized by researchers affiliated to well-known institutes. The book has a profound interdisciplinary character and is addressed to researchers, engineers, PhD students, gradu‐ ate and undergraduate students, teachers, and other readers interested in the assembly ap‐ plications. I am confident that readers will find interesting information and challenging

The book presents case studies focused on new design for special parts using the principles of DFMA, strategies that minimize defects in design and manufacturing applications, spe‐ cial devices produced to replace human activity, multiple criteria analysis to evaluate engi‐ neering solutions, and the advantages of using the additive manufacturing technology to

**Dr.Eng. Ancuţa Păcurar**

Cluj-Napoca, Romania

Technical University of Cluj-Napoca

Lecturer

manufacturing are obtained and the assembly operations are minimized.

design the next generation of complex parts, in different engineering fields.

topics of high academic and scientific level within this book.

## Preface

The manufacturing of industrial products comprises the manufacturing processes of compo‐ nent parts, the assembly process of individual parts in the assembled state, and mounting the finite product right at the end, this product being required to fulfill all the functions for which it has been designed.

The quality of the products and their competitiveness depend on the quality of the compo‐ nent parts, the efficiency of the manufacturing processes, and the right solutions for the as‐ sembly process of a newly developed product in close connection with the efficiency of the assembling technology.

Designing competitive products must also take into consideration the technological design (Design for Manufacturing concept), as well as the assembly design (Design for Assembly concept), and functional aspects of the new developed product at the end. Design for As‐ sembly (DFA) is the method of product design for ease of part assembly, which involves optimization of parts or subsystems.

Design for manufacturing and assembly (DFMA) is the method for process and cost optimi‐ zation of subsystems. Using the principles of DFMA, some efficiencies in assembling and manufacturing are obtained and the assembly operations are minimized.

The book aims to present applicable research in the field of design, manufacturing, and as‐ sembly realized by researchers affiliated to well-known institutes. The book has a profound interdisciplinary character and is addressed to researchers, engineers, PhD students, gradu‐ ate and undergraduate students, teachers, and other readers interested in the assembly ap‐ plications. I am confident that readers will find interesting information and challenging topics of high academic and scientific level within this book.

The book presents case studies focused on new design for special parts using the principles of DFMA, strategies that minimize defects in design and manufacturing applications, spe‐ cial devices produced to replace human activity, multiple criteria analysis to evaluate engi‐ neering solutions, and the advantages of using the additive manufacturing technology to design the next generation of complex parts, in different engineering fields.

> **Dr.Eng. Ancuţa Păcurar** Lecturer Technical University of Cluj-Napoca Cluj-Napoca, Romania

**Chapter 1**

**Provisional chapter**

**Introductory Chapter: Applications of Design for**

**Introductory Chapter: Applications of Design for**

DOI: 10.5772/intechopen.8254

The occurrence and development of new and highly productive technological procedures lead to the need of enhancing the efficiency of assembly procedures as well as of material and

The manufacturing of industrial products involves processing the component parts, as well as assembling the individual parts in subassemblies and then assembling the final product that

> © 2016 The Author(s). Licensee InTech. 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.

© 2018 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.

**Manufacturing and Assembly**

**Manufacturing and Assembly**

Additional information is available at the end of the chapter

Additional information is available at the end of the chapter

part manipulation processes (object of the so-called logistics).

**Figure 1.** Place of the assembling in the manufacturing process.

must fulfill all the functions for which it has been designed (**Figure 1**).

Ancuţa Carmen Păcurar

Ancuţa Carmen Păcurar

http://dx.doi.org/10.5772/

**1. Introduction**

#### **Introductory Chapter: Applications of Design for Manufacturing and Assembly Introductory Chapter: Applications of Design for Manufacturing and Assembly**

DOI: 10.5772/intechopen.8254

#### Ancuţa Carmen Păcurar Ancuţa Carmen Păcurar

Additional information is available at the end of the chapter Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/

**1. Introduction**

The occurrence and development of new and highly productive technological procedures lead to the need of enhancing the efficiency of assembly procedures as well as of material and part manipulation processes (object of the so-called logistics).

The manufacturing of industrial products involves processing the component parts, as well as assembling the individual parts in subassemblies and then assembling the final product that must fulfill all the functions for which it has been designed (**Figure 1**).

© 2016 The Author(s). Licensee InTech. 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. © 2018 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.

Besides the functional characteristics of new products, the competitive design must take into account the principles of the design for manufacture (DFM), as well as the principles of the design for assembly (DFA).

materials that are easy to process, avoidance of secondary operations, minimization of the

Introductory Chapter: Applications of Design for Manufacturing and Assembly

http://dx.doi.org/10.5772/

3

The main principles that must be taken into consideration when designing for manufacturing are the selection of the adequate material; designed shape which ensures the selection of performance cutting tools with low cost; and selection of suitable tolerances for product design, which determine the type of the manufacturing. It is necessary to take into account the mechanical properties of the raw material, to be able to select the right material for the considered product. Due to the fact that the manufacturing time has a significant influence on the final cost of the part, cutting tools must be selected with the appropriate stiffness and strength, which provides a processing time as low as possible. The manufacturing cost is also influenced by the cost of the cutting tools. Therefore, the product must be designed to avoid using specialized, complex cutting tools [3].

By using these principles together with computer-aided design (CAD), computer-aided manufacturing (CAM), and finite element analysis (FEA), designers must find alternatives for the considered project, to obtain a design which assures a minimal machining time and a low-cost

Design for assembly (DFA) is the method of product design for ease of part assembly which involves optimization of parts or subsystems. The use of DFA allows a considerable reduction of the manufacturing costs, assembly time, and number of component parts, as well as increasing the productivity. Under such circumstances, it is possible to establish optimal assembly methods. Of course, the innovative contribution of experts is needed when using DFA software. When establishing the sequence of assembly procedures and selecting the

**5.** *Optimizing the maneuverability of components (Whenever possible, stiff components are preferred instead of the flexible ones, because their manipulation is easier. Adequate surfaces are designed for*

**8.** *Leading to modular design (Usage of standardized modules with the same functional requirements and standard interfaces that facilitate the interchange capabilities. Such an approach facilitates*

*many options, quick design updates, as well as easier testing and servicing.)*

manufacturing operations, and elimination of the intricate shapes [2].

appropriate equipment, the software users must use their expertise.

**1.** *Reducing the number of parts by combining or removing some of them*

The main characteristics of DFA are listed below:

**4.** *Increasing the number of symmetric components*

**7.** *Providing parts having integral self-locking characteristics*

**2.** *Improving the assembling accessibility* **3.** *Improving the flexibility of components*

**6.** *Avoiding separate gripping elements*

*mechanical gripping.)*

manufacturing.

The assembly process comprises the mounting procedures of tools, control devices, and jigs needed to perform the assembling operations. In view of establishing the optimal assembly method, as well as of its differentiation degree, the main factors that influence the assembly process must be analyzed (production batch, constructive characteristics of the product, and available manufacturing equipment).

In order to succeed, the researchers must design products in a right manner for optimal manufacturing, assembly, cost, quality, time, and functionality. Design for manufacturing and assembly (DFMA) is a methodology for product development or product improvement projects in which designers and manufacturing engineers work together instead of working separately. The two groups design the product's manufacturing and assembling processes; at the same time, they design the product itself. There are many CAD—integrated design for manufacturing programs—which help to identify and correct downstream issues in the design stage, leading to the reduction of the cycle time and the product development costs.

The main role of the mechanical engineer in the continuous industrial development is to be a creator by applying his/her knowledge and expertise to solve technical problems and to optimize the manufacturing processes.

The best performance in the field of mechanical manufacturing is obtained by "optimal operations" and "optimal processes." Therefore, the goal is to optimize the manufacturing procedures and their economic impact while making a product. This goal can be achieved by minimizing the manufacturing time. Within a company, certain types of cutting tools and machines characterized by certain technological parameters are used; therefore, to minimize the manufacturing time of a product, it is needed to optimize the machining method.

It is important to realize a design for manufacturing and assembly (DFMA) of the product which must provide an easy machining and assembling and also decrease manufacturing costs without having a negative impact on the quality requirements. It is important to know the fact that about 70% of the manufacturing costs of a product are determined by design decisions [1].

While product specification and costumer's requirements are important, there are other design issues to consider. The principles of design for manufacturing (DFM) are applied throughout the design process, and these affect all aspects from the design phase to the production phase. By applying DFM methods, the manufacturing costs can be estimated, while the costs of components and supporting production can be reduced.

The design for manufacturing system is a group of design principles that are structured to help the designer to reduce the costs and the manufacturing difficulties. The following rules can be mentioned: use of standard materials and components, standardized design, liberal tolerances, materials that are easy to process, avoidance of secondary operations, minimization of the manufacturing operations, and elimination of the intricate shapes [2].

The main principles that must be taken into consideration when designing for manufacturing are the selection of the adequate material; designed shape which ensures the selection of performance cutting tools with low cost; and selection of suitable tolerances for product design, which determine the type of the manufacturing. It is necessary to take into account the mechanical properties of the raw material, to be able to select the right material for the considered product. Due to the fact that the manufacturing time has a significant influence on the final cost of the part, cutting tools must be selected with the appropriate stiffness and strength, which provides a processing time as low as possible. The manufacturing cost is also influenced by the cost of the cutting tools. Therefore, the product must be designed to avoid using specialized, complex cutting tools [3].

By using these principles together with computer-aided design (CAD), computer-aided manufacturing (CAM), and finite element analysis (FEA), designers must find alternatives for the considered project, to obtain a design which assures a minimal machining time and a low-cost manufacturing.

Design for assembly (DFA) is the method of product design for ease of part assembly which involves optimization of parts or subsystems. The use of DFA allows a considerable reduction of the manufacturing costs, assembly time, and number of component parts, as well as increasing the productivity. Under such circumstances, it is possible to establish optimal assembly methods. Of course, the innovative contribution of experts is needed when using DFA software. When establishing the sequence of assembly procedures and selecting the appropriate equipment, the software users must use their expertise.

The main characteristics of DFA are listed below:

Besides the functional characteristics of new products, the competitive design must take into account the principles of the design for manufacture (DFM), as well as the principles of the

The assembly process comprises the mounting procedures of tools, control devices, and jigs needed to perform the assembling operations. In view of establishing the optimal assembly method, as well as of its differentiation degree, the main factors that influence the assembly process must be analyzed (production batch, constructive characteristics of the product, and

In order to succeed, the researchers must design products in a right manner for optimal manufacturing, assembly, cost, quality, time, and functionality. Design for manufacturing and assembly (DFMA) is a methodology for product development or product improvement projects in which designers and manufacturing engineers work together instead of working separately. The two groups design the product's manufacturing and assembling processes; at the same time, they design the product itself. There are many CAD—integrated design for manufacturing programs—which help to identify and correct downstream issues in the design stage, lead-

The main role of the mechanical engineer in the continuous industrial development is to be a creator by applying his/her knowledge and expertise to solve technical problems and to

The best performance in the field of mechanical manufacturing is obtained by "optimal operations" and "optimal processes." Therefore, the goal is to optimize the manufacturing procedures and their economic impact while making a product. This goal can be achieved by minimizing the manufacturing time. Within a company, certain types of cutting tools and machines characterized by certain technological parameters are used; therefore, to minimize the manufacturing

It is important to realize a design for manufacturing and assembly (DFMA) of the product which must provide an easy machining and assembling and also decrease manufacturing costs without having a negative impact on the quality requirements. It is important to know the fact that about 70% of the manufacturing costs of a product are determined by design

While product specification and costumer's requirements are important, there are other design issues to consider. The principles of design for manufacturing (DFM) are applied throughout the design process, and these affect all aspects from the design phase to the production phase. By applying DFM methods, the manufacturing costs can be estimated, while the costs of com-

The design for manufacturing system is a group of design principles that are structured to help the designer to reduce the costs and the manufacturing difficulties. The following rules can be mentioned: use of standard materials and components, standardized design, liberal tolerances,

ing to the reduction of the cycle time and the product development costs.

time of a product, it is needed to optimize the machining method.

ponents and supporting production can be reduced.

design for assembly (DFA).

available manufacturing equipment).

2 Applications of Design for Manufacturing and Assembly

optimize the manufacturing processes.

decisions [1].

