Advances in Mathematics for Industry 4.0

✍ Scribed by Mangey Ram

• Publisher: Academic Press
• Year: 2020
• Tongue: English
• Leaves: 408
• Category: Library

✦ Synopsis

Advances in Mathematics for Industry 4.0 examines key tools, techniques, strategies, and methods in engineering applications. By covering the latest knowledge in technology for engineering design and manufacture, chapters provide systematic and comprehensive coverage of key drivers in rapid economic development. Written by leading industry experts, chapter authors explore managing big data in processing information and helping in decision-making, including mathematical and optimization techniques for dealing with large amounts of data in short periods. Focuses on recent research in mathematics applications for Industry 4.0 Provides insights on international and transnational scales Identifies mathematics knowledge gaps for Industry 4.0 Describes fruitful areas for further research in industrial mathematics, including forthcoming international studies and research

✦ Table of Contents

Advances in Mathematics for Industry 4.0
Copyright
Contents
List of contributors
About the editor
Preface
Acknowledgments
1 Trust-enhancing technologies: Blockchain mathematics in the context of Industry 4.0
1.1 Introduction
1.2 Trust for all
1.3 Privacy by design
1.4 Conclusions, pending challenges, and future works
References
2 Optimization techniques to support decision-making processes via MSM—an Industry 4.0 approach
2.1 Introduction
2.2 The multilayer stream mapping approach
2.3 A synopsis of the proposed methodology
2.4 Problem statement—a real case study
2.4.1 The company and the production system
2.4.2 Processing characteristics and job constraints
2.4.3 Manufacturing processes description
2.4.4 Indicators
2.4.4.1 Flow indicators
2.4.4.2 Resources indicators
2.4.4.3 Associated costs
2.5 Production system model
2.5.1 Model description
2.5.2 Practical description
2.5.3 Model applicability
2.6 Optimization technique
2.6.1 Heuristic approaches
2.6.2 Genetic algorithm
2.7 Acquired results
2.7.1 Heuristics
2.7.2 Genetic algorithm strategy
2.7.3 Optimization approaches enforcement
2.7.3.1 Comparison between the applied heuristics and GA
2.7.3.2 Simulating scenarios
2.8 Conclusions
Acknowledgments
References
3 A probabilistic approach to reconfigurable interactive manufacturing and coil winding for Industry 4.0
3.1 Introduction
3.2 Probabilistic framework
3.2.1 Signal processing
3.2.1.1 Smoothing and normalization
3.2.2 Gaussian mixture model
3.2.3 Incremental Gaussian mixture model
3.2.4 Gaussian mixture regression
3.2.5 System effectiveness
3.3 Automatic assembly
3.3.1 Task and system description
3.3.2 Methodology
3.3.2.1 Learning phase
3.3.2.2 Module and door identification
3.3.3 Results
3.4 Manufacturing of electric motors
3.4.1 Task and system description
3.4.1.1 Human–robot interface
3.4.1.2 Lab scenario
3.4.1.3 Industrial scenario
3.4.2 Methodology
3.4.2.1 Pole selection
3.4.2.2 Entrance and exit position estimation
3.4.2.3 Robot movement
3.4.3 Results
3.4.3.1 Lab scenario
3.4.3.2 Industrial scenario
3.5 Conclusions
References
4 The tolerance scheduling problem for maximum lateness in Industry 4.0 systems
4.1 Introduction
4.2 Industry 4.0 production environments
4.2.1 Cyber-physical systems
4.2.2 Cyber-physical production systems
4.2.3 Decision-making in cyber-physical production systems
4.3 Scheduling
4.4 The tolerance scheduling problem
4.4.1 Dynamic scheduling
4.4.2 Inverse scheduling
4.4.3 Tolerance scheduling
4.5 Application case: single machine scheduling minimizing Lmax
4.6 Conclusions
References
5 Digitalization and security: a new challenge for Mathematics 4.0
5.1 Introduction
5.2 The role of mathematics in Industry 4.0—a special kind of decision-making
5.3 The mathematics of data science—challenges to Industry 4.0
5.3.1 Making decisions—preparation of data
5.4 Digitalization and security
5.4.1 Challenges and opportunities in the course of intelligent process optimization
5.4.2 A duck tale and Industry 4.0
5.5 What do we mean by “digitalization”?
5.6 Efficiency enhancement and added value through digitalization
5.7 Digital distribution channels
5.8 Digital economy and Industry 4.0
