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- Herausgeber: John Wiley & Sons
- Kategorie: Wissenschaft und neue Technologien
- Sprache: Englisch
INTRELLIGENT MANUFACTURING MANAGEMENT SYSTEMS The book explores the latest manufacturing techniques in relation to AI and evolutionary algorithms that can monitor and control the manufacturing environment. The concepts that pertain to the application of digital evolutionary technologies in the sphere of industrial engineering and manufacturing are presented in this book. A few chapters demonstrate stepwise discussion, case studies, structured literature review, rigorous experimentation results, and applications. Further chapters address the challenges encountered by industries in integrating these digital technologies into their operational activities, as well as the opportunities for this integration. In addition, the reader will find: * Systemic explanations of the unique characteristics of big data, cloud computing, and AI used for decision-making in intelligent production systems; * Highlights of the current and highly relevant topics in manufacturing management; * Structured presentations resolving the issues being faced by many real-world applications in a broad range of areas such as smart supply chains, knowledge management, intelligent inventory management, IoT adoption in manufacturing management, and more; * Intelligent techniques for sustainable practices in industrial waste management. Audience The book will be used by researchers, industry engineers, and data scientists/AI specialists working in industrial engineering, mechanical engineering, production engineering, manufacturing engineering, and operations and supply chain management. The book will also be valuable to the service sector industry, such as logistics and those implementing smart cities.
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Table of Contents
Cover
Series Page
Title Page
Copyright Page
Preface
Part I: SMART TECHNOLOGIES IN MANUFACTURING
1 Smart Manufacturing Systems for Industry 4.0
Abbreviations
1.1 Introduction
1.2 Research Methodology
1.3 Pillars of Smart Manufacturing
1.4 Enablers and Their Applications
1.5 Assessment of Smart Manufacturing Systems
1.6 Challenges in Implementation of Smart Manufacturing Systems
1.7 Implications of the Study for Academicians and Practitioners
1.8 Conclusion
References
2 Smart Manufacturing Technologies in Industry 4.0: Challenges and Opportunities
Abbreviations
2.1 Introduction to Smart Manufacturing
2.2 Technology Pillars of Industry 4.0
2.3 Summary and Conclusions
Acknowledgement
References
3 IoT-Based Intelligent Manufacturing System: A Review
3.1 Introduction
3.2 Literature Review
3.3 Research Procedure
3.4 Smart Manufacturing
3.5 Academia Industry Collaboration
3.6 Conclusions
References
4 3D Printing Technology in Smart Manufacturing Systems for Efficient Production Process
Abbreviations
4.1 Introduction and Literature Reviews
4.2 Network in Smart Manufacturing System
4.3 Data Drives in Smart Manufacturing
4.4 Manufacturing of Product Through 3D Printing Process
4.5 Conclusion
References
5 Smart Inventory Control: Proposed Framework on Basis of IoT, RFID, and Supply Chain Management
5.1 Introduction
5.2 Objectives
5.3 Research Methodology
5.4 Literature Review
5.5 Components of SIM
5.6 Framework
5.7 Optimization
5.8 Results and Discussion
5.9 A Mirror to Researchers and Managers
5.10 Conclusions
5.11 Future Scope
References
6 Application of Machine Learning in the Machining Processes: Future Perspective Towards Industry 4.0
6.1 Introduction
6.2 Machine Learning
6.3 Smart Factory
6.4 Intelligent Machining
6.5 Machine Learning Processes Used in Machining Process
6.6 Performance Improvement of Machine Structure Using Machine Learning
6.7 Conclusions
References
7 Intelligent Machine Learning and Deep Learning Techniques for Bearings Fault Detection and Decision-Making Strategies
Abbreviations
7.1 Introduction
7.2 Literature Review
7.3 Methodology
7.4 Analysis
7.5 Results and Discussion
7.6 Conclusions
7.7 Scope of Future Work
References
8 Smart Vision-Based Sensing and Monitoring of Power Plants for a Clean Environment
8.1 Introduction
8.2 Literature Review
8.3 Materials and Methods
8.4 Results and Discussion
8.5 Conclusion
References
9 Implementation of FEM and Machine Learning Algorithms in the Design and Manufacturing of Laminated Composite Plate
Abbreviations
9.1 Introduction
9.2 Numerical Experimentation Program
9.3 Discussion of the Results
9.4 Conclusion
Acknowledgements
References
Part II: INTEGRATION OF DIGITAL TECHNOLOGIES TO OPERATIONS
10 Edge Computing-Based Conditional Monitoring
10.1 Introduction
10.2 Literature Review
10.3 Edge Computing
10.4 Methodology
10.5 Discussion
10.6 Conclusion
References
11 Optimization Methodologies in Intelligent Manufacturing Systems: Application and Challenges
11.1 Introduction
11.2 Literature Review
11.3 Intelligent Manufacturing System Framework
11.4 Bayesian Networks (BNs)
11.5 Problems of Implementing Machine Learning in Manufacturing
11.6 Conclusions
References
12 Challenges of Warehouse Management Towards Smart Manufacturing: A Case of an Indian Consumer Electrical Company
12.1 Introduction
12.2 Literature Review
12.3 The Proposed ISM Methodology
12.4 Results and Discussion
12.5 Practical Implications
12.6 Conclusions
References
13 The Impact of Organizational Ergonomics on Teaching Rapid Prototyping
Abbreviations
13.1 Introduction
13.2 Organizational Ergonomics
13.3 Rapid Prototyping and Teaching Rapid Prototyping
13.4 Industry 4.0 Factors Associated with Organizational Ergonomics in a Rapid Prototyping/Manufacturing Facility
13.5 Implications of Industry 4.0 on Prototyping and Prototyping Facilities in Design Schools
13.6 The Influence of Cooperative Working Ergonomics of Distributed Manufacturing in Teaching and Learning Rapid Prototyping
13.7 Health and Safety in Rapid Prototyping Laboratories
13.8 Impact of Digital/Rapid Prototyping on Organizational Ergonomics
13.9 Implications of the Study for Academicians and Practitioners
13.10 Conclusions and Future Work
References
14 Sustainable Manufacturing Practices through Additive Manufacturing: A Case Study on a Can-Making Manufacturer
14.1 Introduction
14.2 Literature Review
14.3 Research Set Up
14.4 Additive Manufacturing Techniques
14.5 Strategies Used by Production Company
14.6 Sustainable Manufacturing
14.7 Sustainable Additive Manufacturing
14.8 Additive Manufacturing with IFC CMD: A Case Study
14.9 Contribution of Additive Manufacturing Towards Sustainability
14.10 Limitations of Additive Manufacturing
14.11 Conclusions and Recommendations
References
Index
End User License Agreement
List of Tables
Chapter 7
Table 7.1 Specifications of the equipment used.