## **Author details**

Ancuţa Carmen Păcurar

Address all correspondence to<EMAIL_ADDRESS>Technical University of Cluj-Napoca, Cluj-Napoca, Romania

## **References**

**Chapter 2**

**Provisional chapter**

**Application of Design for Manufacturing and**

**Application of Design for Manufacturing and**

Ilesanmi Afolabi Daniyan and Khumbulani Mpofu

Ilesanmi Afolabi Daniyan and Khumbulani Mpofu

Additional information is available at the end of the chapter

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/intechopen.80085

**Processor**

**Abstract**

cost optimization

**1. Introduction**

**Processor**

**Assembly: Development of a Multifeedstock Biodiesel**

**Assembly: Development of a Multifeedstock Biodiesel**

Design for manufacturing and assembly (DFMA) is the method for process and cost optimization of subsystems, whole system as well as the entire manufacturing process. While minimizing assembly operations, it helps in eliminating component redundancy, facilitates assembly and manufacturing of products that are cost effective in terms of material requirements, parts production, labor and overhead. In this study, a multi feedstock biodiesel processor with intelligent systems for control and monitoring was developed using the principles of design for manufacturing and assembly. It consists of a 2 kW variable speed sparkless electric motor, reaction chamber, a thermostatically controlled 3 kW electric heater, saw dust insulation, ball valve, thermostat, funnel, a stirrer of diameter 20 mm with five blades that rotate in the reaction chamber and two baffles to control the splashing. The stirrer is driven by the electric motor. A 2 mm thick galvanized steel was used in the fabrication of the reaction chamber because of its high resistance to corrosion. This work provides a design framework for both small and large scale biodiesel plant for industrial, laboratory and experimental purposes. In addition, the assembly operations of the processor's components via the principles of DFMA were simplified to reduce ambiguity and redundancy. Hence, the overall processor is cost

effective in terms of material requirements, parts production, labor and overhead. **Keywords:** design, biodiesel, manufacturing and assembly, multi feedstock,

Design for manufacturing and assembly is a combined method developed to evaluate product assembly in order to enhance simplicity and cost effectiveness in manufacturing during

> © 2016 The Author(s). Licensee InTech. 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.

© 2018 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.

DOI: 10.5772/intechopen.80085

#### **Application of Design for Manufacturing and Assembly: Development of a Multifeedstock Biodiesel Processor Application of Design for Manufacturing and Assembly: Development of a Multifeedstock Biodiesel Processor**

DOI: 10.5772/intechopen.80085

Ilesanmi Afolabi Daniyan and Khumbulani Mpofu Ilesanmi Afolabi Daniyan and Khumbulani Mpofu

Additional information is available at the end of the chapter Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/intechopen.80085

#### **Abstract**

**Author details**

**References**

Ancuţa Carmen Păcurar

4 Applications of Design for Manufacturing and Assembly

Address all correspondence to<EMAIL_ADDRESS>Technical University of Cluj-Napoca, Cluj-Napoca, Romania

[1] http://www.unm.edu/~bgreen/ME101/dfm.pdf

[2] http://www.slideshare.net/NIELITA/dfm-design-principles

[3] https://www.mfg.com/sites/default/files/files/DFM%20CNC%20Machining.pdf

Design for manufacturing and assembly (DFMA) is the method for process and cost optimization of subsystems, whole system as well as the entire manufacturing process. While minimizing assembly operations, it helps in eliminating component redundancy, facilitates assembly and manufacturing of products that are cost effective in terms of material requirements, parts production, labor and overhead. In this study, a multi feedstock biodiesel processor with intelligent systems for control and monitoring was developed using the principles of design for manufacturing and assembly. It consists of a 2 kW variable speed sparkless electric motor, reaction chamber, a thermostatically controlled 3 kW electric heater, saw dust insulation, ball valve, thermostat, funnel, a stirrer of diameter 20 mm with five blades that rotate in the reaction chamber and two baffles to control the splashing. The stirrer is driven by the electric motor. A 2 mm thick galvanized steel was used in the fabrication of the reaction chamber because of its high resistance to corrosion. This work provides a design framework for both small and large scale biodiesel plant for industrial, laboratory and experimental purposes. In addition, the assembly operations of the processor's components via the principles of DFMA were simplified to reduce ambiguity and redundancy. Hence, the overall processor is cost effective in terms of material requirements, parts production, labor and overhead.

**Keywords:** design, biodiesel, manufacturing and assembly, multi feedstock, cost optimization

## **1. Introduction**

Design for manufacturing and assembly is a combined method developed to evaluate product assembly in order to enhance simplicity and cost effectiveness in manufacturing during

© 2016 The Author(s). Licensee InTech. 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. © 2018 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.

the product development. It is a two part analysis of product development namely; design for assembly (DFA) and design for manufacturing (DFM), with the aim to increase flexibility and profitability [1, 2], reduce overall cost in terms of material, production, labor and overhead [3, 4], as well as to reduce the complexity of manufacturing process [5]. A complex process increases manufacturing time, cost and rigidity. Design for assembly (DFA) is the method of product design for ease of part assembly which involves optimization of part or subsystem. According to Strienstra [6], DFA is a tool used in the design of product parts or subsystems that will transit into production at an effective cost, focusing on the number of parts, handling and ease of assembly. The aim is to minimize part lists without sacrificing quality, reduce assembly cost and to select the optimum cost of material that will be employed in manufacturing. On the other hand, design for manufacturing (DFM) is a method of design for ease of manufacturing via optimization of the process of product integration into the final product [7]. The process of optimization presents several optimum solutions for the manufacturing process hence DFM select the best solution out of the possible solutions. This reduces overall production cost as well as the complexity of the process of product development without compromising the standard. The process of product development starts with the conceptual design stage to design for assembly from where it transits to design for manufacturing and detailed design of the product. Depending on the orientation and geometry of different component parts, manufacturing processes are utilized as a single approach or in combination. This may include forming or machining processes, etc. Besides designing a component to function and fit for the intended application, it is important to put manufacturing processes into consideration. This proactive step is known as Design for Manufacture and Assembly (DFMA). It reduces the product development cycle while helping in ensuring that rework and time wasting activities are eliminated as parts are produced in the most judicious and economic way. According to [2, 8] other advantages of DFMA includes; correct interpretation of design information, optimization of design and manufacturing methods into one single step, increase in productivity with attendant decrease in production cost as well as high degree of precision in design and manufacturing. The basic principle for DFA involves the minimization of part list via the design for parts with self-locating and fastening ability. This makes the design simple as the use of locators and fasteners are reduced. In addition, parts are designed for ease of handling, insertion and retrieval. This increases design flexibility and minimizes the re-orientation of parts during assembly. Modular design of parts and part assembly from top to bottom is an effective DFA concept that can be employed to optimize the design phase. According to Whitney [9], product assembly can be done via manual, fully automated or semi-automated means. While manual assembly relies on human effort for assembly operations, automatic assembly uses robotic technology and computer controlled systems [3]. Semi-automatic relies partly on both human effort and intelligent systems for assembly operations. The method of assembly however depends on the nature of product, product complexity, environment, manufacturing structure, the efficiency required amongst other factors. Manual method of assembly has the merit of flexibility, cost effectiveness and mostly suitable for simple products with short assembly cycle. The demerits however lies in the low speed of assembly which increases assembly time, poor handling, and lower efficiency due to stress and fatigue on the part of personnel when compared to automatic means and most times not suitable for assembly of complex geometry.

Robots are programmable machines which receives signals from the system and environment to carry out programmed activities autonomously or semi autonomously [10]. They take decisions and interact with other interface as well as the central control system via the sensors and actuators. Robots combine the techniques of numerical control and remote control to replace human personnel with numerically controlled mechanical actuators. Robots are classified into two categories; artificially intelligent robots and non-artificially intelligent robots. Artificially intelligent robots are robots which are controlled by artificial intelligent programs involving learning, perception, problem-solving, language-understanding and logical reasoning to perform complex tasks while non-artificially intelligent robots simply carry out a defined sequence of instructions without the use of artificial intelligent programs. Robots possesses good material handling ability and suitable for assembly of complex products at higher efficiency and reduced assembly time. The use of robots for assembly is costly and

Application of Design for Manufacturing and Assembly: Development of a Multifeedstock…

http://dx.doi.org/10.5772/intechopen.80085

7

The emerging technology in product assembly involves the use of machines operated and controlled by computer programs. There are limitations in the rate of production using manual or robotic means because time is wasted during assembly. The computer controlled system uses some codes of instruction consisting of numbers, letters of the alphabets and symbols. These instructions are converted into electrical pulses which the machine's controls follow to carry out the assembly operations and the process is automated with the help of micro-controllers. They are costly, but highly efficient and can carry out any type of assembly operation with high degree of precision and accuracy within a short time. The pace of assembly delivery with high precision will offset the high initial cost. This work applies the design principles for design for manufacturing and assembly in the development of a multi-feedstock biodiesel processor limited to the production of 50 L of biodiesel per day. Several researchers have worked on the development of a biodiesel processor. For instance, Leevijit et al. [11] developed a continuous stirred tank reactor for the transesterification of palm oil to biodiesel. Process simulation was performed to optimize the mixing performance of the continuous reactor and the required time for the transesterification of palm oil in the optimized reactor was predicted as 5 h. However, the analysis of plant design was not sufficiently highlighted. Marjanovic et al. [12] also developed a batch reactor for the production of biodiesel from divers' feedstock but the analysis of integration of plant components was a missing link. Bello et al. [13] developed a batch biodiesel processor that can transesterify used and unused oil to biodiesel but there are some redundant complex geometries which increases manufacturing time, cost and rigidity. Musa [14] designed and constructed a pilot plant for the production of biodiesel from cottonseed. The capacity of the plant is limited to 20 L of biodiesel per day. The biodiesel pilot plant consists of a transesterification reactor with heater, a stirrer, chemical mixing tank, three glycerol settling tanks, and washing tank. However, the analysis of control circuitry and effect of process parameters were not studied. Bhachu et al. [15] designed a mobile biodiesel production plant, which is capable of producing 3000 L of biodiesel per week. The limitation lies in the fact that there are many redundant parts that increased the overall cost of manufacturing. Also, Highina et al. [16] reported on biodiesel production using *Jatropha curcas* oil in a batch reactor in the presence of zinc oxide as catalyst. The reactor is limited to single feed and use of heterogeneous catalyst only. Although, heterogeneous catalysts boast of several advantages over homogeneous catalysts in the conversion of feedstock to biodiesel [17–20]. The analysis of material handling was however not sufficiently highlighted. Hashem et al. [21] developed a

some robots are not flexible enough when compared to manual method.

Robots are programmable machines which receives signals from the system and environment to carry out programmed activities autonomously or semi autonomously [10]. They take decisions and interact with other interface as well as the central control system via the sensors and actuators. Robots combine the techniques of numerical control and remote control to replace human personnel with numerically controlled mechanical actuators. Robots are classified into two categories; artificially intelligent robots and non-artificially intelligent robots. Artificially intelligent robots are robots which are controlled by artificial intelligent programs involving learning, perception, problem-solving, language-understanding and logical reasoning to perform complex tasks while non-artificially intelligent robots simply carry out a defined sequence of instructions without the use of artificial intelligent programs. Robots possesses good material handling ability and suitable for assembly of complex products at higher efficiency and reduced assembly time. The use of robots for assembly is costly and some robots are not flexible enough when compared to manual method.

the product development. It is a two part analysis of product development namely; design for assembly (DFA) and design for manufacturing (DFM), with the aim to increase flexibility and profitability [1, 2], reduce overall cost in terms of material, production, labor and overhead [3, 4], as well as to reduce the complexity of manufacturing process [5]. A complex process increases manufacturing time, cost and rigidity. Design for assembly (DFA) is the method of product design for ease of part assembly which involves optimization of part or subsystem. According to Strienstra [6], DFA is a tool used in the design of product parts or subsystems that will transit into production at an effective cost, focusing on the number of parts, handling and ease of assembly. The aim is to minimize part lists without sacrificing quality, reduce assembly cost and to select the optimum cost of material that will be employed in manufacturing. On the other hand, design for manufacturing (DFM) is a method of design for ease of manufacturing via optimization of the process of product integration into the final product [7]. The process of optimization presents several optimum solutions for the manufacturing process hence DFM select the best solution out of the possible solutions. This reduces overall production cost as well as the complexity of the process of product development without compromising the standard. The process of product development starts with the conceptual design stage to design for assembly from where it transits to design for manufacturing and detailed design of the product. Depending on the orientation and geometry of different component parts, manufacturing processes are utilized as a single approach or in combination. This may include forming or machining processes, etc. Besides designing a component to function and fit for the intended application, it is important to put manufacturing processes into consideration. This proactive step is known as Design for Manufacture and Assembly (DFMA). It reduces the product development cycle while helping in ensuring that rework and time wasting activities are eliminated as parts are produced in the most judicious and economic way. According to [2, 8] other advantages of DFMA includes; correct interpretation of design information, optimization of design and manufacturing methods into one single step, increase in productivity with attendant decrease in production cost as well as high degree of precision in design and manufacturing. The basic principle for DFA involves the minimization of part list via the design for parts with self-locating and fastening ability. This makes the design simple as the use of locators and fasteners are reduced. In addition, parts are designed for ease of handling, insertion and retrieval. This increases design flexibility and minimizes the re-orientation of parts during assembly. Modular design of parts and part assembly from top to bottom is an effective DFA concept that can be employed to optimize the design phase. According to Whitney [9], product assembly can be done via manual, fully automated or semi-automated means. While manual assembly relies on human effort for assembly operations, automatic assembly uses robotic technology and computer controlled systems [3]. Semi-automatic relies partly on both human effort and intelligent systems for assembly operations. The method of assembly however depends on the nature of product, product complexity, environment, manufacturing structure, the efficiency required amongst other factors. Manual method of assembly has the merit of flexibility, cost effectiveness and mostly suitable for simple products with short assembly cycle. The demerits however lies in the low speed of assembly which increases assembly time, poor handling, and lower efficiency due to stress and fatigue on the part of personnel when compared to automatic means and most times not suitable for assembly of complex

6 Applications of Design for Manufacturing and Assembly

geometry.