5.8.1 Strategic digitalization—intelligent tracking and complex security
5.8.2 Interaction and intelligent overall system
5.9 Digital processes as a supreme discipline—computerization versus digitalization
5.10 Maturity models
5.11 Other success stories of mathematics and Industry 4.0
5.12 General technological advantages
5.12.1 Cloud computing
5.13 Success stories in companies
5.13.1 Bayer biopharmaceutical located in Garbagnate, Italy: the correct use of data
5.13.2 Haier located in Qingdao, China: predicted maintenance needs
5.13.3 Phoenic contact located in Bad Pyrmont and Blomberg, Germany: digital twins
5.13.4 Siemens located in Chengdu, China: augmented reality
5.13.5 NX: a platform for the development of solutions
5.13.6 Bosch Automotive in Wuxi, China
5.13.7 Summary
5.14 Outlook: mathematics and Industry 4.0—digitalization and security
5.14.1 Digitization and security
5.14.1.1 A new challenge for Mathematics 4.0
References
Further reading
6 Proposal and application of a framework to measure the degree of maturity in Quality 4.0: A multiple case study
6.1 Introduction
6.2 Industry 4.0: Pillars and perspectives of integration
6.3 Quality 4.0: Alignment of quality in the new scenario of the Fourth Industrial Revolution
6.3.1 Quality 4.0 organizational dimensions
6.3.1.1 Data
6.3.1.2 Analytics
6.3.1.3 Connectivity
6.3.1.4 Collaboration
6.3.1.5 App development
6.3.1.6 Scalability
6.3.1.7 Management system
6.3.1.8 Compliance
6.3.1.9 Culture
6.3.1.10 Leadership
6.3.1.11 Competence
6.4 Framework development to assess the organization’s Quality 4.0 maturity
6.4.1 Systematic application and data treatment based on a numerical approach
6.5 Quality 4.0: Multiple case study
6.5.1 Quality 4.0 maturity in the automotive industry
6.5.2 Quality 4.0 maturity in the energy industry
6.5.3 Weaknesses and potentialities identified
6.6 Comments and future perspectives
References
7 Intelligent manufacturing as a social institute: Internal and external regulation
7.1 Introduction
7.2 Literature review
7.3 Materials and method
7.4 Results
7.4.1 Evaluation of the effectiveness of the existing practices of internal and external stimulation of the development of ...
7.4.2 Innovative practices of stimulation of the development of intellectual production for its quick and successful instit...
7.4.3 Modeling of the institutionalization of intellectual production and practical recommendations (policy implications)
7.5 Conclusions
Acknowledgments
References
8 Production planning and supply chain management under the conditions of Industry 4.0
8.1 Introduction
8.2 Literature review
8.3 Materials and method
8.4 Results
8.4.1 Regional models of production planning and supply chain management in the modern global economy
8.4.2 Perspective model of production planning and supply chain management under the conditions of Industry 4.0
8.4.3 Adapting the perspective model of production planning and supply chain management under the conditions of Industry 4....
8.5 Conclusions
Acknowledgment
References
9 Infrastructural provision and organization of production on the basis of the Internet of Things
9.1 Introduction
9.2 Literature review
9.3 Materials and method
9.4 Results
9.4.1 The essence and specific features of the Fourth Industrial Revolution and advantages of automatized production on the...
9.4.2 Modeling of production and distribution processes in the Internet economy during automatized production on the basis ...
9.4.3 Infrastructural provision of automatized production on the basis of the Internet of Things: Specific features, defici...
9.5 Conclusions
Acknowledgment
References
10 Artificial intelligence as the core of production of the future: Machine learning and intellectual decision supports
10.1 Introduction
10.2 Literature review
10.3 Materials and method
10.4 Results
10.4.1 The new economic practice in the sphere of the development of artificial intelligence
10.4.2 Scenarios of digital modernization of the modern economy depending on the functions of artificial intelligence in th...
10.4.3 Algorithms of artificial intelligence training for the execution of various functions within the compiled scenarios
10.5 Conclusions
Acknowledgment
References