Table 7.2 Specifications of the equipment used.
Table 7.3 Classification of faults.
Table 7.4 Features extracted.
Table 7.5 Classification report of decision tree (Load 1).
Table 7.6 Classification report of random forest model (Load 1).
Table 7.7 Classification report for KNN (optimistic).
Table 7.8 Classification report for logistic regression.
Table 7.8 Tuned hyper-parameters for SVM.
Table 7.9 SVM classification report (Load 1).
Chapter 8
Table 8.1 Flame image for combustion categories (for validation).
Table 8.2 Performance evaluation using BPA.
Table 8.3 Performance evaluation using BPA+ACO.
Chapter 9
Table 9.1 Validation of central deflection values of laminated composite plate...
Table 9.2 Input parameters and their selected levels.
Table 9.3 Numerically prepared dataset [nonlinear nondimensional central defle...
Table 9.4 Ultimate input, hidden layers and hyperparameters of the ANN model.
Table 9.5
R
_scores for the combinations train-test split of the DTR model.
Table 9.6
R
_scores for the combinations train-test split of the RFR model.
Table 9.7
R
_scores for the combinations train-test split of the LightGBMR mode...
Table 9.8 Algorithms with tuned hyperparameters used and their respective accu...
Chapter 11
Table 11.1 Types of optimization problems.
Chapter 12
Table 12.1 Adaptation of ISM in other industry.
Table 12.2 Factors with their notations.
Table 12.3 Structural self-interaction matrix (SSIM).
Table 12.4 Reachability matrix.
Table 12.5 Level partition.
Chapter 13
Table 13.1 Potential hazards associated with 3D printing.
List of Illustrations
Chapter 1
Figure 1.1 A schematic representation of the research methodology.
Figure 1.2 Framework and enablers of smart manufacturing systems.
Chapter 2
Figure 2.1 Four generations of industrial revolutions [2, 3].
Figure 2.2 Technology pillars of Industry 4.0 [12–14].
Figure 2.3 Automation in a car assembly line.
Figure 2.4 Degrees of freedom of a typical robot arm [15–17].
Figure 2.5 General process steps in 3D printing [22, 23].
Figure 2.6 Big Data analytics [26].
Figure 2.7 Cloud computing [27].
Figure 2.8 Augmented and virtual reality [27].
Figure 2.9 Internet of Things [41, 42].
Chapter 3
Figure 3.1 Necessary components of IoT framework [Source:12] Tao
et al
.
Figure 3.2 Functional domains of IIS.
Figure 3.3 Domain controlling operations.
Figure 3.4 Reconfigurable manufacturing system.
Figure 3.5 Machine control.
Chapter 4
Figure 4.1 Manufacturing unit and IT collaboration.
Figure 4.2 Smart manufacturing and management.
Figure 4.3 3D printing process.
Figure 4.4 Steps involved in 3D printing technology.
Figure 4.5 3D printing applications.
Figure 4.6 3D printing process layout.
Figure 4.7 (a) Honeycomb structure model. (b) Geometrical parameter of honeyco...
Chapter 5
Figure 5.1 Supply management system.
Figure 5.2 Proposed framework.
Chapter 6
Figure 6.1 Machine learning problem-solving steps.
Figure 6.2 Classification of machine learning.
Figure 6.3 4 layer smart factory concept [15].
Figure 6.4 Representation of Industry 4.0.
Chapter 7
Figure 7.1 Brief flowchart of methodology.
Figure 7.2 Experimental setup.
Figure 7.3 Logistic regression curve fitting.
Figure 7.4 KNN working.
Figure 7.5 Decision tree algorithm structure.
Figure 7.6 Effect of the value of gamma in SVM.
Figure 7.7 Effect of the value of regularization in SVM.
Figure 7.8 SVM working and the decision boundary.
Figure 7.9 Decision tree structure in random forest.
Figure 7.10 Confusion matrices for decision tree model. (a) Test confusion mat...
Figure 7.11 Test confusion matrices for random forest algorithm with n optimis...
Figure 7.12 Feature importance for all features employed.
Figure 7.13 Error rate vs K value for KNN (Load 1).