The emerging technology in product assembly involves the use of machines operated and controlled by computer programs. There are limitations in the rate of production using manual or robotic means because time is wasted during assembly. The computer controlled system uses some codes of instruction consisting of numbers, letters of the alphabets and symbols. These instructions are converted into electrical pulses which the machine's controls follow to carry out the assembly operations and the process is automated with the help of micro-controllers. They are costly, but highly efficient and can carry out any type of assembly operation with high degree of precision and accuracy within a short time. The pace of assembly delivery with high precision will offset the high initial cost. This work applies the design principles for design for manufacturing and assembly in the development of a multi-feedstock biodiesel processor limited to the production of 50 L of biodiesel per day. Several researchers have worked on the development of a biodiesel processor. For instance, Leevijit et al. [11] developed a continuous stirred tank reactor for the transesterification of palm oil to biodiesel. Process simulation was performed to optimize the mixing performance of the continuous reactor and the required time for the transesterification of palm oil in the optimized reactor was predicted as 5 h. However, the analysis of plant design was not sufficiently highlighted. Marjanovic et al. [12] also developed a batch reactor for the production of biodiesel from divers' feedstock but the analysis of integration of plant components was a missing link. Bello et al. [13] developed a batch biodiesel processor that can transesterify used and unused oil to biodiesel but there are some redundant complex geometries which increases manufacturing time, cost and rigidity. Musa [14] designed and constructed a pilot plant for the production of biodiesel from cottonseed. The capacity of the plant is limited to 20 L of biodiesel per day. The biodiesel pilot plant consists of a transesterification reactor with heater, a stirrer, chemical mixing tank, three glycerol settling tanks, and washing tank. However, the analysis of control circuitry and effect of process parameters were not studied. Bhachu et al. [15] designed a mobile biodiesel production plant, which is capable of producing 3000 L of biodiesel per week. The limitation lies in the fact that there are many redundant parts that increased the overall cost of manufacturing. Also, Highina et al. [16] reported on biodiesel production using *Jatropha curcas* oil in a batch reactor in the presence of zinc oxide as catalyst. The reactor is limited to single feed and use of heterogeneous catalyst only. Although, heterogeneous catalysts boast of several advantages over homogeneous catalysts in the conversion of feedstock to biodiesel [17–20]. The analysis of material handling was however not sufficiently highlighted. Hashem et al. [21] developed a bench top automated system that simulates an existing processor design with modifications necessary to make it run by computer control. The processor is adaptable to test alternatives and novel processing techniques in the laboratory on the bench top scale. The automation was done using a program controller integrated data acquisition software and Labview software to monitor and control the operation. The prototype developed is useful in simulating full scale automated system and in testing new processing technologies. The specific goals of the design are to minimize manual labor and cost while ensuring plant flexibility in processing multi feedstock. The aim of the work is to develop a multi-feedstock biodiesel processor with a capacity of 50 L per day using the principles of design for manufacturing and assembly (DFMA).

The objectives of the work are to:

## **2. Methodology**

This work uses the principles of design for manufacturing and assembly for development of a smart biodiesel processor. The design considerations are as follow;

**i.** The number of parts and number of interfaces which determines the simplicity or complexity of the overall assembly. The parts are listed in order of assembly and are assigned numbers to identify the parts. The complexity factor is expressed as Eq. (1).

$$CF = \left(\sum N\_p \times \sum N\_i\right)^{0.5} \tag{1}$$

**iv.** The method of joining the different parts together. This is followed by evaluation of the

Application of Design for Manufacturing and Assembly: Development of a Multifeedstock…

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**v.** The determination of the right drafting and modeling tools in transforming conceptual-

**a.** Significant reduction in part list and short assembly cycle with parts designed for ease of

**b.** Provision of a smart processor that is flexible and versatile enough to process biodiesel from different feedstock via incorporation of all feedstock requirements for the produc-

The Arduino Uno is a microcontroller board that provides a simple and modular way of interfacing the real world with the computer to handle basic processing tasks on a chip while working with hardware sensors (**Figure 1**). The Arduino Uno uses the ATmega328 chip that supports

14 digital pins that can be configured as either input or output and 6 analog inputs [22].

assembly and readjustment.

handling, insertion and retrieval.

**2.1. Design novelties**

tion of biodiesel.

*2.1.1. Arduino Uno microcontroller*

ized design into reality with parts specifications.

The design novelties of the developed processor are as follow:

**c.** Provision of suitable template for scaling its development; and **d.** Incorporation of intelligent systems for control and monitoring.

**Figure 1.** The monitoring system circuit with Arduino Uno microcontroller.

The intelligent systems for control and monitoring comprises of the following;

where ∑*Np* is the total number of part list and ∑*Ni* is the total number of part-to-part interface.

**ii.** The practical and theoretical minimum parts as well standard parts with their cost. Parts of different materials are identified and functional analysis is carried out with respect to existing design and possibility of redesign. This is a crucial consideration because the aim of product development is for profitability and to come up with reliable products that will meet customers' requirement by performing satisfactorily in service. Hence, the right balance must be struck between cost and component parts without sacrificing quality.

The theoretical part list efficiency is expressed by Eq. (2).

$$\frac{T\_n}{T\_n} \times 100\% \tag{2}$$

where *Tm* is the theoretical minimum number of parts and *Tn* is the total number of parts.

**iii.** Nature of the parts to be assembled. This determines the method of handling, insertion an alignment.

#### **2.1. Design novelties**

bench top automated system that simulates an existing processor design with modifications necessary to make it run by computer control. The processor is adaptable to test alternatives and novel processing techniques in the laboratory on the bench top scale. The automation was done using a program controller integrated data acquisition software and Labview software to monitor and control the operation. The prototype developed is useful in simulating full scale automated system and in testing new processing technologies. The specific goals of the design are to minimize manual labor and cost while ensuring plant flexibility in processing multi feedstock. The aim of the work is to develop a multi-feedstock biodiesel processor with a capacity of 50 L per day

**i.** design a smart multi-feedstock biodiesel processor with a capacity of 50 L per day with

**ii.** consider existing processors and reduce the overall manufacturing cost via redesign and

This work uses the principles of design for manufacturing and assembly for development of

**i.** The number of parts and number of interfaces which determines the simplicity or complexity of the overall assembly. The parts are listed in order of assembly and are assigned

**ii.** The practical and theoretical minimum parts as well standard parts with their cost. Parts of different materials are identified and functional analysis is carried out with respect to existing design and possibility of redesign. This is a crucial consideration because the aim of product development is for profitability and to come up with reliable products that will meet customers' requirement by performing satisfactorily in service. Hence, the right balance must be struck between cost and component parts without sacrificing quality.

0.5 (1)

is the total number of part-to-part interface.

× 100% (2)

is the total number of parts.

numbers to identify the parts. The complexity factor is expressed as Eq. (1).

*Tn*

**iii.** Nature of the parts to be assembled. This determines the method of handling, insertion

using the principles of design for manufacturing and assembly (DFMA).

The objectives of the work are to:

reduction in the part list;

**2. Methodology**

where ∑*Np*

elimination of redundant parts;

8 Applications of Design for Manufacturing and Assembly

**iii.** increase the versatility and flexibility of the processor.

*CF* = (∑*Np* × ∑*Ni*)

is the total number of part list and ∑*Ni*

The theoretical part list efficiency is expressed by Eq. (2).

where *Tm* is the theoretical minimum number of parts and *Tn*

*<sup>T</sup>*\_\_\_*<sup>m</sup>*

an alignment.

a smart biodiesel processor. The design considerations are as follow;

The design novelties of the developed processor are as follow:

The intelligent systems for control and monitoring comprises of the following;

#### *2.1.1. Arduino Uno microcontroller*

The Arduino Uno is a microcontroller board that provides a simple and modular way of interfacing the real world with the computer to handle basic processing tasks on a chip while working with hardware sensors (**Figure 1**). The Arduino Uno uses the ATmega328 chip that supports 14 digital pins that can be configured as either input or output and 6 analog inputs [22].

**Figure 1.** The monitoring system circuit with Arduino Uno microcontroller.

The technical specification of the Arduino Uno is presented in **Table 1**.

The Arduino is programmed using the Arduino IDE with source code will be written in C.

It consists of an elastic material that deforms under the application of pressure and an electri-

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This is a kit that measures pH of a substance. It is specially designed for the Arduino and has an accuracy of ±0.1 pH (at 25°C). The kit has a range of 0–14 pH. The kit consists of a pH sensor probe, a BNC connector and a pH 2.0 interface. The pH meter kit is shown in **Figure 3**.

This takes temperature readings for the plant to aid process insights. It has a temperature

The processor design involves the determination of a detailed, industrial-scale engineering model for a biodiesel processor and the associated parametric specification. The components parts are linked up with the aim of integrating and optimizing the entire process in the conversion of oil to biodiesel. The design involves the determination of the reactor geometry and capacity as well as the choice of construction material. The first design consideration is the reactor capacity which is determined by the mass balance of input and output streams. The sum of the volume of all streams entering the reactor gives the total liquid volume of the reactor for one batch. In order to

The processor part list as well as the materials employed in the construction of a biodiesel

meet an annual production of 18,000 L, daily production is calculated as 50 L per day.

cal element which detects the deformation and transmits it as changes in voltage.

*2.1.3. pH meter kit*

*2.1.4. Temperature sensor*

range of 0–400°C.

**Figure 3.** pH meter kit.

**2.3. Materials**

processor is presented in **Table 2**.

**2.2. Processor design**

**Table 1.** Technical specifications of the Arduino Uno.

#### *2.1.2. Pressure transducer sensor*

This measures the pressure of gas with a carbon steel alloy sensor material (**Figure 2**). It has a working pressure range of 0–1.2 MPa. The normal working temperature range is 0–85°C and the response time is approximately 2 ms.

**Figure 2.** Pressure transducer sensor.

It consists of an elastic material that deforms under the application of pressure and an electrical element which detects the deformation and transmits it as changes in voltage.

#### *2.1.3. pH meter kit*

The technical specification of the Arduino Uno is presented in **Table 1**.

**S/N Item Value Remarks**

4 Analog IO 6 10-bit 5 Program memory Flash 32 kb, EEPROM 1 kb SRAM 2 kb

8 Programmer In-system firmware USB-based 9 Serial communications SPI, I2C Software UART

1 Micro-controller 8-bit Atmel ATmega328p 1 mm sheet metal 2 Operational voltage 5 V Input range: 7–12 V 3 Digital GPIO 14 6 capable of PWM

*2.1.2. Pressure transducer sensor*

**Figure 2.** Pressure transducer sensor.

the response time is approximately 2 ms.

