Advances in Mathematics for Industry 4.0

Advances in Mathematics for Industry 4.0\nCopyright\nContents\nList of contributors\nAbout the editor\nPreface\nAcknowledgments\n1 Trust-enhancing technologies: Blockchain mathematics in the context of Industry 4.0\n	1.1 Introduction\n	1.2 Trust for all\n	1.3 Privacy by design\n	1.4 Conclusions, pending challenges, and future works\n	References\n2 Optimization techniques to support decision-making processes via MSM—an Industry 4.0 approach\n	2.1 Introduction\n	2.2 The multilayer stream mapping approach\n	2.3 A synopsis of the proposed methodology\n	2.4 Problem statement—a real case study\n		2.4.1 The company and the production system\n		2.4.2 Processing characteristics and job constraints\n		2.4.3 Manufacturing processes description\n		2.4.4 Indicators\n			2.4.4.1 Flow indicators\n			2.4.4.2 Resources indicators\n			2.4.4.3 Associated costs\n	2.5 Production system model\n		2.5.1 Model description\n		2.5.2 Practical description\n		2.5.3 Model applicability\n	2.6 Optimization technique\n		2.6.1 Heuristic approaches\n		2.6.2 Genetic algorithm\n	2.7 Acquired results\n		2.7.1 Heuristics\n		2.7.2 Genetic algorithm strategy\n		2.7.3 Optimization approaches enforcement\n			2.7.3.1 Comparison between the applied heuristics and GA\n			2.7.3.2 Simulating scenarios\n	2.8 Conclusions\n	Acknowledgments\n	References\n3 A probabilistic approach to reconfigurable interactive manufacturing and coil winding for Industry 4.0\n	3.1 Introduction\n	3.2 Probabilistic framework\n		3.2.1 Signal processing\n			3.2.1.1 Smoothing and normalization\n		3.2.2 Gaussian mixture model\n		3.2.3 Incremental Gaussian mixture model\n		3.2.4 Gaussian mixture regression\n		3.2.5 System effectiveness\n	3.3 Automatic assembly\n		3.3.1 Task and system description\n		3.3.2 Methodology\n			3.3.2.1 Learning phase\n			3.3.2.2 Module and door identification\n		3.3.3 Results\n	3.4 Manufacturing of electric motors\n		3.4.1 Task and system description\n			3.4.1.1 Human–robot interface\n			3.4.1.2 Lab scenario\n			3.4.1.3 Industrial scenario\n		3.4.2 Methodology\n			3.4.2.1 Pole selection\n			3.4.2.2 Entrance and exit position estimation\n			3.4.2.3 Robot movement\n		3.4.3 Results\n			3.4.3.1 Lab scenario\n			3.4.3.2 Industrial scenario\n	3.5 Conclusions\n	References\n4 The tolerance scheduling problem for maximum lateness in Industry 4.0 systems\n	4.1 Introduction\n	4.2 Industry 4.0 production environments\n		4.2.1 Cyber-physical systems\n		4.2.2 Cyber-physical production systems\n		4.2.3 Decision-making in cyber-physical production systems\n	4.3 Scheduling\n	4.4 The tolerance scheduling problem\n		4.4.1 Dynamic scheduling\n		4.4.2 Inverse scheduling\n		4.4.3 Tolerance scheduling\n	4.5 Application case: single machine scheduling minimizing Lmax\n	4.6 Conclusions\n	References\n5 Digitalization and security: a new challenge for Mathematics 4.0\n	5.1 Introduction\n	5.2 The role of mathematics in Industry 4.0—a special kind of decision-making\n	5.3 The mathematics of data science—challenges to Industry 4.0\n		5.3.1 Making decisions—preparation of data\n	5.4 Digitalization and security\n		5.4.1 Challenges and opportunities in the course of intelligent process optimization\n		5.4.2 A duck tale and Industry 4.0\n	5.5 What do we mean by “digitalization”?\n	5.6 Efficiency enhancement and added value through digitalization\n	5.7 Digital distribution channels\n	5.8 Digital economy and Industry 4.0\n		5.8.1 Strategic digitalization—intelligent tracking and complex security\n		5.8.2 Interaction and intelligent overall system\n	5.9 Digital processes as a supreme discipline—computerization versus digitalization\n	5.10 Maturity models\n	5.11 Other success stories of mathematics and Industry 4.0\n	5.12 General technological advantages\n		5.12.1 Cloud computing\n	5.13 Success stories in companies\n		5.13.1 Bayer biopharmaceutical located in Garbagnate, Italy: the correct use of data\n		5.13.2 Haier located in Qingdao, China: predicted maintenance needs\n		5.13.3 Phoenic contact located in Bad Pyrmont and Blomberg, Germany: digital twins\n		5.13.4 Siemens located in Chengdu, China: augmented reality\n		5.13.5 NX: a platform for the development of solutions\n		5.13.6 Bosch Automotive in Wuxi, China\n		5.13.7 Summary\n	5.14 Outlook: mathematics and Industry 4.0—digitalization and security\n		5.14.1 Digitization and security\n			5.14.1.1 A new challenge for Mathematics 4.0\n	References\n	Further reading\n6 Proposal and application of a framework to measure the degree of maturity in Quality 4.0: A multiple case study\n	6.1 Introduction\n	6.2 Industry 4.0: Pillars and perspectives of integration\n	6.3 Quality 4.0: Alignment of quality in the new scenario of the Fourth Industrial Revolution\n		6.3.1 Quality 4.0 organizational dimensions\n			6.3.1.1 Data\n			6.3.1.2 Analytics\n			6.3.1.3 Connectivity\n			6.3.1.4 Collaboration\n			6.3.1.5 App development\n			6.3.1.6 Scalability\n			6.3.1.7 Management system\n			6.3.1.8 Compliance\n			6.3.1.9 Culture\n			6.3.1.10 Leadership\n			6.3.1.11 Competence\n	6.4 Framework development to assess the organization’s Quality 4.0 maturity\n		6.4.1 Systematic application and data treatment based on a numerical approach\n	6.5 Quality 4.0: Multiple case study\n		6.5.1 Quality 4.0 maturity in the automotive industry\n		6.5.2 Quality 4.0 maturity in the energy industry\n		6.5.3 Weaknesses and potentialities identified\n	6.6 Comments and future perspectives\n	References\n7 Intelligent manufacturing as a social institute: Internal and external regulation\n	7.1 Introduction\n	7.2 Literature review\n	7.3 Materials and method\n	7.4 Results\n		7.4.1 Evaluation of the effectiveness of the existing practices of internal and external stimulation of the development of ...\n		7.4.2 Innovative practices of stimulation of the development of intellectual production for its quick and successful instit...\n		7.4.3 Modeling of the institutionalization of intellectual production and practical recommendations (policy implications)\n	7.5 Conclusions\n	Acknowledgments\n	References\n8 Production planning and supply chain management under the conditions of Industry 4.0\n	8.1 Introduction\n	8.2 Literature review\n	8.3 Materials and method\n	8.4 Results\n		8.4.1 Regional models of production planning and supply chain management in the modern global economy\n		8.4.2 Perspective model of production planning and supply chain management under the conditions of Industry 4.0\n		8.4.3 Adapting the perspective model of production planning and supply chain management under the conditions of Industry 4....\n	8.5 Conclusions\n	Acknowledgment\n	References\n9 Infrastructural provision and organization of production on the basis of the Internet of Things\n	9.1 Introduction\n	9.2 Literature review\n	9.3 Materials and method\n	9.4 Results\n		9.4.1 The essence and specific features of the Fourth Industrial Revolution and advantages of automatized production on the...\n		9.4.2 Modeling of production and distribution processes in the Internet economy during automatized production on the basis ...\n		9.4.3 Infrastructural provision of automatized production on the basis of the Internet of Things: Specific features, defici...\n	9.5 Conclusions\n	Acknowledgment\n	References\n10 Artificial intelligence as the core of production of the future: Machine learning and intellectual decision supports\n	10.1 Introduction\n	10.2 Literature review\n	10.3 Materials and method\n	10.4 Results\n		10.4.1 The new economic practice in the sphere of the development of artificial intelligence\n		10.4.2 Scenarios of digital modernization of the modern economy depending on the functions of artificial intelligence in th...\n		10.4.3 Algorithms of artificial intelligence training for the execution of various functions within the compiled scenarios\n	10.5 Conclusions\n	Acknowledgment\n	References\n11 Active digital manufacturing: Conceptual foundations and practical solutions\n	11.1 Introduction\n	11.2 Literature review\n	11.3 Materials and method\n	11.4 Results\n		11.4.1 The scientific concept of active digital manufacturing, its principles, priorities, and differences from the concept...