Figure 7.14 Test confusion matrices of KNN. (a) Test confusion matrix. (b) Tes...
Figure 7.15 Test confusion matrices for logistic regression. (a) Test confusio...
Figure 7.16 Test confusion matrices for SVM (Load 1). (a) Test confusion matri...
Figure 7.17 Accuracies of all the models.
Figure 7.18 Average accuracies of all algorithms.
Figure 7.19 Average precision and recall of all models.
Chapter 8
Figure 8.1 Development of image.
Figure 8.2 Formation of image on RGB plane.
Figure 8.3 A typical single-shaft gas turbine.
Figure 8.4 Chemical reactions during the less air.
Figure 8.5 Chart diagram for intelligent flame image analysis.
Figure 8.6 (a) Existing organization for flame monitoring system at Neyveli Li...
Figure 8.7 The effect of various filtering performances on the corrupted image...
Figure 8.8 Edge detection using Sobel operator. (a) Gray scale image. (b) Afte...
Figure 8.9 (a) Design for multiple BPA. (b) Design for multiple RBF. (c) Desig...
Figure 8.10 Curvelet transform flowchart.
Figure 8.11 (a) Function flow map for BPA-trained feed forward neural network....
Figure 8.12 (a) Histogram analysis for flame image for complete combustion. (b...
Figure 8.13 (a) Surface plot of the full combustion flame. (b) Surface flame p...
Figure 8.14 Bi-dimensional pattern distribution for Set 1, Set 2, and Set 3 im...
Figure 8.15 (a) CO emission prediction by PRBFBPA. (b) CO
2
emission prediction...
Figure 8.16 (a) Prediction of NO
x
emissions. (b) Prediction of SOx emissions. ...
Figure 8.17 Performance metrics for BPA.
Figure 8.18 (a) Estimation of SOx emissions with 7 attributes using BPA. (b) N...
Figure 8.19 (a) SOx pollution calculation using BPA with 10 attributes. (b) NO...
Figure 8.20 Performance metrics for BPA in SO
x
and NO
x
estimation using 7 and ...
Figure 8.21 (a) Estimation of the efficiency of combustion using BPA. (b) NOx ...
Figure 8.22 (a) Results of ACO for combustion quality estimation. (b) Results ...
Figure 8.23 BPA and BPA+ACO efficiency metrics.
Chapter 9
Figure 9.1 Typical geometry, orthogonal coordinate system and the layer segmen...
Figure 9.2 The element used for discretization.
Figure 9.3 Convergence behavior and comparison of nonlinear nondimensional def...
Figure 9.4 Flow chart showing sequence of steps in the present ML regression m...
Figure 9.5 Schematic representation of ANN model.
Figure 9.6 Predicted deflection and actual deflection of (a) train data and (b...
Figure 9.7 Predicted deflection and actual deflection of (a) train data and (b...
Figure 9.8 Predicted deflection and actual deflection of (a) train data and (b...
Figure 9.9 Predicted deflection and actual deflection of (a) train data and (b...
Chapter 10
Figure 10.1 P-F curve.
Figure 10.2 Illustration of the architecture of information data center operat...
Chapter 11
Figure 11.1 Evolution of Industry 4.0 and its changing carriers. [Source: Csal...
Figure 11.2 The five types of intelligent manufacturing system technology.
Figure 11.3 Intelligent manufacturing system framework architecture. [Source: ...
Figure 11.4 Vertical reconciliation framework. [Source: Csaloódi
et al.
[15].]
Figure 11.5 Horizontal reconciliation framework. [Source: Csaloódi
et al.
[15]...
Figure 11.6 Linking optimization to the requirements and characteristics of In...
Figure 11.7 Architecture of knowledge-based digital twin solutions. [Source: C...
Chapter 12
Figure 12.1 Classification of challenges (driver-dependence graph).
Figure 12.2 ISM Model.
Chapter 13
Figure 13.1 Tricycle pedal design from SolidWorks by students of a computer-ai...
Figure 13.2 3D printed headlight torch as an example of hands-on project-based...
Figure 13.3 Triple helix framework.
Chapter 14
Figure 14.1 Flow chart of research set-up.
Figure 14.2 “Three pillars” of sustainability
Figure 14.3 Process flow chart of 3D printing spare parts.
Figure 14.4 Quality Pyramid of additive manufacturing [24]
Figure 14.5 Productivity of IFC, CMD.
Guide
Cover Page
Series Page
Title Page
Copyright Page
Preface
Table of Contents
Begin Reading
Index
WILEY END USER LICENSE AGREEMENT
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Scrivener Publishing100 Cummings Center, Suite 541JBeverly, MA 01915-6106
Publishers at ScrivenerMartin Scrivener ([email protected])Phillip Carmical ([email protected])
Intelligent Manufacturing Management Systems
Operational Applications of Evolutionary Digital Technologies in Mechanical and Industrial Engineering
Edited by
Kamalakanta MuduliV. P. KommulaDevendra K. YadavM. Chithirai Pon Selvan
and
Jayakrishna Kandasamy
This edition first published 2023 by John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, USA and Scrivener Publishing LLC, 100 Cummings Center, Suite 541J, Beverly, MA 01915, USA© 2023 Scrivener Publishing LLCFor more information about Scrivener publications please visit www.scrivenerpublishing.com.