**Table 1.** Technical specifications of the Arduino Uno.

6 Clock speed 16 MHz 7 USB Type B socket

10 Applications of Design for Manufacturing and Assembly

10 Other RTC, watchdog, interrupts

The Arduino is programmed using the Arduino IDE with source code will be written in C.

This measures the pressure of gas with a carbon steel alloy sensor material (**Figure 2**). It has a working pressure range of 0–1.2 MPa. The normal working temperature range is 0–85°C and This is a kit that measures pH of a substance. It is specially designed for the Arduino and has an accuracy of ±0.1 pH (at 25°C). The kit has a range of 0–14 pH. The kit consists of a pH sensor probe, a BNC connector and a pH 2.0 interface. The pH meter kit is shown in **Figure 3**.

**Figure 3.** pH meter kit.

#### *2.1.4. Temperature sensor*

This takes temperature readings for the plant to aid process insights. It has a temperature range of 0–400°C.

#### **2.2. Processor design**

The processor design involves the determination of a detailed, industrial-scale engineering model for a biodiesel processor and the associated parametric specification. The components parts are linked up with the aim of integrating and optimizing the entire process in the conversion of oil to biodiesel. The design involves the determination of the reactor geometry and capacity as well as the choice of construction material. The first design consideration is the reactor capacity which is determined by the mass balance of input and output streams. The sum of the volume of all streams entering the reactor gives the total liquid volume of the reactor for one batch. In order to meet an annual production of 18,000 L, daily production is calculated as 50 L per day.

#### **2.3. Materials**

The processor part list as well as the materials employed in the construction of a biodiesel processor is presented in **Table 2**.

*2.3.3. Heater*

sured and regulated by means of a thermostat.

*ρ* = \_\_

The weight of the fluid is expressed as Eq. (4).

(workdone by the heater) is given as Eq. (5).

temperature difference between the final T2

reaction tank is 1.0 J/S-m2

of the reactor (0.5430 m<sup>3</sup>

temperature T<sup>1</sup>

assumed electrical to heat conversion efficiency (η<sup>e</sup>

The power rating of the heater is expressed as Eq. (6).

*P* = \_\_

The rate of heat loss is expressed as Eq. (7).

(30°C).

Therefore the rate of heat loss *RL*

Therefore, the weight of the fluid is calculated as 219.84 N.

The fluid density is expressed as Eq. (3).

where *v* is the volume (0.027 m<sup>3</sup>

fluid is calculated 22.41 kg.

The bottom of the cylindrical reaction chamber is fitted with an internal heating element. The heater is a 3 kW, 120 V a/c stainless steel heating element. It is used to raise the temperature of the reaction chamber and its content between 0 and 400°C. The processor chamber is heated by this thermostatically controlled electric heater and the temperature of the content is mea-

Application of Design for Manufacturing and Assembly: Development of a Multifeedstock…

*m*

*w* = *mg* (4)

where *m* is the mass of the fluid (22.41 kg) and *g* is the acceleration due to gravity (9.81 m/s2

The specific heat capacity of vegetable oil is 2340 J/kg K, weight (W) of 25 L of vegetable oil is 219.84 N, the heater has a power rating of 3 kW, assuming the ambient temperature of 30°C, and the mixture is heated to a temperature of 60°C, therefore, the heat transferred to the fluid

*Q* = *mc*Δ*T* (5)

where *m* is the fluid mass (kg); *c* is the specific heat capacity of the fluid (J/kg K); and ∆*T* is the

Hence, 1573 kJ is the quantity of heat transferred to 25 L of vegetable oil per batch. The

*W*

where *P* is the power rating of the heater (3 kW); *W* is the workdone by the heater (1573 kJ).

*RL* = *α* ∗ *SA* ∗ Δ*T* (7)

is calculated as 16.29 J/s.

); ∆*T* is the temperature difference between the final T2

Therefore, the estimated heating time for the used vegetable oil is 530 s.

where *α* is heat loss coefficient for the reaction tank (1.0 J/S-m2

and ambient temperature T<sup>1</sup>

) and *ρ* is the fluid density (830 kg/m<sup>3</sup>

*<sup>v</sup>* (3)

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.

) is 80%. The heat loss coefficient for the

*<sup>T</sup>* (6)

.

and ambient

), then the mass *m* of the

).

13

**Table 2.** Processor parts list and materials employed.

#### *2.3.1. Sparkless electric motor*

This is a device powered by electric current to produce continuous motion. It rotates at a maximum speed of 300 rpm and attached to it, is the shaft. The power of the electric motor is rated as 2 kW and it is such that it is a variable speed electric motor from which different stir speed can be selected. It has a maximum frequency of 3000 Hz. The electric motor drives the stirrer which in turn stirred the raw materials inside the reaction chamber tank.

According to [23] a 2 kW electric motor is adequate to stir fluid of density 800–900 kg/m<sup>3</sup> at a speed of 300 rpm. This informs the choice of this type of electric motor.

#### *2.3.2. Reactor*

This is the main chamber where transesterification takes place. The reactor is pressurized and its chamber is constructed into cylindrical shape with galvanized steel. The reaction tank has a galvanized steel lid welded on. The lid for the tank is important in order to prevent escape of methoxide fumes during the process and also allows for distilling out the methanol after each batch. The lid also helps to prevent exposure to poisonous fumes, dangerous chemicals and fire. The capacity of the reaction tank is limited to 25 L per batch of vegetable oil.

#### *2.3.3. Heater*

*2.3.1. Sparkless electric motor*

**Table 2.** Processor parts list and materials employed.

*2.3.2. Reactor*

This is a device powered by electric current to produce continuous motion. It rotates at a maximum speed of 300 rpm and attached to it, is the shaft. The power of the electric motor is rated as 2 kW and it is such that it is a variable speed electric motor from which different stir speed can be selected. It has a maximum frequency of 3000 Hz. The electric motor drives the

½″ adapter ¾″ socket ¾″ × ½″ bushing ¼″ gas outlet valve ½″ T-fitting ½″ PVC pipe

stirrer which in turn stirred the raw materials inside the reaction chamber tank.

fire. The capacity of the reaction tank is limited to 25 L per batch of vegetable oil.

speed of 300 rpm. This informs the choice of this type of electric motor.

**S/N Description Quantity Remarks** 1 Sparkless electric motor (E2A), 3000 Hz, max 700 NCm 1 2 kW capacity

12 Applications of Design for Manufacturing and Assembly

4 Valve 1 regulatory valve

6 Thermostat 1 TED-2001 (0–400°C) 7 Stirrer 1 Stainless steel

9 Arduino Uno 1 Microcontroller 10 pH meter kit 1 43 × 32 mm 11 Pressure transducer sensor 1 ¼″ 1.2 MPa

13 Valves and fitting 2 ½″ ball valve

5 Insulator Saw dust

8 Funnel 1 Glass

12 Temperature sensor 1

2 Reactor 1 2 mm thick galvanized sheet metal 3 Heater 1 3 kW of heat capacity of 0.44 kJ/kgK

According to [23] a 2 kW electric motor is adequate to stir fluid of density 800–900 kg/m<sup>3</sup>

This is the main chamber where transesterification takes place. The reactor is pressurized and its chamber is constructed into cylindrical shape with galvanized steel. The reaction tank has a galvanized steel lid welded on. The lid for the tank is important in order to prevent escape of methoxide fumes during the process and also allows for distilling out the methanol after each batch. The lid also helps to prevent exposure to poisonous fumes, dangerous chemicals and

at a

The bottom of the cylindrical reaction chamber is fitted with an internal heating element. The heater is a 3 kW, 120 V a/c stainless steel heating element. It is used to raise the temperature of the reaction chamber and its content between 0 and 400°C. The processor chamber is heated by this thermostatically controlled electric heater and the temperature of the content is measured and regulated by means of a thermostat.

The fluid density is expressed as Eq. (3).

$$
\rho = \frac{m}{\overline{v}} \tag{3}
$$

where *v* is the volume (0.027 m<sup>3</sup> ) and *ρ* is the fluid density (830 kg/m<sup>3</sup> ), then the mass *m* of the fluid is calculated 22.41 kg.

The weight of the fluid is expressed as Eq. (4).

$$w = mg\tag{4}$$

where *m* is the mass of the fluid (22.41 kg) and *g* is the acceleration due to gravity (9.81 m/s2 ).

Therefore, the weight of the fluid is calculated as 219.84 N.

The specific heat capacity of vegetable oil is 2340 J/kg K, weight (W) of 25 L of vegetable oil is 219.84 N, the heater has a power rating of 3 kW, assuming the ambient temperature of 30°C, and the mixture is heated to a temperature of 60°C, therefore, the heat transferred to the fluid (workdone by the heater) is given as Eq. (5).

$$Q = mc\Delta T\tag{5}$$

where *m* is the fluid mass (kg); *c* is the specific heat capacity of the fluid (J/kg K); and ∆*T* is the temperature difference between the final T2 and ambient temperature T<sup>1</sup> .

Hence, 1573 kJ is the quantity of heat transferred to 25 L of vegetable oil per batch. The assumed electrical to heat conversion efficiency (η<sup>e</sup> ) is 80%. The heat loss coefficient for the reaction tank is 1.0 J/S-m2 -°C and the surface are of the reactor is 0.5430 m<sup>3</sup> .

The power rating of the heater is expressed as Eq. (6).

$$P = \frac{W}{T} \tag{6}$$

where *P* is the power rating of the heater (3 kW); *W* is the workdone by the heater (1573 kJ).

Therefore, the estimated heating time for the used vegetable oil is 530 s.

The rate of heat loss is expressed as Eq. (7).

$$R\_{\perp} = \alpha \ast SA \ast \Delta T \tag{7}$$

where *α* is heat loss coefficient for the reaction tank (1.0 J/S-m2 -°C); *SA* is the total surface area of the reactor (0.5430 m<sup>3</sup> ); ∆*T* is the temperature difference between the final T2 and ambient temperature T<sup>1</sup> (30°C).

Therefore the rate of heat loss *RL* is calculated as 16.29 J/s.

## *2.3.4. Valve*

The ball valve is fitted to the bottom of reaction tank in order to control the rate of discharge of reaction products from the reaction chamber.

The resisting torque is expressed as Eq. (10).

sec (400 rev/s).

*T* = \_\_\_\_ *<sup>P</sup>*

Hence, the total resisting torque is calculated as 0.80 N.

tion tank in order to overcome the problem of corrosion.

(Eq. (12)) is found to occur at the centre of the plate.

ymax <sup>=</sup> *<sup>α</sup>qb*<sup>4</sup>

relationships expressed by Eqs. (13) and (14).

).

Furthermore, recall the weight of the fluid given by Eq. (4).

; *α* = 0.0843.

*J* = \_\_\_\_

where *d* is the diameter of the shaft (mm). According to [25], using *τ* = 55 MN/m2

*<IP_ADDRESS>. Design for shaft deflection*

modulus of elasticity (N/m2

and *β*<sup>2</sup>

m; *E* = 200 GN/m2

significant distortion.

Then the weight of the fluid is 219.84 N.

Hence, maximum deflection ymax = 1.5 × 10−<sup>3</sup>

The factors *β*<sup>1</sup>

10−<sup>3</sup>

Furthermore, the polar moment of inertia is expressed as Eq. (11).

where *P* is the power rating of the electric motor (2 kW); *N* is the number of revolution per

Application of Design for Manufacturing and Assembly: Development of a Multifeedstock…

*d*4

shaft is made) and substituting for *T* in Eq. (8), the shaft diameter is calculated as 17 mm and designed as 20 mm to a safety factor of 1.2. The weight of the shaft is negligible since the shaft

Stainless steel was employed for the fabrication of the shaft and galvanized steel for the reac-

For a plate of length *d* (mm), shorter side *b* and thickness *t* (mm), the maximum deflection *ymax*

where *q* is the fluid weight and the value of factor *α* depends on the ratio of *d*/*b* and *E* is the

The maximum bending moment also occur at the centre of the plate and are given by the

MXYmax = *β*<sup>1</sup> *qb*<sup>2</sup> (13)

MYXmax = *β*<sup>2</sup> *qb*<sup>2</sup> (14)

The maximum deflection given by Eq. (12) is calculated where *q* = 219.84 N; *b* = 0.6 m; *t* = 2.0 ×

indicates that the processor has satisfactory strength to withstand the loading forces without

are given for an assumed value of Poisson's ratio *v* equal to 0.3.