\n		11.4.2 Comparative analysis of the advantages from the development of passive and active digital manufacturing\n		11.4.3 Current problems of starting and implementing active digital manufacturing and their perspective solutions\n	11.5 Conclusions\n	Acknowledgment\n	References\n12 Big Data management and data analysis: Applied solutions in view of the spheres of the modern economy\n	12.1 Introduction\n	12.2 Literature review\n	12.3 Materials and method\n	12.4 Results\n		12.4.1 Evaluation of the sufficiency of the existing statistical data bases and tools for processing of large arrays of sta...\n		12.4.2 The conceptual model of application of breakthrough digital technologies of management and Big Data analysis for sta...\n		12.4.3 The algorithm of statistical accounting and analytics for various spheres of a modern economy in the conditions of I...\n	12.5 Conclusions\n	References\n13 Infusion–diffusion process-based modeling and profit estimation for manufacturing industries\n	13.1 Introduction\n	13.2 Role of warranty in determining overall profit: A literature review\n	13.3 Formulation of sales function\n	13.4 Optimization problem formulation\n		13.4.1 Manufacturing cost modeling\n			13.4.1.1 Model for quantity produced\n			13.4.1.2 Manufacturing cost\n		13.4.2 Warranty cost modeling\n			13.4.2.1 Possible number of complaints\n			13.4.2.2 Warranty probability\n			13.4.2.3 Actual number of complaints\n			13.4.2.4 Complaint factor\n		13.4.3 Formulation of warranty probability in terms of product performance and customer expectation\n		13.4.4 Problem formulation for optimal profit\n	13.5 Numerical illustration\n	13.6 Managerial implications\n	13.7 Conclusions\n	References\n14 Application of AHP in evaluating the financial performance of industries\n	14.1 Introduction\n	14.2 Financial ratios\n		14.2.1 Liquidity ratios\n		14.2.2 Financial leverage ratios\n		14.2.3 Profitability ratios\n		14.2.4 Growth ratios\n	14.3 Methodology\n		14.3.1 Multicriteria decision-making\n		14.3.2 Analytical hierarchy process\n	14.4 Experiments and results\n		14.4.1 Data analysis\n			14.4.1.1 Criteria level\n			14.4.1.2 Subcriteria level\n	14.5 Discussion and conclusions\n	References\n15 Application of Internet of Things-aided simulation and digital twin technology in smart manufacturing\n	15.1 Introduction\n	15.2 Smart manufacturing systems and Industry 4.0 technologies—a glimpse\n		15.2.1 Autonomous mobile robots\n		15.2.2 The industrial Internet of Things\n		15.2.3 Additive manufacturing\n			15.2.3.1 Additive manufacturing processes\n			15.2.3.2 Additive manufacturing technologies\n			15.2.3.3 Additive manufacturing materials\n		15.2.4 Augmented reality\n			15.2.4.1 Augmented reality for product design, inspection, and maintenance\n			15.2.4.2 Augmented reality for upskilling and productivity\n			15.2.4.3 Augmented reality for quality assurance\n		15.2.5 Simulation and virtual reality\n		15.2.6 Cloud computing\n		15.2.7 Big Data and analytics\n			15.2.7.1 Big Data analytics technologies and tools\n			15.2.7.2 How Big Data analytics works\n	15.3 Digital twin-driven smart manufacturing\n		15.3.1 A reference model for the digital twin\n	15.4 Creation of a digital twin in a smart manufacturing system\n	15.5 Summary\n	References\n16 Mathematical models for the dimensional accuracy of products generated by additive manufacturing\n	16.1 Introduction to dimensional quality in additive manufacturing\n	16.2 Main factors for dimensional accuracy in additive manufacturing\n		16.2.1 Effects of layer thickness and surface orientation\n		16.2.2 Effects of extruder errors\n		16.2.3 Effects of material shrinkage and beam offset\n	16.3 Mathematical modeling of dimensional deviations in additive manufacturing\n		16.3.1 Dimensional deviations in additive manufacturing-generated parts\n		16.3.2 Surface roughness in additive manufacturing-generated parts\n	16.4 Accuracy improvement in additive manufacturing by optimization or compensation techniques\n		16.4.1 Optimization of part orientation in additive manufacturing\n		16.4.2 Compensation of extruder errors in additive manufacturing\n		16.4.3 Compensation of shrinkage effect and beam offset\n	16.5 Conclusions\n	References\nIndex