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Library of Congress Cataloging-in-Publication Data
ISBN 978-1-119-83624-7
Cover image: Pixabay.ComCover design by Russell Richardson
Preface
Since the world is no longer reliant on analogue technology, the new standard, which is digital, is centered on the management of data, which has become the equivalent of industrial gold in this century. The examination of this data has a great many applications in the business world, ranging from retail enterprises to medical applications and supply chain management, amongst other areas, and can be used to forecast various aspects of consumer behavior such as product utilization and consumer requirements. With the current improvement in IoT applications, the utilization of data has progressed beyond these fundamental economic applications at this point. Because of this digitization, information is now being shared on a massive scale, to the point where there is now an intelligent information system that connects industry, machines, and even end-users across a wide range of devices, which, when structured, models the physical world.
The collection of a wide variety of datasets, collectively referred to as “big data,” is now much simpler thanks to the widespread use of internet-based technology. In most cases, this data is obtained via social media, shopping data, and the purchasing habits of consumers, among other sources. Understanding behaviors and conducting predictive analysis could both benefit from this information. Using artificial intelligence (AI), large amounts of data may be easily interpreted for the sake of strategic planning. The majority of the machinery and tools used in manufacturing industries come equipped with sensors and make use of the internet for the purposes of monitoring as well as data transfer. Artificial intelligence, with its growing capacity for machine learning, could be combined with these features to drive the manufacturing industry. This could be put to use for a broad variety of economic purposes, including the management of maintenance based on data analysis, the making of decisions, the planning of efficiency improvements, remote management, automation of industrial lines, and data visualization, to mention a few. Because of this, it is clear that even though IoT collects a lot of data from equipment, sensors, and other sources, the large amount of data creates an analytical bottleneck that could be solved by using AI to quickly evaluate and understand the data in real time.
The concepts that pertain to the application of digital evolutionary technologies in the sphere of industrial engineering and manufacturing are presented in this book. A few chapters of the book demonstrate these concepts with stepwise discussion, case studies, structured literature review, rigorous experimentation results, and applications. A few chapters also attempt to address the challenges encountered by industries in integrating these digital technologies into their operational activities as well as the opportunities for this integration.
The ideas and concepts addressed in this book will be useful to professionals, researchers, academics, and undergraduate and graduate students in non-circuit fields, particularly those majoring in mechanical engineering, industrial engineering, and business studies. So, this book offers practicing engineers, stakeholders, and academics a better way to move toward Industry 4.0.
The Editors
February 2023
Part ISMART TECHNOLOGIES IN MANUFACTURING
1Smart Manufacturing Systems for Industry 4.0
Gaijinliu Gangmei1* and Polash Pratim Dutta2
1Center for Interdisciplinary Programs, Indian Institute of Technology, Hyderabad, Kandi, Sangareddy, Telangana, India
2Department of Mechanical Engineering, Tezpur University, Napaam, Sonitpur, Assam, India
Abstract
Manufacturing industries have evolved from using of steam power for mechanization to using of electricity in the past two industrial revolutions. The third industrial revolution was brought about with the application of information technology in manufacturing. Now, it has reached the fourth industrial revolution or Industry 4.0 which is built on inter-connectivity. Smart manufacturing systems play an integral role in moving towards Industry 4.0. The aim of this chapter is to discuss the technologies which supports and contributes to smart manufacturing and to understand its characteristics. Because of the benefits of smart manufacturing, it has attracted various professionals to apply smart manufacturing in their own fields. In this chapter, the applications of smart manufacturing are presented especially in industrial and mechanical engineering. The challenges faced by the industry while implementing smart manufacturing systems has also been mentioned.
Keywords: Industry 4.0, additive manufacturing, smart manufacturing system, big data analytics, machine learning, CPS, IoT, cloud manufacturing
Abbreviations
CPS
Cyber-Physical System
DT
Digital Twin
AI
Artificial Intelligence
NIST
National Institute of Standards and Technology
AM
Additive Manufacturing
CAD
Computer Aided Design
ANN
Artificial Neural Network
IoT
Internet of Things
CNC
Computer Numerical Control
PdM
Predictive Maintenance
AMCoT
Advanced Manufacturing Cloud of Things
RFID
Radio Frequency Identification
GPS
Global Positioning System
GIS
Geographic Information Systems
VR
Virtual Reality
AR
Augmented Reality
SMTS
Smart Machine Tool System
DQN
Deep Q Network
SMKL
Smart Manufacturing Kaizen Level
FAHP
Fuzzy Analytic Hierarchy Process
SMMEs
Small, Medium and Micro-Enterprise
1.1 Introduction
Smart manufacturing systems aid in transforming traditional industry into an intelligent and interconnected manufacturing system through technologies like Cyber-Physical Systems (CPS), Digital Twin (DT), Artificial Intelligence (AI), etc. Due to its network, it enables a shift from centralized to decentralized manufacturing units [1]. Manufacturing technology is merged with information technology via an interface so as to connect the local intelligence with the system intelligence [2]. Smart Manufacturing is mostly about the methods of improving processes and decisions within industrial manufacturing environments [3].
The National Institute of Standards and Technology (NIST) defines smart manufacturing as “fully-integrated, collaborative manufacturing systems that respond in real time to meet changing demands and conditions in the factory, in the supply network, and in customer needs”. Smart manufacturing systems are implemented with the objectives of providing autonomous lean operation and co-development of multi-stakeholder and smart manufacturing systems enterprise by sharing of knowledge and information [4]. Moreover, it should have the characteristics of self-learning, self-optimizing, and adaptability to the change in manufacturing environment [5].