\_\_\_\_

is vertical in orientation for mixing, it is subjected majorly to twisting moment.

<sup>2</sup>*<sup>N</sup>* (10)

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15

<sup>32</sup> (11)

*Et*<sup>3</sup> (12)

mm. This small value of the maximum deflection

(maximum shearing stress of steel from which the

### *2.3.5. Insulator*

The reaction tank is well lagged all through to keep the temperature of the reaction tank and its content constant by preventing heat loss. The insulating material employed is saw dust and has a thermal resistivity of 33.3 mK/W. Insulated tanks maintain heat better than uninsulated ones due to continuous escape of heat from the tank.

#### *2.3.6. Thermostat*

This is essential for temperature regulation or control. It is a safety device capable of gauging temperature and helps in accurate temperature control as well as prevention of pressure build up within the reaction tank. The temperature range of the temperature regulator is between 0 and 400°C. This allows transesterification reaction to take place at selected temperatures. A temperature probe was attached to sense the temperature inside the reaction tank for two reasons; to indicate that the oil has been pre-heated so that methanol/catalyst can be loaded into the reaction tank and to keep the temperature of the solution between a given temperature range during the reaction loop. The outside temperature probe is to indicate when the inline heater should be engaged or disengaged.

#### *2.3.7. Shaft*

A shaft of 20 mm diameter is used, which ensures satisfactory strength and rigidity when the shaft is transmitting power under different operating and loading conditions. It is fabricated from stainless steel because of its high resistance to corrosive effects of chemicals and catalysts, since the shaft often comes in contact with chemicals and catalyst during mixing process at high temperatures.

#### *<IP_ADDRESS>. Design of shaft*

Shaft design consists primarily of the determination of the correct shaft diameter to ensure satisfactory strength and rigidity when the shaft is transmitting power under various operating and loading conditions [24].

Using the relationship given in Eq. (8).

$$\frac{T}{l} = \frac{\pi}{l} = \frac{G\theta}{l} \tag{8}$$

where *G* is the modulus of rigidity (N/m2 ); *L* is the length of shaft (m); *θ* is the angle of twist (degree); *T* is the total resisting torque (N); *τ* is the maximum shearing stress (N/m2 ); *J* is the polar moment of inertia (m/s<sup>4</sup> ); *R* is the radius of shaft in (m).

From the relation in Eq. (9),

$$\frac{T}{I} = \frac{\pi}{T} \tag{9}$$

The resisting torque is expressed as Eq. (10).

*2.3.4. Valve*

*2.3.5. Insulator*

*2.3.6. Thermostat*

*2.3.7. Shaft*

at high temperatures.

*<IP_ADDRESS>. Design of shaft*

ing and loading conditions [24].

polar moment of inertia (m/s<sup>4</sup>

From the relation in Eq. (9),

Using the relationship given in Eq. (8).

\_\_

where *G* is the modulus of rigidity (N/m2

\_\_

of reaction products from the reaction chamber.

14 Applications of Design for Manufacturing and Assembly

heater should be engaged or disengaged.

insulated ones due to continuous escape of heat from the tank.

The ball valve is fitted to the bottom of reaction tank in order to control the rate of discharge

The reaction tank is well lagged all through to keep the temperature of the reaction tank and its content constant by preventing heat loss. The insulating material employed is saw dust and has a thermal resistivity of 33.3 mK/W. Insulated tanks maintain heat better than un-

This is essential for temperature regulation or control. It is a safety device capable of gauging temperature and helps in accurate temperature control as well as prevention of pressure build up within the reaction tank. The temperature range of the temperature regulator is between 0 and 400°C. This allows transesterification reaction to take place at selected temperatures. A temperature probe was attached to sense the temperature inside the reaction tank for two reasons; to indicate that the oil has been pre-heated so that methanol/catalyst can be loaded into the reaction tank and to keep the temperature of the solution between a given temperature range during the reaction loop. The outside temperature probe is to indicate when the inline

A shaft of 20 mm diameter is used, which ensures satisfactory strength and rigidity when the shaft is transmitting power under different operating and loading conditions. It is fabricated from stainless steel because of its high resistance to corrosive effects of chemicals and catalysts, since the shaft often comes in contact with chemicals and catalyst during mixing process

Shaft design consists primarily of the determination of the correct shaft diameter to ensure satisfactory strength and rigidity when the shaft is transmitting power under various operat-

*<sup>r</sup>* <sup>=</sup> \_\_\_\_\_\_\_\_ *<sup>G</sup>*<sup>θ</sup>

*<sup>l</sup>* (8)

*<sup>r</sup>* (9)

); *J* is the

); *L* is the length of shaft (m); *θ* is the angle of twist

*T <sup>J</sup>* <sup>=</sup> \_\_<sup>τ</sup>

(degree); *T* is the total resisting torque (N); *τ* is the maximum shearing stress (N/m2

); *R* is the radius of shaft in (m).

*T <sup>J</sup>* <sup>=</sup> \_\_<sup>τ</sup>

$$T = \frac{P}{2\pi N} \tag{10}$$

where *P* is the power rating of the electric motor (2 kW); *N* is the number of revolution per sec (400 rev/s).

Hence, the total resisting torque is calculated as 0.80 N.

Furthermore, the polar moment of inertia is expressed as Eq. (11).

$$J = \frac{\pi d 4}{32} \tag{11}$$

where *d* is the diameter of the shaft (mm).

According to [25], using *τ* = 55 MN/m2 (maximum shearing stress of steel from which the shaft is made) and substituting for *T* in Eq. (8), the shaft diameter is calculated as 17 mm and designed as 20 mm to a safety factor of 1.2. The weight of the shaft is negligible since the shaft is vertical in orientation for mixing, it is subjected majorly to twisting moment.

Stainless steel was employed for the fabrication of the shaft and galvanized steel for the reaction tank in order to overcome the problem of corrosion.

#### *<IP_ADDRESS>. Design for shaft deflection*

For a plate of length *d* (mm), shorter side *b* and thickness *t* (mm), the maximum deflection *ymax* (Eq. (12)) is found to occur at the centre of the plate.

$$\mathbf{y}\_{\text{max}} = \frac{aqb^4}{Et^3} \tag{12}$$

where *q* is the fluid weight and the value of factor *α* depends on the ratio of *d*/*b* and *E* is the modulus of elasticity (N/m2 ).

The maximum bending moment also occur at the centre of the plate and are given by the relationships expressed by Eqs. (13) and (14).

MXYmax = *β*<sup>1</sup> *qb*<sup>2</sup> (13)

$$\mathbf{M}\_{\rm Y\uparrow\!\!\!\!\!\/\!\!\/\!\!\/} = \beta\_2 \, qb^2 \tag{14}$$

The factors *β*<sup>1</sup> and *β*<sup>2</sup> are given for an assumed value of Poisson's ratio *v* equal to 0.3.

Furthermore, recall the weight of the fluid given by Eq. (4).

Then the weight of the fluid is 219.84 N.

The maximum deflection given by Eq. (12) is calculated where *q* = 219.84 N; *b* = 0.6 m; *t* = 2.0 × 10−<sup>3</sup> m; *E* = 200 GN/m2 ; *α* = 0.0843.

Hence, maximum deflection ymax = 1.5 × 10−<sup>3</sup> mm. This small value of the maximum deflection indicates that the processor has satisfactory strength to withstand the loading forces without significant distortion.

#### *2.3.8. Funnel*

After completion of the reaction, the product is transferred into a separating funnel for a certain time interval (approximately 24 h) for phase separation. Since the solubility of methyl ester is low, the glycerine tends to collect at the bottom. With excess alcohol, the unconverted triglycerides should essentially be zero. However, some monoglyceride and diglycerides must be present [26]. Due to their polarity, partially reacted glycerides should be preferentially attracted to the glycerine phase and then removed when the phase is separated. The funnel is employed during separation of biodiesel from the glycerine. It is also used for separating washed methyl esters from impurities such as water, sodium hydroxide and glycerine.

#### **2.4. Construction of the biodiesel processor**

A 2 mm thick galvanized steel was used in the fabrication of the reaction tank because of the following reasons:

#### **2.5. Design for volume**

The volume of the reaction tank (cylinder) is given by Eq. (15).

$$V = \pi r^2 h\tag{15}$$

both functions. Depending on the nature of feedstock, the incorporation of intelligent systems for control and monitoring helps in determining the need for the pre-treatment process or otherwise. In addition existing biodiesel processor has a base which makes drainage of the product a challenge. The application of DFMA in the development of the biodiesel processor replaces the base with a conical base which makes drainage quite easy. Other significant improvements in the new design compared to the existing design via the application of the DFA are discussed in Section 2.9. Autodesk inventor was employed in the design and assem-

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17

**Figures 4**–**9** show the various views of the assembly diagram of the existing processor while **Figure 10** shows the new design for the biodiesel processor upon the application of design for

bly drawing of the processor the developed processor.

manufacturing and assembly.

**Figure 4.** Sectional view of the developed processor.

where *π* = 22/7; *r* is the radius of the cylinder (0.12 m) and *h* is the height of cylinder (0.60 m).

The capacity of the processor is limited to 25 L of vegetable oil per batch.

#### **2.6. Total surface area of the processor**

The total surface area of the reactor is expressed by Eq. (16).

$$\text{T.S.A} = \text{ } 2\pi r^2 \text{\textquotedbl{}} \text{ } 2\pi rh \tag{16}$$

where *π* = 22/7 and *R* is the radius of the cylinder (0.12 m).

Then the total surface area of the tank is 0.5430 m<sup>3</sup> .

#### **2.7. Design for manufacturing and assembly**

Existing biodiesel processor has a separate pre-treatment tank either as a stand-alone facility or in addition to the reactor which makes the overall assembly expensive and cumbersome. The application of the design for manufacturing and assembly (DFMA) in the development of the biodiesel processor eliminates the need of the pre-treatment tank while the reactor serves both functions. Depending on the nature of feedstock, the incorporation of intelligent systems for control and monitoring helps in determining the need for the pre-treatment process or otherwise. In addition existing biodiesel processor has a base which makes drainage of the product a challenge. The application of DFMA in the development of the biodiesel processor replaces the base with a conical base which makes drainage quite easy. Other significant improvements in the new design compared to the existing design via the application of the DFA are discussed in Section 2.9. Autodesk inventor was employed in the design and assembly drawing of the processor the developed processor.

**Figures 4**–**9** show the various views of the assembly diagram of the existing processor while **Figure 10** shows the new design for the biodiesel processor upon the application of design for manufacturing and assembly.

**Figure 4.** Sectional view of the developed processor.

*2.3.8. Funnel*

following reasons:

corrosion.

**2.5. Design for volume**

**2.4. Construction of the biodiesel processor**

16 Applications of Design for Manufacturing and Assembly

**i.** It does not catalyze the oil unlike copper;

**2.6. Total surface area of the processor**

**ii.** Its high resistance to pressure and temperature;

The volume of the reaction tank (cylinder) is given by Eq. (15).

The total surface area of the reactor is expressed by Eq. (16).

where *π* = 22/7 and *R* is the radius of the cylinder (0.12 m).

T.S.A = 2*πr* <sup>2</sup>

Then the total surface area of the tank is 0.5430 m<sup>3</sup>

**2.7. Design for manufacturing and assembly**

The capacity of the processor is limited to 25 L of vegetable oil per batch.

After completion of the reaction, the product is transferred into a separating funnel for a certain time interval (approximately 24 h) for phase separation. Since the solubility of methyl ester is low, the glycerine tends to collect at the bottom. With excess alcohol, the unconverted triglycerides should essentially be zero. However, some monoglyceride and diglycerides must be present [26]. Due to their polarity, partially reacted glycerides should be preferentially attracted to the glycerine phase and then removed when the phase is separated. The funnel is employed during separation of biodiesel from the glycerine. It is also used for separating washed methyl esters from impurities such as water, sodium hydroxide and glycerine.

A 2 mm thick galvanized steel was used in the fabrication of the reaction tank because of the

**iii.** Its ability to withstand the actions of chemicals and catalyst without any sign of rust or

*V* = *r* <sup>2</sup> *h* (15)

where *π* = 22/7; *r* is the radius of the cylinder (0.12 m) and *h* is the height of cylinder (0.60 m).

.