11 Active digital manufacturing: Conceptual foundations and practical solutions
11.1 Introduction
11.2 Literature review
11.3 Materials and method
11.4 Results
11.4.1 The scientific concept of active digital manufacturing, its principles, priorities, and differences from the concept...
11.4.2 Comparative analysis of the advantages from the development of passive and active digital manufacturing
11.4.3 Current problems of starting and implementing active digital manufacturing and their perspective solutions
11.5 Conclusions
Acknowledgment
References
12 Big Data management and data analysis: Applied solutions in view of the spheres of the modern economy
12.1 Introduction
12.2 Literature review
12.3 Materials and method
12.4 Results
12.4.1 Evaluation of the sufficiency of the existing statistical data bases and tools for processing of large arrays of sta...
12.4.2 The conceptual model of application of breakthrough digital technologies of management and Big Data analysis for sta...
12.4.3 The algorithm of statistical accounting and analytics for various spheres of a modern economy in the conditions of I...
12.5 Conclusions
References
13 Infusion–diffusion process-based modeling and profit estimation for manufacturing industries
13.1 Introduction
13.2 Role of warranty in determining overall profit: A literature review
13.3 Formulation of sales function
13.4 Optimization problem formulation
13.4.1 Manufacturing cost modeling
13.4.1.1 Model for quantity produced
13.4.1.2 Manufacturing cost
13.4.2 Warranty cost modeling
13.4.2.1 Possible number of complaints
13.4.2.2 Warranty probability
13.4.2.3 Actual number of complaints
13.4.2.4 Complaint factor
13.4.3 Formulation of warranty probability in terms of product performance and customer expectation
13.4.4 Problem formulation for optimal profit
13.5 Numerical illustration
13.6 Managerial implications
13.7 Conclusions
References
14 Application of AHP in evaluating the financial performance of industries
14.1 Introduction
14.2 Financial ratios
14.2.1 Liquidity ratios
14.2.2 Financial leverage ratios
14.2.3 Profitability ratios
14.2.4 Growth ratios
14.3 Methodology
14.3.1 Multicriteria decision-making
14.3.2 Analytical hierarchy process
14.4 Experiments and results
14.4.1 Data analysis
14.4.1.1 Criteria level
14.4.1.2 Subcriteria level
14.5 Discussion and conclusions
References
15 Application of Internet of Things-aided simulation and digital twin technology in smart manufacturing
15.1 Introduction
15.2 Smart manufacturing systems and Industry 4.0 technologies—a glimpse
15.2.1 Autonomous mobile robots
15.2.2 The industrial Internet of Things
15.2.3 Additive manufacturing
15.2.3.1 Additive manufacturing processes
15.2.3.2 Additive manufacturing technologies
15.2.3.3 Additive manufacturing materials
15.2.4 Augmented reality
15.2.4.1 Augmented reality for product design, inspection, and maintenance
15.2.4.2 Augmented reality for upskilling and productivity
15.2.4.3 Augmented reality for quality assurance
15.2.5 Simulation and virtual reality
15.2.6 Cloud computing
15.2.7 Big Data and analytics
15.2.7.1 Big Data analytics technologies and tools
15.2.7.2 How Big Data analytics works
15.3 Digital twin-driven smart manufacturing
15.3.1 A reference model for the digital twin
15.4 Creation of a digital twin in a smart manufacturing system
15.5 Summary
References
16 Mathematical models for the dimensional accuracy of products generated by additive manufacturing
16.1 Introduction to dimensional quality in additive manufacturing
16.2 Main factors for dimensional accuracy in additive manufacturing
16.2.1 Effects of layer thickness and surface orientation
16.2.2 Effects of extruder errors
16.2.3 Effects of material shrinkage and beam offset
16.3 Mathematical modeling of dimensional deviations in additive manufacturing
16.3.1 Dimensional deviations in additive manufacturing-generated parts
16.3.2 Surface roughness in additive manufacturing-generated parts
16.4 Accuracy improvement in additive manufacturing by optimization or compensation techniques
16.4.1 Optimization of part orientation in additive manufacturing
16.4.2 Compensation of extruder errors in additive manufacturing
16.4.3 Compensation of shrinkage effect and beam offset
16.5 Conclusions
References
Index