The study on smart manufacturing systems for Industry 4.0 has been carried out to help the readers specially beginners, in understanding the concepts as well as the broad structure of Industry 4.0. It will aid the researchers to advance the work or to implement Industry 4.0 in the manufacturing system. Given the diverse nature of smart manufacturing, existing scientific literature does not provide a clear understanding of this topic as most researchers either work on a specific domain or on a particular enabling technology. Therefore, this study aims at filling this gap by covering the broad scope of smart manufacturing.
This chapter will brief the eight pillars of smart manufacturing which are sustainability, data, manufacturing technology and processes, materials, predictive engineering, resource sharing and networking, stakeholders and standardization [2]. Various cutting-edge technologies and solutions has been developed under each pillar which supports and contributes to smart manufacturing. For simplicity, such enablers will be grouped under five categories i.e., smart machining, smart control, smart design, smart monitoring, and smart scheduling [6]. The enablers will be discussed in detail along with their applications in Industrial and Mechanical Engineering. Assessment of smart manufacturing system as well as the challenges faced by the industry while implementing smart manufacturing systems has also been discussed.
Smart manufacturing is not only about the technologies that are associated with it but it’s about how the data are collected, captured, analyzed and communicated so as to make the best decisions in real-time. Because of the benefits of smart manufacturing like energy efficiency, greater productivity and its ability to tackle the competitiveness of the industry, it is applied in the fields of aviation, manufacturing, healthcare, building management, automotive industry, etc. The chapter will finally end with the implications of the study for academicians and practitioners, followed by concluding remarks and contribution of this chapter.
1.2 Research Methodology
The main purpose of this study is to provide a basic knowledge of smart manufacturing system in context of Industry 4.0. In order to obtain that, a systematic mapping process [7] was applied in this study. This research methodology was chosen as it provides a broad overview of the research area.
Figure 1.1 A schematic representation of the research methodology.
The schematic representation of the research methodology used in this study is shown in Figure 1.1. The first step was to identify the search term required for finding the research articles. The research papers were chosen from three leading digital databases; Science Direct, Google Scholar and IEEE Xplore. A total of 77 articles were identified from the four databases. It was followed by processing of papers using a set of inclusion and exclusion criteria. To be included in the study, the search terms should be present in the articles and it should be from an academic source. While the exclusion criteria involve removing duplication from those papers which met the inclusion criteria as well as omitting those papers that does not align with the topic of the study. As a result, 58 papers were found to be relevant for this study.
1.3 Pillars of Smart Manufacturing
The nature of smart manufacturing can be expressed across eight pillars [2]. Each pillar is described in the following section.
1.3.1 Manufacturing Technology and Processes
A manufacturing unit with intelligent control technology is reliable as it gives accurate products and can quickly respond to changes namely, market changes, fluctuations in load, and uncertain conditions, etc. [8]. Smart manufacturing technology like additive manufacturing (AM) enables the fabrication of complex component directly from its Computer Aided Design (CAD) models. Because it is a material addition process, parts can be manufactured using exotic materials. Traditional manufacturing has its own advantages which cannot be overlooked. As a result, hybrid of additive and traditional processes will produce a quality product as it will exploit the benefits of both the processes. Manufacturing equipment can be made smarter with an addition of sensor and robots along with integration of several operations.
Manufacturing of precision components from sheet metal coil requires levelling before performing metal forming/cutting process so as to remove the residual stress which was left from the coil. Expert machine technicians choose the machine parameters based on their experiences. So, Tsai et al. [9] digitize the knowledge of expert technicians on coil leveling system through deep learning method, originated from Artificial Neural Network (ANN). ANN is an information processing paradigm where knowledge is acquired through a learning process similar to the way the brain process information [10].
Due to the availability of numerous smart manufacturing solutions, Martin et al. [11] developed a methodology based on value stream mapping method. It supports the production planner to choose the best solution for a given manufacturing system.
1.3.2 Materials
Smart manufacturing utilizes all sort of materials which includes smart materials, organic-based materials, and biomaterials. This enables the engineers to take advantage of their unique properties in creating a component. Smart materials use sensors to detect unwanted changes in environment and operations, while the required corrective actions are carried out by actuators [12]. The sensors used for smart manufacturing systems (CPS, Internet of things (IoT), robot-human interactions) are multi-material sensors as single material sensors are not enough for such applications [13].
Smart manufacturing is not directly involved in the development of novel materials but it led to exploration of new materials as almost all materials could be fabricated. Although there are some materials which require novel processes to be developed for its fabrication. Making use of recycled materials as raw materials will reduce the amount of products which are discarded at the landfills after their end of life.
1.3.3 Data
Data plays a major role in smart manufacturing. It could be considered as the building block of smart manufacturing system. Data are of various kinds such as vibration data, visual data, auditory data, etc. It is collected from diverse sources like sensors, wireless technology, analytics, and so on. The data are being collected for various purposes such as, productivity analysis, building predictive models, preserving and extraction of information related to manufacturing, etc. This ultimately results in tremendous amount of data production. Such large sets of data including real-time data could be analyzed using a technology called big data analytics [8].
The data can also be analyzed using data visualization technique where data are represented by means of graphs or other visual representations. After converting the data into useful knowledge, it could be used to develop decision, predictive and diagnostic models. Moreover, the study of real-world system could be simplified by analyzing the data generated from the simulation of a model.