Existing biodiesel processor has a separate pre-treatment tank either as a stand-alone facility or in addition to the reactor which makes the overall assembly expensive and cumbersome. The application of the design for manufacturing and assembly (DFMA) in the development of the biodiesel processor eliminates the need of the pre-treatment tank while the reactor serves

+ 2*rh* (16)

**v.** Welding of five blades to the shaft of diameter 20 mm and length 400 mm to form the

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19

**vi.** Welding of the flat cover to the tank and use of hinges to create a top opening for main-

**vii.** Attachment of the electric motor to the stirrer assembly using bolt and nuts.

stirrer assembly.

tenance purposes.

**viii.**Finishing operations: deburring and painting.

**Figure 6.** Right view of the developed processor.

**Figure 5.** Sectional view (top) of the developed processor.

#### **2.8. Method of assembly**

The procedural steps for the assembly operation are as follow;

**Figure 6.** Right view of the developed processor.

**2.8. Method of assembly**

height of 600 mm.

The procedural steps for the assembly operation are as follow;

**Figure 5.** Sectional view (top) of the developed processor.

18 Applications of Design for Manufacturing and Assembly

**iv.** Welding of the stand of height 180 mm to the cylinder.

**i.** Rolling of the inner cylinder of diameter 240 mm and height and height 350 mm.

**iii.** Lagging of the space in between the inner and outer cylinder followed by welding of the inner and outer diameter to give a single cylinder of diameter 360 mm with an overall

**ii.** Rolling of the outer cylinder of diameter 360 mm and height 500 mm.

**Figure 7.** Exploded view of the developed processor.

**Figure 9.** The developed biodiesel processor.

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21

**Figure 10.** The new design for the biodiesel processor.

**Figure 8.** Isometric view of the developed processor.

Application of Design for Manufacturing and Assembly: Development of a Multifeedstock… http://dx.doi.org/10.5772/intechopen.80085 21

**Figure 9.** The developed biodiesel processor.

**Figure 7.** Exploded view of the developed processor.

20 Applications of Design for Manufacturing and Assembly

**Figure 8.** Isometric view of the developed processor.

**Figure 10.** The new design for the biodiesel processor.

#### **2.9. Differences between the existing and new design**

Following the application of the design for manufacturing and assembly (DFA) in the development of the biodiesel processor, the following are the differences between the existing and new design;

**S/N Parts Process Materials Cost (USD)**

Total 210

http://dx.doi.org/10.5772/intechopen.80085

Application of Design for Manufacturing and Assembly: Development of a Multifeedstock…

**(USD)**

Subtotal 752.67

1 ¼″ 1.2 MPa 14.79 14.79

**Total price (USD)**

23

1. 3 support legs Cutting/welding Mild steel 33 2. Cylindrical body Cutting/rolling Galvanized steel 70 3. Flat cover Cutting/welding Galvanized steel 12 4. Control panel Bought out Galvanized steel 30 5. Stirrer assembly Cutting/welding Stainless steel 65

**Name Quantity Description Unit price**

 Stainless steel rod 1 0.60 m length 60 60 Mild steel 1 2 m length 45 45 2 kW electric motor 1 AC motor with speed control 70 70 Ball valve 3 Outlet and inlet valves 0.55 1.64 Galvanized sheet metal 3 rolls Square cross section 41 123 Welding electrodes 1 packet E020, E6023 2.73 2.73 Heater 1 8.22 8.22 Temperature probe 1 8.22 8.22 Contactor 1 10.95 10.95 Funnel 1 8.22 8.22 Neon and switch light 2 2.73 2.73 Arduino Uno 1 Microcontroller 17.8 17.8

 Analog pH meter 1 43 × 32 mm 34.2 34.2 LCD 1 3.28 3.28 Electric cables 5 yards 1.40 6.84 Angle iron 1 20 20 Control panel 1 30 30 Resistors and capacitors 2.75 2.75 Transport 40 40 Labor 12.3 12.3 Performance evaluation 275 275

22 Miscellaneous 10% of total costs 75.267

**Total 827.937**

**Table 4.** Cost analysis of the new design.

13 Pressure transducer sensor

**Table 5.** Bill of engineering measurement.

**Item #**

#### **2.10. Summary of development analysis**

The bill of engineering measurements (development and evaluation) is presented in **Table 5**.

**Table 3.** Cost analysis of the existing design.

Application of Design for Manufacturing and Assembly: Development of a Multifeedstock… http://dx.doi.org/10.5772/intechopen.80085 23

**Table 4.** Cost analysis of the new design.

**2.9. Differences between the existing and new design**

top for maintenance purposes.

22 Applications of Design for Manufacturing and Assembly

**2.10. Summary of development analysis**

**Table 3.** Cost analysis of the existing design.

new design;

Following the application of the design for manufacturing and assembly (DFA) in the development of the biodiesel processor, the following are the differences between the existing and

**i.** Existing design involves rolling of the cylinder cover whose inner diameter is 240 mm and outer diameter 360 mm. The space in between the inner and outer cylindrical cover was also lagged after which the two are brought together by welding. In the new design, the cylindrical cover replaced with a flat cover welded to the tank with an opening at the

**ii.** Existing design has seven blades welded to the shaft to form the stirrer assembly with four baffles to prevent splashing. In the new design fives blades ensures homogeneity of

**iii.** In the existing design, the shaft was joined to the cylindrical cover using bearing, bolt and nuts while this was simplified in the new design with the replacement of the cylindrical cover with a flat cover. There was no joining of the shaft to the cover. This makes disassembly quite easy for maintenance purposes. The shaft was passed through the flat cover with the electric motor attached to the top of the shaft using bearing, bolts and nuts. **iv.** Welding of the rectangular control panel whose length is 300 mm and breadth 200 mm, as well as welding of the stand of height 550 mm to the control panel was done in the existing design. In the new design, the fabrication of the control panel was replaced with a smart control panel bought out at price lesser than the cost of fabrication in the existing design.

**v.** Smart and intelligent systems were incorporated into the new design for process moni-

**vi.** The application of the design for manufacturing and assembly (DFA) in the development of the biodiesel processor replaces the base with a conical base which makes drainage quite easy.

**vii.** The new design is smart, robust and more efficient than the existing design. The cost comparison of the new and existing design is shown in **Tables 3** and **4**, respectively

The bill of engineering measurements (development and evaluation) is presented in **Table 5**.

Total 337

**S/N Parts Process Materials Cost (USD)**

1. 7 support legs Cutting/welding Mild steel 77 2. Cylindrical body Cutting/rolling Galvanized steel 30 3. Cylindrical cover Cutting/rolling Galvanized steel 60 4. Control panel Cutting/welding Galvanized steel 60 5. Stirrer assembly Cutting/ welding Stainless steel 75

the mixture with two baffles to prevent splashing of the mixture.

toring and control which was lacking in existing design.

**Table 5.** Bill of engineering measurement.

From **Tables 4** and **5**, the development of the biodiesel processor was cost effective with the elimination of pre-treatment and use of simple manufacturing processes.

The development and performance evaluation of the processor cost a total sum of 827.937 USD. This is relatively cost effective considering the price of ready-made biodiesel processors in the market. Using the principles of DFMA, This work provides a design framework for both small and large scale biodiesel plant for industrial, laboratory and experimental purposes.

Application of Design for Manufacturing and Assembly: Development of a Multifeedstock…

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25

Faculty of Applied Sciences, Department of Chemical and Biological Sciences, Vancouver,

[1] Boothroyd G. Early cost estimation in product design. Journal of Manufacturing

[2] Naiju CD, Jayakrishnan V, Warrier V, Rijul B. Application of Design for Manufacturing and Assembly (DFMA) methodology in the steel furniture industry. In: National

[3] Boothroyd G, Dekker M. Assembly Automation and Product Design. New York; 1992.

[4] Katja T, Miika J, Jari P. Activity based costing and process modelling for cost-conscious product design: A case study in a manufacturing company. International Journal of

[5] Jain VK. Manufacturing Processes. India Institute of Technology, Kanpur, India:

[6] Strienstra D. Introduction to Design for Assembly and Manufacturing. 2012. pp. 1-81

[7] Lucchetta G, Bariani PF, Knight WA. Integrated design analysis for product simplifica-

[8] Ejaz S, Arjun MG, Naiju CD, Annamalai K. Conceptualizatio Design for Manufacture and Assembly (DFMA) of juice mixer grinder. In: National Conference on Advances in

[9] Whitney D. Mechanical Assembly and its Role in Product Development. 2005. pp. 1-29 [10] O'Sullivan D. Industrial Automation. Portugal: Universidade do Minho; 2009. pp. 1-62 [11] Leevijit T, Wisutmethangoon W, Prateepchaikul G, Tongurai C, Allen M. Transesterification of palm oil in series of continuous stirred tank reactors. Asian Journal on

Conference on Technological Advancements in Engineering. 2016. pp. 1-4

**Author details**

Canada

**References**

pp. 1-10

Systems. 1994;**7**:183-192

Ilesanmi Afolabi Daniyan\* and Khumbulani Mpofu

Production Economics. 2002;**79**(1):75-78

Energy and Environment. 2006;**7**(3):336-346

Department of Mechanical Engineering; 2015. pp. 1-34

tion. CIRP Annals-Manufacturing Technology. 2009;**54**(1):147-150

Mechanical Engineering. India: NCAME-2011; 2011. pp. 297-302

\*Address all correspondence to<EMAIL_ADDRESS>
#### **2.11. Design specifications**

The specifications in accordance with design is as follow:

## **3. Contribution to knowledge**

The work will contribute to knowledge as follows:

## **4. Conclusions**

This work brings about the development of a smart small scale biodiesel processor with a capacity of 50 L per day. The processor incorporate all feedstock requirements for the production of biodiesel hence, both alkali-catalyzed and acid-catalyzed transesterification can be undertaken with the processor. In addition, the assembly operations of the processor was simplified to reduce ambiguity and redundancy. This saves a total cost of 127 USD with increased efficiency and robustness. Hence, the overall processor is cost effective in terms of material requirements, parts production, labor and overhead. The principles of DFMA also minimizes part requirements without sacrificing quality. This reduces the overall assembly and manufacturing cost. The development and performance evaluation of the processor cost a total sum of 827.937 USD. This is relatively cost effective considering the price of ready-made biodiesel processors in the market. Using the principles of DFMA, This work provides a design framework for both small and large scale biodiesel plant for industrial, laboratory and experimental purposes.

## **Author details**

From **Tables 4** and **5**, the development of the biodiesel processor was cost effective with the

**i.** Application of the principles of design for manufacture and assembly in the development

**ii.** Improvement in process control and monitoring via the use of sensors and a

**iv.** Provision of design framework for both small and large scale biodiesel processor for

**v.** Development of a processor for processing biodiesel from different feedstock which

This work brings about the development of a smart small scale biodiesel processor with a capacity of 50 L per day. The processor incorporate all feedstock requirements for the production of biodiesel hence, both alkali-catalyzed and acid-catalyzed transesterification can be undertaken with the processor. In addition, the assembly operations of the processor was simplified to reduce ambiguity and redundancy. This saves a total cost of 127 USD with increased efficiency and robustness. Hence, the overall processor is cost effective in terms of material requirements, parts production, labor and overhead. The principles of DFMA also minimizes part requirements without sacrificing quality. This reduces the overall assembly and manufacturing cost.

**iii.** Incorporation of low-cost monitoring system in the biodiesel processor.

elimination of pre-treatment and use of simple manufacturing processes.

i. Size Fits in a 1.524 m × 0.609 m space

vi. Methanol consumption for unused oil 200 mL per 33 mL of oil olein vegetable oil

vii. Methanol consumption for used oil 200 mL per 25 mL of oil frying oil viii. Catalyst consumption 4.5 g per 200 mL methanol

The specifications in accordance with design is as follow:

ii. Operating temperature 20–90°C iii. Stir speed 50–600 rev/min iv. Production amount 25 L per batch v. Production time 3 h per batch

**2.11. Design specifications**

24 Applications of Design for Manufacturing and Assembly

**3. Contribution to knowledge**

of a smart biodiesel processor.

micro-controller.

**4. Conclusions**

The work will contribute to knowledge as follows:

industrial, laboratory and experimental purposes.

could serve as a template for scaling its development.