1.3.4 Sustainability
Sustainability in manufacturing is essential in safeguarding the environment as well as in increasing the resource efficiency. Sustainability is not only about what product is manufactured but it should also include re-manufacturing, product usage impacts, surface restoration, supply chain energy costs, and waste disposal [14].
Sustainability efforts include sustainable product design, using environment friendly materials or biodegradable or recycled materials as raw materials, development of manufacturing processes which consume less energy and release fewer pollutants. As a matter of fact, AM is a sustainable manufacturing process as it generates less scrap and it could perform surface as well as geometry restoration. To reduce energy consumption in Computer Numerical Control (CNC) shop floor for stamping dies, an identical parallel machine scheduling problem was studied by Wang et al. [15] and developed an efficient method. Blömeke et al. [16] identified smart manufacturing technologies and solutions such as, smart bin, automated transport system, pick by vision, and cobots to support companies in carrying out recycling and remanufacturing operations. In a system which involves human-robot collaboration, protective measures could be given to the human component by connecting it with adaptor technologies for tracking safety distance or human position [17].
1.3.5 Resource Sharing and Networking
Resource sharing and networking is one of the capabilities of smart manufacturing systems that set it apart from other manufacturing system. Exchange and sharing of information between the system units enables interoperability within the system [8]. Storing data digitally for instance, in cloud, which behaves like a database, allows sharing of resources across businesses in an instant. In a manufacturing network, computational and physical resource efficiency can be increased using an intelligent cloud manufacturing platform [18]. Adoption of IoT enables smart manufacturing to carry out collaborative modelling, lease manufacturing equipment, share software and expertise, etc.
Transportation in manufacturing, though a non-value-adding activity, is an essential part of manufacturing. Autonomy in material handling, supply and distribution network can be increased with developments in robotics and autonomous vehicles. Whereas sharing of resources digitally will reduce the overall transportation cost.
1.3.6 Predictive Engineering
Predictive engineering provides manufacturing solutions by enabling machines and systems or material handling and transportation vehicles to make their own decisions. This is made possible due to the adoption of smart sensor networks, smart machines and smart vehicles. It utilizes advanced prediction tools for processing the data into information that gives an insight into future performance of the equipment. This will reduce the impact of uncertainties on the quality of manufactured products and services as solutions can be implemented to prevent performance loss [19].
A manufacturing system which responds and recovers autonomously to disturbances in real-time could manage to lower the downtime of the system. But Predictive maintenance (PdM) could be a better process as it involves correcting future critical conditions which are predicted using both current and past machinery data [20]. Implementing PdM reduces the cost associated with defective products and downtime. For PdM, a multiple classifier machine learning methodology was applied on a semiconductor manufacturing Ion-Implanter by Susto et al. [21] for related maintenance related task. It resulted in reduction of operating cost and better performance. A case study was carried out by Lin et al. [22] in a semiconductor company in Taiwan, where a smart manufacturing platform-AMCoT (Advanced Manufacturing Cloud of Things) was applied to a bumping process. It has the capabilities to conduct total inspection, detect the root cause of yield loss, store and handle production data, and provide predictive maintenance on the equipment.
1.3.7 Stakeholders
Smart manufacturing stakeholders consists of manufacturers (managers, employees), suppliers (system integrators, material, software, energy and hardware suppliers), communities (local and government communities), and customers. With an advancement in technology like AI, cloud computing and big data, stakeholders’ requirement could be predicted and fulfilled [23]. In smart manufacturing system, co-development of enterprise and its multi-stakeholder is a must [4]. Smart manufacturing system will evolve depending on the optimization of stakeholders’ value.
Intelligent tracking technologies like Radio Frequency Identification (RFID), Global Positioning Systems (GPS), wireless telecommunications, and Geographic Information Systems (GIS) improves customer services. Using such technology in supply chain provides accurate information and a visual experience to the related stakeholders. Moreover, stakeholders are involved in the creation of sustainable products and services. Due to the manufacturing system moving towards automation, reduction in jobs for low-skilled laborers will indeed take place. Conversely, demand for employees with competencies in software development and IT technologies will rise due to the increasing use of analytics, software, and connectivity [24].
1.3.8 Standardization
Standards are fundamental for the implementation and development of smart manufacturing system. Availability of standards eliminate the loss cause by repetition of research. In addition to that, developing common standards is useful to form or to operate smart manufacturing supply networks. Around 30 standards related to smart manufacturing systems have been published [25]. Standards come in varieties so as to enable the various capabilities of smart manufacturing system [26]. The standard of smart manufacturing system includes [27]:
Smart design standards which deal with design activities and data management.
Smart production standards focused on the working processes.
Business operation and management standards focused on management activities for design and production.
System integration standards which deal with the technologies that integrate systems of different domains.
Fundamental technologies and supporting environment standards focused on common supporting technologies.
1.4 Enablers and Their Applications
The goals of smart manufacturing are achieved due to the development of cutting-edge technologies and solutions especially in the area of cyber world and networking. Prevalent enablers of smart manufacturing system are grouped within the framework of Industry 4.0 smart manufacturing systems [6] which includes smart machining, smart design, smart control, smart monitoring, and smart scheduling as shown in Figure 1.2.
Figure 1.2 Framework and enablers of smart manufacturing systems.
1.4.1 Smart Design
Advancement in technologies like Virtual Reality (VR) and Augmented Reality (AR) has enabled smart design. VR simulates a real environment in a virtual world while AR is an interface of digital and real environment [28]. They both facilitate product design visualization and improves decision-making. Sensors are used to get real time performance of a prototype like a personalized smart wearable device [6]. While, user’s experience can be recorded via VR headset or eye tracker. VR technology could be used to perform remote inspection of manufacturing machines [29].