Ilesanmi Afolabi Daniyan\* and Khumbulani Mpofu

\*Address all correspondence to<EMAIL_ADDRESS>
Department of Industrial Engineering, Tshwane University of Technology, Pretoria, South Africa

## **References**

[11] Leevijit T, Wisutmethangoon W, Prateepchaikul G, Tongurai C, Allen M. Transesterification of palm oil in series of continuous stirred tank reactors. Asian Journal on Energy and Environment. 2006;**7**(3):336-346

**Chapter 3**

**Provisional chapter**

**Application of Six Sigma in Semiconductor**

**Application of Six Sigma in Semiconductor**

Additional information is available at the end of the chapter

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/intechopen.81058

design of experiments (DOE)

Prashant Reddy Gangidi

Prashant Reddy Gangidi

**Abstract**

**1. Introduction**

**Manufacturing: A Case Study in Yield Improvement**

The purpose of this chapter is to outline systematic implementation of the Six Sigma DMAIC methodology as a case study in solving the problem of poor wafer yields in semiconductor manufacturing. The chapter also describes well-known industry standard business processes to be implemented and benchmarked in a semiconductor wafer fabrication facility to manage defect and yield issues while executing a Six Sigma project. The execution of Six Sigma enabled identification of the key process factors, root cause analysis, desired performance levels, and Cpk improvement opportunities. Implementing multilevel factorial design of experiments (DOE) study revealed critical input parameters on process tools contributing to defect formation. Improvement performed on these process tools resulted in in-line defect reduction and ultimately improving final yields.

**Keywords:** Six Sigma, semiconductor manufacturing, DMAIC, defects, yield,

Six Sigma framework is a continuous improvement strategy that minimizes defects and process variation toward an achievement of 3.4 defects per million opportunities in design, manufacturing, and service-oriented industries [1–3]. Six Sigma practitioners often lead crossfunctional teams in an organization to find and eliminate the causes of the errors, defects, lead, and cycle time delays in business processes. With rapid advancements in computers, artificial intelligence, and automotive vehicles, biomedical imaging semiconductor manufacturers find themselves constantly battling the demanding needs of the industry to sustain Moore's law and manufacture smaller chips to support next-generation software and hardware products.

**Manufacturing: A Case Study in Yield Improvement**

© 2016 The Author(s). Licensee InTech. 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.

© 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.

DOI: 10.5772/intechopen.81058

#### **Application of Six Sigma in Semiconductor Manufacturing: A Case Study in Yield Improvement Application of Six Sigma in Semiconductor Manufacturing: A Case Study in Yield Improvement**

DOI: 10.5772/intechopen.81058

Prashant Reddy Gangidi Prashant Reddy Gangidi

[12] Marjanovic AV, Stamenkovic OS, Todorovic ZB, Lazic ML, Veljkovic VB. Kinetics of the

[13] Bello EI, Daniyan IA, Akinola AO, Ogedengbe TI. Development of a biodiesel processor.

[14] Musa IA. Design & construction of a pilot plant for the production of biodiesel from

[15] Bhachu C, Chow N, Christensen A, Drew A, Ishkintana L, Lu J, Poon C, Villamayor C, Setiaputra A, Yan T. Design of a Portable Biodiesel Plant. University of Brititsh Colombia;

[16] Highina BK, Bugaje IM, Umar B. Liquid biofuel as alternative transport fuel in Nigeria.

[17] Olutoye MA, Wong SW, Chin LH, Asif M, Hameed BH. Synthesis of fatty acid methyl esters via transesterification of waste cooking oil by methanol with a barium-modified

[18] Tan YH, Abdullah MO, Hipolito CN, Taufiq-Yap YH. Waste ostrich and chicken-eggshells as heterogeneous base catalyst for biodiesel production from used cooking oil: Catalyst characterization and biodiesel yield performance. Applied Energy. 2015;**2**:1-13

[19] Yusuff AS, Adeniyi OD, Olutoye MA, Akpan UG. Development and characterization of a composite anthill-chicken eggshell catalyst for biodiesel production from waste frying

[20] Olutoye MA, Hameed BH. A highly active clay-based catalyst for the synthesis of fatty acid methyl ester from waste cooking palm oil. Applied Catalysis A: General.

[21] Hashem D, Huang S, Pearson J, Farag IH. Biodiesel Processor Automation. Department of Chemical Engineering. United States: University of New Hampshire; 2012. pp. 1-12

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[26] Ma F, Clements LD, Hanna MA. The Effects of Catayst, Free fatty acids and Water on

Transesterification of Beef tallow. Trans ASAE. 1998;**41**(5):1261-1264

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2005. pp. 1-137

2013;**450**:57-62

A Press; 2013

Additional information is available at the end of the chapter Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/intechopen.81058

#### **Abstract**

The purpose of this chapter is to outline systematic implementation of the Six Sigma DMAIC methodology as a case study in solving the problem of poor wafer yields in semiconductor manufacturing. The chapter also describes well-known industry standard business processes to be implemented and benchmarked in a semiconductor wafer fabrication facility to manage defect and yield issues while executing a Six Sigma project. The execution of Six Sigma enabled identification of the key process factors, root cause analysis, desired performance levels, and Cpk improvement opportunities. Implementing multilevel factorial design of experiments (DOE) study revealed critical input parameters on process tools contributing to defect formation. Improvement performed on these process tools resulted in in-line defect reduction and ultimately improving final yields.

**Keywords:** Six Sigma, semiconductor manufacturing, DMAIC, defects, yield, design of experiments (DOE)

## **1. Introduction**

Six Sigma framework is a continuous improvement strategy that minimizes defects and process variation toward an achievement of 3.4 defects per million opportunities in design, manufacturing, and service-oriented industries [1–3]. Six Sigma practitioners often lead crossfunctional teams in an organization to find and eliminate the causes of the errors, defects, lead, and cycle time delays in business processes. With rapid advancements in computers, artificial intelligence, and automotive vehicles, biomedical imaging semiconductor manufacturers find themselves constantly battling the demanding needs of the industry to sustain Moore's law and manufacture smaller chips to support next-generation software and hardware products.

© 2016 The Author(s). Licensee InTech. 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. © 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.

In order to manufacture nanometer range scale chips, there is a tremendous impetus toward developing advanced process control and measurement system capabilities. Defects become a big challenge in the efforts to reduce feature size in most semiconductor fabs as they negatively impact product yields [4]. Six Sigma methodology is often neglected in most fabs, and this chapter gives an overview of its importance and how it can be implemented to reduce defects with the help of a general case study. This chapter presents the step-by-step application of the Six Sigma define, measure, analyze, improve, control (DMAIC) approach to eliminate defects in a lithography process of a semiconductor manufacturing organization. This has helped to reduce defects in the process and thereby improve the final probe yields on a critical technology node. During the measure and analyze phases of the project, data were collected from the processes to understand the baseline performance and for validation of causes. These data were studied through various graphical and statistical analyses. Chi-square test, ANOVA, design of experiments (DOE), control charts, fishbone analysis, FMEA, etc. were used to make meaningful and scientifically proven conclusions about the process and the related causes [5].

**3. Six Sigma application to defect reduction in semiconductor**

In this section, some business process areas have been identified to focus on benchmarking before considering Six Sigma project execution. Once these business process areas are well established, then only deploying Six Sigma teams would be beneficial to see tangible results. Motorola was one of the pioneers of Six Sigma methodology along with General Electric. Apart from Motorola, no other semiconductor manufacturing company has openly advocated the use of Six Sigma but has definitely inherited a lot of concepts and molded them into different terminologies. A big challenge to Six Sigma implementation is management support, and based on historical success rates of such projects initiated at their respective firms, the management decides to stick to their existing problem-solving methodologies or use some concepts from Six Sigma and other techniques used in industries such as aviation

Application of Six Sigma in Semiconductor Manufacturing: A Case Study in Yield Improvement

http://dx.doi.org/10.5772/intechopen.81058

29

The most important goal for any semiconductor fab is to improve the final product yields [4]. Yield is directly correlated to contamination, design margin, process, and equipment errors along with fab operators [11]. **Figure 1** referenced from Integrated Circuit Engineering Corp. shows the ranking of top yield loss causing problems across various fabs [4]. Six Sigma DMAIC methodology can be used as an effective quality and reliability management tool to solve most of these issues, and several literature papers in the form of case studies have been

Sources of random defects could be the equipment, fab personnel, process margins, process chemicals and gases, or cleanroom itself. Data collected from Integrated Circuit Engineering

Human and cleanroom sources of contamination have been steadily declining due to advanced training in this field being developed over the years at various universities and corporations along with rapid strides in automation and artificial intelligence that have modernized clean rooms and minimized human contact in handling wafers. Engineers and upper-level management of these fabs must adopt a systematic methodology in resolving yield losses that occur due to process and equipment variations, and in this chapter, some basic business processes

Corp. (ICE) over the last three decades have been shown in **Figure 2**.

**fabrication sites (FABS)**

and automotive.

published regarding the same.

**Figure 1.** Ranking of yield loss causing problems in fabs.

## **2. Six Sigma literature review**

The primary methodology of Six Sigma is the application of DMAIC problem-solving steps. A brief explanation for each of the steps involved is as follows:

*Define (D):* The first stage focuses on analysis of customer identification, feedback, and requirements along with forming the project stakeholder team [6]. The project team looks at critical to quality (CTQ) and cost of quality (COQ) improvement projects that need to be addressed keeping the end goal of customer satisfaction in mind by defining project scope/problem statement and budget scheduling. It is very critical to define an accurate problem statement along with the scope to ensure the Six Sigma project team will invest all the time, skills, and resources in the right direction.

*Measure (M*): This is the data collection phase where types of data, measurement scales, and sampling and collection methods are evaluated. All the initial metrics of the business case are established to measure the problem scale [2, 7].

*Analyze (A)*: Measure and model relationships between variables, hypothesis testing, root cause analysis using cause and effect analysis tools such as fishbone/Ishikawa diagrams, 8D methodology, and 5 Whys analysis [2, 7].

*Improve (I)*: Post root cause analysis, this phase tries to understand optimum levels of factors responsible for causing the problem via design of experiments (DOE), giving insights to determine corrective and preventative actions. This stage involves lean strategies such as the Kaizen Blitz, poka-yoke (mistake-proofing), cycle time reduction, etc. [7–9].

*Control (C)*: Primary objective in this last phase is to maintain and sustain control over the process and suggest improvement activities to minimize variation and defects. Statistical process control (SPC), total productive maintenance (TPM), and control plan development are some key tools used in the control phase [10].

## **3. Six Sigma application to defect reduction in semiconductor fabrication sites (FABS)**

In order to manufacture nanometer range scale chips, there is a tremendous impetus toward developing advanced process control and measurement system capabilities. Defects become a big challenge in the efforts to reduce feature size in most semiconductor fabs as they negatively impact product yields [4]. Six Sigma methodology is often neglected in most fabs, and this chapter gives an overview of its importance and how it can be implemented to reduce defects with the help of a general case study. This chapter presents the step-by-step application of the Six Sigma define, measure, analyze, improve, control (DMAIC) approach to eliminate defects in a lithography process of a semiconductor manufacturing organization. This has helped to reduce defects in the process and thereby improve the final probe yields on a critical technology node. During the measure and analyze phases of the project, data were collected from the processes to understand the baseline performance and for validation of causes. These data were studied through various graphical and statistical analyses. Chi-square test, ANOVA, design of experiments (DOE), control charts, fishbone analysis, FMEA, etc. were used to make meaningful and scientifically proven conclusions about the process and the related causes [5].

The primary methodology of Six Sigma is the application of DMAIC problem-solving steps.

*Define (D):* The first stage focuses on analysis of customer identification, feedback, and requirements along with forming the project stakeholder team [6]. The project team looks at critical to quality (CTQ) and cost of quality (COQ) improvement projects that need to be addressed keeping the end goal of customer satisfaction in mind by defining project scope/problem statement and budget scheduling. It is very critical to define an accurate problem statement along with the scope to ensure the Six Sigma project team will invest all the time, skills, and

*Measure (M*): This is the data collection phase where types of data, measurement scales, and sampling and collection methods are evaluated. All the initial metrics of the business case are

*Analyze (A)*: Measure and model relationships between variables, hypothesis testing, root cause analysis using cause and effect analysis tools such as fishbone/Ishikawa diagrams, 8D

*Improve (I)*: Post root cause analysis, this phase tries to understand optimum levels of factors responsible for causing the problem via design of experiments (DOE), giving insights to determine corrective and preventative actions. This stage involves lean strategies such as the

*Control (C)*: Primary objective in this last phase is to maintain and sustain control over the process and suggest improvement activities to minimize variation and defects. Statistical process control (SPC), total productive maintenance (TPM), and control plan development are some

Kaizen Blitz, poka-yoke (mistake-proofing), cycle time reduction, etc. [7–9].