After collection of data, data analytics can be carried out and subsequently, the required improvement in CAD model could be carried out. The fabrication of customized parts could be produced with AM. AM is a material addition process which uses CAD data to create a solid part [30]. For design optimization and evaluations of a component, using rapid virtual prototyping tool gives better design compared to manual improvement. Using such tool, a ship design process was optimized [31].
1.4.2 Smart Machining
Smart manufacturing, which is a part of industry 4.0 [32] has enabling technologies such as IoT, CPS, and DT that push the existing machining technologies towards smart machining. CPS is an automated system which is an integration of physical processes, computation, and networking within the embedded systems or through the internet. IoT refers to a CPS which is connected to the Internet [33] and DT is a digital representation and computerized part of a physical system. DT uses real world data to replicate processes or product of a physical system. The data flow between the digital and physical object are completely integrated, i.e., a change in the state of one object will simultaneously lead to a change in the state of the other object [34].
Liu and Xu [35] proposed Machine Tool 4.0 which is an integration of CPS, IoT and cloud computing. It can control and monitor the machining processes without human intervention. Similarly, Jeon et al. [36] proposed another machine tool architecture called Smart Machine Tool System (SMTS) and applied this system in the rotor manufacturing industry. The system was based on cyber-physical manufacturing systems; the physical system consists of the shop floor while the cyber system consists of big data analytics, DT, and AI, which supports the operation of the physical system. AI can mimic human experts by using the knowledge obtained during the learning process of solving problems. AI-based techniques could be implemented to obtain the optimum cutting parameters of CNC machining [37].
1.4.3 Smart Monitoring
Mostly, sensors are responsible for realizing smart monitoring. Sensors are of various types such as pressure sensors, thermo sensors, vision sensors, vibration sensors and so on. It could be used in tracking the state of a system in real time and to send alerts when abnormality occurs in the system. For the purpose of continuous or intermittent tracking in real time, RFID technology could be employed which can identify an object from a distance [38]. Whereas, an audio signal processing approach and machine learning has been used to monitor the tool wear of milling operations in real-time by Li et al. [39]. Machine learning techniques are a subset of AI that enables a machine to perform or improve a particular task automatically through experience or from historical data [40]. Machine learning could be applied in flight-data monitoring system for predicting the failure of aircraft components [41].
Regular monitoring of production healthiness increases the productivity as any potential manufacturing disaster would be prevented. For such purpose, a smart production healthiness monitoring system was developed by Ang et al. [42] using ANN model. Cloud manufacturing which is based on networks [43] has been implemented for machine tool monitoring, process monitoring, planning and control. It connects the cloud computing and services with the physical resources. Furthermore, for smart process monitoring in machining, a cloud manufacturing framework was developed by Caggiano et al. [44].
1.4.4 Smart Control
As manufacturing industry advances toward smart manufacturing, the control system of a machine tool has to possess advanced features such as adaptive control, position dependent process control, intelligent process control, etc. [45]. The control system could also be upgraded to smart control owning to development of cloud manufacturing environment. As the machines and operator are connected via the cloud, the operator can control the machine or robots anywhere and at any given time using smart phones. Not only the control of the process and the machine tool, Sprock and McGinnis [46] designed a model to control the smart manufacturing systems at the management level.
Integration of CPS, AI, and Industrial robots enables autonomy and adaptability by carrying out operations and incorporating any required changes without human intervention. In shop-floor, human robot interaction is made possible with the help of image recognition and sensors [47]. According to Hinchy et al. [48], an open source low-cost microcontroller could be used to control machine operations as well as for data communication between a V-bending machine and its digital twin.
1.4.5 Smart Scheduling
Scheduling involves assigning the appropriate production resources or processes in a workflow. Sensors are employed in the shop floor to get real-time information and the large amount of data captured from sensors could be analyzed using big data analytics. The information and knowledge obtained from big data analytics helps in decision making to achieve smart scheduling.
Machine learning techniques can aid in solving scheduling problems as it can make decisions like expert human schedulers. A deep reinforcement learning-based method was developed by Zhou et al. [49] to solve the dynamic service scheduling problem. It is an improvement of reinforcement learning where the intelligent policy is constructed using deep neural network. Reinforcement learning is one of the algorithms of machine learning. As it does not depend on dataset to train the model, it is mostly suitable for decision-making problems which are random in nature. Similarly, job shop scheduling problems was solved by Lin et al. [50] using a multiclass deep Q network (DQN), which is a combination of deep learning and reinforcement learning. Whereas, Rossit et al. [51] solved the production scheduling problems by developing a smart approach based on CPS. It has the ability to reprogram schedules on encountering unforeseen or disruptive events such as machine breakdown or unavailability.
1.5 Assessment of Smart Manufacturing Systems
After implementation of smart manufacturing system, its competency, capabilities, and impact could be measured using maturity model. A maturity model could assess the readiness of manufacturing companies to implement smart manufacturing. A maturity model named SMKL (Smart Manufacturing Kaizen Level) which focuses on smart manufacturing system was developed by Shi et al. [52] for sustainable factory automation. It assessed the maturity levels according to the management level. The four management level consists of installation or worker, work station, factory, and supply chain. With an alignment to the management level, four maturity levels were developed namely, collecting, visualizing, analyzing, and optimizing. The capabilities of the system depend upon the evaluation of the maturity level. Similarly, Yoo et al. [53] defined six maturity levels of smart manufacturing which includes- No assessment, Identified, Measured, Analyzed, Optimized, and Customized. It assessed the extent to which traditional manufacturing has adopted information communication technologies. Furthermore, assessment indicators were created as a form of questionnaires for the decision of maturity level. Another method of determining the maturity level is to identify the contribution of each maturity item using Fuzzy Analytic Hierarchy Process (FAHP) which is a multi-criteria decision-making technique [54].
Smart Manufacturing Maturity Model for small and medium-sized Enterprise has been developed by Mittal et al. [55] to support and realized smart manufacturing capabilities. The model can be considered as a three-axis model; organizational dimensions (X-axis), complementary toolboxes (Y-axis), and maturity levels (Z-axis). The model consists of five maturity levels namely, novice, beginner, learner, intermediate, and expert. Small and medium-sized enterprise would reach the next level of maturity by identifying the required input or support within the five organizational dimensions i.e., finance, people, strategy, process, and product. Within each dimension, a toolkit enables a small and medium-sized enterprise to perform more sophisticated activities to reach the final goal. It consists of manufacturing/fabrication toolbox, design and simulation toolbox, robotics and automation toolbox, sensors and connectivity toolbox, cloud/storage toolbox, data analytics toolbox, and business management toolbox. A case study was carried out on Taiwan enterprises to assess smart manufacturing readiness using a maturity model [56]. The model was based on the economic development board’s Singapore smart industry readiness index. Self-assessments of companies using this model allow alignment of companies with smart manufacturing system. Maturity model is an essential tool to analyze the current level of maturity of smart manufacturing and accordingly, the level of maturity and manufacturing capability could be improved. It reduces the risk of transitioning into smart manufacturing as it could prioritize the right technologies for investment.
1.6 Challenges in Implementation of Smart Manufacturing Systems
Smart manufacturing systems are being adopted because of its capability to overcome the challenges of existing industry. But as it makes use of new technologies, there exist some challenges [25] during its implementation. The identified challenges are structured in two main categories [17] which are mentioned below.
1.6.1 Technological Issue
Interoperability: Smart manufacturing system involves integration of various technologies where the software or hardware can be designed by various companies. So, there is a need of common standards and communication protocols in order to obtain interoperability for the exchange of data and information between them.
Cybersecurity: In smart manufacturing environment, integrated network system (mostly internet) is use for sharing information and knowledge. Cyber-attacks can affect design parameters, disrupt design files or ruin the equipment [
57
]. In AM, malicious cyber-attacks can alter or copy the design of the model and affect the performance of the printer. To avoid such attacks, the entire system needs to be secured with global unique identification and end-to-end data encryption when information is shared through the internet.
Handling data growth: Smart manufacturing generates an immense amount of data as data are required for every decision making process. So, there is a need to ensure the integrity and quality of the data that has been captured or communicated [
17
].
System integration: Integrating new technologies with an old machine tool is a challenge as they use different software and communication protocols. According to Gumbi and Twinomurinzi [
58
], Small, Medium and Micro-Enterprise (SMMEs) are incompatible with smart manufacturing due to insufficient resources, expertise, and skills. Moreover, replacing an existing machine with a new one is expensive.
1.6.2 Methodological Issue
Reference models: Smart manufacturing, being a combination of various domains, such as mechanical, electrical, electronics, computer science etc., has variety of technical standards from various disciplines. So, there is a need of reference models which establish a clear description of fundamental concepts.
Requirement engineering: For smart manufacturing development, requirement engineering need to be able to handle the continually changing user requirements, system requirements, and stakeholder demands.
Skills gap: With an upgrade in the manufacturing system, the employees need to gain expertise in both the areas of digital tools and manufacturing process of the new system. Learning of current decision making process and update of behavior rules are also required. This can be achieved by training the existing staff or hiring new talent.
1.7 Implications of the Study for Academicians and Practitioners
Smart manufacturing involves integration of various domains and technologies which are still evolving. As a result, new theories or new definitions are emerging. This can be confusing to an individual who is new to this topic. To solve this problem, the current study was carried out which summarizes the broad research topic without going too detailed for easy understandability. It provides a strong basis to academicians and practitioners to start their research or investigation on a specific area within this topic. It will also enable them to focus on one specific application of a particular technology.
The findings of maturity model could be used as a guideline for implementation or improving the capabilities of smart manufacturing system. The impact of smart manufacturing system on economy, education, employment, and productivity could be studied as well. To realize the full capabilities of smart manufacturing, existing standards could be extended or new standards could be developed. Additionally, protocols for communication between smart manufacturing systems could be developed.
1.8 Conclusion
Smart manufacturing is not about a single process or factory, but it involves the entire enterprise. The present chapter provides an overview of the characteristics and enablers of smart manufacturing systems. The assessment of smart manufacturing system and challenges faced during the implementation of smart manufacturing systems in the industry has been discussed as well.
This study gives a good introduction to smart manufacturing system and it will enable the reader to derive insights that can improve existing manufacturing system. It identified and grouped the existing enablers of smart manufacturing system within the framework.
As manufacturing continue to evolve, new technologies and ideas will emerge. So, the technologies that can be implemented for industry 4.0 are not limited to the enablers that are mentioned in this chapter. With an increase in the adoption of smart manufacturing in industry and academia, more challenges will emerge as well.
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