**2. Six Sigma literature review**

28 Applications of Design for Manufacturing and Assembly

resources in the right direction.

established to measure the problem scale [2, 7].

methodology, and 5 Whys analysis [2, 7].

key tools used in the control phase [10].

A brief explanation for each of the steps involved is as follows:

In this section, some business process areas have been identified to focus on benchmarking before considering Six Sigma project execution. Once these business process areas are well established, then only deploying Six Sigma teams would be beneficial to see tangible results. Motorola was one of the pioneers of Six Sigma methodology along with General Electric. Apart from Motorola, no other semiconductor manufacturing company has openly advocated the use of Six Sigma but has definitely inherited a lot of concepts and molded them into different terminologies. A big challenge to Six Sigma implementation is management support, and based on historical success rates of such projects initiated at their respective firms, the management decides to stick to their existing problem-solving methodologies or use some concepts from Six Sigma and other techniques used in industries such as aviation and automotive.

The most important goal for any semiconductor fab is to improve the final product yields [4]. Yield is directly correlated to contamination, design margin, process, and equipment errors along with fab operators [11]. **Figure 1** referenced from Integrated Circuit Engineering Corp. shows the ranking of top yield loss causing problems across various fabs [4]. Six Sigma DMAIC methodology can be used as an effective quality and reliability management tool to solve most of these issues, and several literature papers in the form of case studies have been published regarding the same.

Sources of random defects could be the equipment, fab personnel, process margins, process chemicals and gases, or cleanroom itself. Data collected from Integrated Circuit Engineering Corp. (ICE) over the last three decades have been shown in **Figure 2**.

Human and cleanroom sources of contamination have been steadily declining due to advanced training in this field being developed over the years at various universities and corporations along with rapid strides in automation and artificial intelligence that have modernized clean rooms and minimized human contact in handling wafers. Engineers and upper-level management of these fabs must adopt a systematic methodology in resolving yield losses that occur due to process and equipment variations, and in this chapter, some basic business processes

**Figure 1.** Ranking of yield loss causing problems in fabs.

the Six Sigma concepts, cause and effect diagrams, design of experiments, Pareto principles, etc. to tie the total defects measured at the end of the process sequence to the likely sources in the process flow. Most cutting-edge fabs have automated scripts using machine learning principles to have correlation between in-line defects and final yield loss. Advanced data mining software can quickly scan through very large data sets involving integrated circuit parameters, processing parameters, equipment parameters, probe bins, defect metrics, etc. to come up with various models which the Six Sigma team can use to infer meaningful results during the analyze phase of DMAIC. Thus, having a good defect management and yield monitoring system while benchmarking to industry leaders will enable semiconductor fabs to execute Six Sigma projects efficiently while maintaining a competitive edge in

Application of Six Sigma in Semiconductor Manufacturing: A Case Study in Yield Improvement

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31

Another major factor controlling line and probe yields is the ability of the fab to control process variations on critical parameters. In many progressive fabs, situational SPC is now becoming more popular as engineers concentrate more on critical processes and avoid the temptation to overuse tools such as process control charts. Two most important points that fabs must know

**i.** Due to the high number of processing steps and the possibility of defects forming from any source, fabs must have particle monitoring (PMON) charts which are effectively attribute charts that track defects per million opportunities (DPMO) on bare silicon wafers to plot particles coming from the equipment. This helps to isolate defect sources solely coming from process equipment and can be tied with regular total productive maintenance (TPM) cycles in the fab. Process engineering/metrology experts must be able to decide the

**ii.** In-line defect metrology teams must be able to skillfully partition the line in placing SPC charts to control defect metrics based on historical learnings. Most fabs that do not have Six Sigma experts on their team usually have a hard time in determining the number of control charts and end up oversampling or undersampling. Attribute charts for key defect metrics that have downstream product yield impact or customer reliability issues

This section describes a case study wherein Six Sigma DMAIC methodology was used to tackle a probe yield issue due to an in-line defect contamination occurring in a lithography

This phase of the DMAIC methodology aims to define the scope and goals of the improved project in terms of customer requirements and to develop a process that delivers these

regarding SPC charts to track in-line defects for Six Sigma scalability are:

should be given the highest priority in establishing control charts.

the market.

**4. Case study**

process step.

**4.1. Phase 1: Define**

**3.3. Role of SPC in defect monitoring**

sampling frequency in this case.

**Figure 2.** Bar chart showing wafer contamination source.

have been described that must exist in a fab for it to achieve maximum operational efficiency and produce high-quality chips. Six Sigma DMAIC methodology could then be used as and when required to improve these business processes.

#### **3.1. Contamination and control protocols**

Semiconductor processing involves several process fluids and gases, especially in lithography, film/metal deposition, etching, and cleaning steps. These fluids and gases contain impurity elements that can be dangerous to silicon devices [4]. These elements could be classified as the heavy metals, alkali metals, and light elements. Heavy metals such as Fe, Cu, Ni, Zn, Cr, Au, Hg, and Ag could result in wafer scraps and back end yield fallout due to corrosion in electroplating and metal deposition processing steps. Alkali metals such as Na, K, and Li and light elements like Al, Mg, Ca, C, S, Cl, and F could pose processing problems resulting in defects, which could be yield killing [4]. These elements also sometimes accumulate along the chambers, handlers, chucks, etc. of various equipment used in fabs resulting in tool downtime which greatly affects production schedules. Moreover, these elements could also pose safety problems to fab personnel. To understand potential risks and sources of these impure elements, a highly cross-functional FMEA team can be deployed to map out all the processes where source chemicals and gases are used along with identifying potential fail modes, severity, occurrence, and detection capabilities. The RPN exercise can be continued to drive improvements at each processing step where such impure elements are likely to occur.

#### **3.2. Defect process mapping and yield management system**

Defect density is defined as the total number of defects calculated per unit area on the wafer die [4]. In order to reduce defect density between processes, engineers need to identify the specific process steps, equipment, input materials, etc. that are the major contributors to the defect density. This involves the construction of a detailed process flow diagram for isolated segments of the process and the use of various problem-solving tools such as using the Six Sigma concepts, cause and effect diagrams, design of experiments, Pareto principles, etc. to tie the total defects measured at the end of the process sequence to the likely sources in the process flow. Most cutting-edge fabs have automated scripts using machine learning principles to have correlation between in-line defects and final yield loss. Advanced data mining software can quickly scan through very large data sets involving integrated circuit parameters, processing parameters, equipment parameters, probe bins, defect metrics, etc. to come up with various models which the Six Sigma team can use to infer meaningful results during the analyze phase of DMAIC. Thus, having a good defect management and yield monitoring system while benchmarking to industry leaders will enable semiconductor fabs to execute Six Sigma projects efficiently while maintaining a competitive edge in the market.

#### **3.3. Role of SPC in defect monitoring**

Another major factor controlling line and probe yields is the ability of the fab to control process variations on critical parameters. In many progressive fabs, situational SPC is now becoming more popular as engineers concentrate more on critical processes and avoid the temptation to overuse tools such as process control charts. Two most important points that fabs must know regarding SPC charts to track in-line defects for Six Sigma scalability are:

## **4. Case study**

have been described that must exist in a fab for it to achieve maximum operational efficiency and produce high-quality chips. Six Sigma DMAIC methodology could then be used as and

Semiconductor processing involves several process fluids and gases, especially in lithography, film/metal deposition, etching, and cleaning steps. These fluids and gases contain impurity elements that can be dangerous to silicon devices [4]. These elements could be classified as the heavy metals, alkali metals, and light elements. Heavy metals such as Fe, Cu, Ni, Zn, Cr, Au, Hg, and Ag could result in wafer scraps and back end yield fallout due to corrosion in electroplating and metal deposition processing steps. Alkali metals such as Na, K, and Li and light elements like Al, Mg, Ca, C, S, Cl, and F could pose processing problems resulting in defects, which could be yield killing [4]. These elements also sometimes accumulate along the chambers, handlers, chucks, etc. of various equipment used in fabs resulting in tool downtime which greatly affects production schedules. Moreover, these elements could also pose safety problems to fab personnel. To understand potential risks and sources of these impure elements, a highly cross-functional FMEA team can be deployed to map out all the processes where source chemicals and gases are used along with identifying potential fail modes, severity, occurrence, and detection capabilities. The RPN exercise can be continued to drive improvements at each processing step where such impure elements are likely to occur.

Defect density is defined as the total number of defects calculated per unit area on the wafer die [4]. In order to reduce defect density between processes, engineers need to identify the specific process steps, equipment, input materials, etc. that are the major contributors to the defect density. This involves the construction of a detailed process flow diagram for isolated segments of the process and the use of various problem-solving tools such as using

when required to improve these business processes.

**3.2. Defect process mapping and yield management system**

**3.1. Contamination and control protocols**

**Figure 2.** Bar chart showing wafer contamination source.

30 Applications of Design for Manufacturing and Assembly

This section describes a case study wherein Six Sigma DMAIC methodology was used to tackle a probe yield issue due to an in-line defect contamination occurring in a lithography process step.

#### **4.1. Phase 1: Define**

This phase of the DMAIC methodology aims to define the scope and goals of the improved project in terms of customer requirements and to develop a process that delivers these requirements. The first step toward solving any problem in the Six Sigma methodology is by formulating a team of people associated with the process [12]. For the case study in discussion, suspect of in-line process step was not known, and only the critical business impacting factor of yield loss data was known. The initial team comprised of a Certified Six Sigma Black Belt (CSSBB) who were the site quality engineering manager, yield engineering managers, failure analysis engineers, and technicians along with defect metrology engineers. Roles of the team members are shown in the project charter (**Table 1**). Next, the problem statement addressing CTQ and magnitude of the problem was identified. For a span of 13 weeks, one of the factory's key products had been failing for bin fallout along the edges of the wafer resulting in a yield loss of 7% for dies. Failure analysis team was contacted to perform extensive cross-sectional analysis of the defect location. Based on the information available to the Six Sigma team at this stage, problem statement was defined as follows:

Here, the CTQ metric of die yield loss could directly be correlated to the number of such defects forming during in-line manufacturing that cause poor yields which results in delayed shipments and dissatisfied customers. The Six Sigma team's next tasks will be outlined in the subsequent sections showing measurement, data analysis, root cause drill down, and correc-

Application of Six Sigma in Semiconductor Manufacturing: A Case Study in Yield Improvement

http://dx.doi.org/10.5772/intechopen.81058

In this phase, data correlation was conducted to see the correlation between in-line defect counts per wafer to the number of failing dies per wafer at the final probe and percentage of

It is evident from **Figure 3**, that the correlation between CTQ metric edge die yield losses to

value approaching close to 1. Six Sigma project team must reduce in-line defect count to less

The analysis phase consisted of searching through brainstorming rounds, the possible factors that may be affecting the electrical performance of the product. This stage of the Six Sigma process improvement methodology is often termed as Thought Process Mapping [13] wherein process experts and Six Sigma champions assimilate existing facts and data collected so far and look for initial trends and themes to find clues to go after. The factors that were considered most important were raised as hypotheses and tested by several statistical tests.

Wafer fabrication line is partitioned into three modules—front end of line (FEOL), middle of line (MOL), and back end of line (BEOL)—where each module involves complex steps such as lithography, thin-film depositions, etching, planarization, and diffusion. Inspection sampling plans are strategically placed across several processing steps within these three modules considering cost, cycle time, and wafer throughput times. In this case, the project team was

**Figure 3.** Regression plot of (a) number of failing die per wafer at final probe and (b) percentage edge die yield loss per

33

the number of in-line defect counts occurring per wafer is linear with the high R2

than 50 defects per wafer to have no die yield fallout at the final probe test.

tive action implementation.

edge die yield loss. Results are shown in **Figure 4**.

**4.2. Phase 2: Measure**

**4.3. Phase 3: Analyze**

wafer to number of defects per wafer in-line.

*Problem Statement:* Defects occurring on about 25% of wafers around the edge result in bin failure and die loss on specific technology node.

**Project title:** Reduction of defects for edge die yield improvement on specific technology node wafers

#### **Business case for selecting the project:**