Intelligent Systems In Production Engineering And Maintenance

This book reports on intelligent methods and solutions in engineering production and maintenance. It describes advanced tools for optimizing production processes, increasing their automation, safety and sustainability. Contributions cover different stages of the production process, such as product design, supply chain, and equipment maintenance and safety. This is one of the two volumes based on the 4th International Conference on Intelligent Systems in Production Engineering and Maintenance, ISPEM 2023, held on September 13-15, 2023, in Wroclaw, Poland.

This book discusses the papers presented at Conference ISPEM 2023 which was organized by Wrocław University of Science and Technology, Liverpool John Moores University, and University of Minho. The conference gave an opportunity to exchange experiences in intelligent systems and tools in production, and maintenance, especially its practical application.

The book presents a collection of 103 peer-reviewed articles from the Second International Conference on Intelligent Systems in Production Engineering and Maintenance (ISPEM 2018). The conference was organized by the Faculty of Mechanical Engineering and CAMT (Centre for Advanced Manufacturing Technologies), Wrocław University of Science and Technology and was held in Wrocław (Poland) on 17–18 September 2018. The conferences topics included the possibility of using a wide range of intelligent methods in production engineering, presenting and discussing new solutions for innovative plants, research findings and case studies demonstrating advances in production and maintenance from the point of view of Industry 4.0 – particularly applications of intelligent systems, methods and tools in production engineering, maintenance, logistics, quality management, information systems and product development. The book is divided into two parts: the first includes papers related to intelligent systems in production engineering, while the second is dedicated to special sessions focusing on: 1. Computer Aided methods in Production Engineering 2. Mining 4.0 and Intelligent Mining Transportation 3. Modelling and Simulation of Production Processes 4. Multi-Faceted Modelling of Networks and Processes 5. Product Design and Product Manufacturing in Industry 4.0 This book is an excellent source of information for scientists in the field of manufacturing engineering and for top managers in production enterprises.

The volume presents a collection of 44 peer-reviewed articles from the First International Conference on Intelligent Systems in Production Engineering and Maintenance (ISPEM 2017). ISPEM 2017 was organized by the Faculty of Mechanical Engineering, Wrocław University of Science and Technology and was held in Wrocław (Poland) on 28–29 September 2017. The main topics of the conference included the possibility of using widely understood intelligent methods in production engineering. New solutions for innovative plants, research results and case studies taking into account advances in production and maintenance from the point of view of Industry 4.0 were presented and discussed—with special attention paid to applications of intelligent systems, methods and tools in production engineering, maintenance, logistics, quality management, information systems, and product development. The volume is divided into two parts: 1. Intelligent Systems in Production Engineering 2. Intelligent Systems in Maintenance This book is an excellent reference resource for scientists in the field of manufacturing engineering and for top managers in production enterprises.

Industry 4.0 is changing how we manage operations to drive systems more intelligently. Technologies and applications are rapidly evolving. Disruptive technologies, such as artificial intelligence, big data, cloud computing and digital twin, are shaking up different industries and have motivated us to revisit engineering and management tools for improving system design, efficiency, effectiveness, reliability, and responsiveness. While these emerging technologies have powered new applications, novel industrial engineering methodologies are required to achieve the goals. Industrial Engineering was sprouted from major engineering disciplines that called for better professional understanding of industrialization. Ever since, the discipline of Industrial Engineering has been the star role player in confronting emerging industries; be it manufacturing, service, high tech products, outer space technology, information technology, industrial policy, ergonomics, and now the world’s greatest concern, sustainable development. This book presents the state-of-the-art in industrial engineering research from different countries and cities around the globe. The book covers a wide range of topics in industrial engineering, including: Demand Chain Management, E-business / Information Technology, Evolutionary Algorithm, Green Manufacturing/Management, Health Care Systems and more.

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.

Cyber-Physical and Gentelligent Systems in Manufacturing and Life Cycle explores the latest technologies resulting from the integration of sensing components throughout the production supply chain, and the resulting possibilities to improve efficiency, flexibility, and product quality. The authors present cutting edge research into data storage in components, communication devices, data acquisition, as well as new industrial applications. Detailed technical descriptions of the tools are presented in addition to discussions of how these systems have been used, the benefits they provide, and what industry problems they could tackle in the future. This is essential reading for researchers and production engineers interested in the potential of cyber physical systems to optimize all parts of the supply chain.

Artificial intelligence (AI) plays a crucial role in production engineering and management, revolutionizing operation optimization, data analysis, forecasting, and task automation. In production engineering, AI contributes to operational efficiency by identifying patterns, predicting failures, and optimizing supply chains. In management, it offers predictive and prescriptive insights, allowing quick adaptations to changes in market conditions. However, it is essential to address ethical challenges, such as data security and workforce impacts, to ensure responsible and sustainable implementation of AI in these contexts. Among the different topics covered in Artificial Intelligence in Production Engineering and Management, the reader can find: living in the age of AI; machine learning and large language models; AI and people management; nudging financial behavior with AI; the European Union narrative on AI; multidimensional transhuman influence; and AI, emotional intelligence, and ethics. This topical book will be of great value for those working and researching in the production engineering and management fields who are seeking to understand and capitalize on the revolutionary shift that artificial intelligence brings to modern engineering and management. Combines research in AI from the fields of production engineering and management to provide effective solutions to problems with interdisciplinary relevance Offers a comprehensive view of the possibilities that artificial intelligence brings to production engineering and management Inspires reflection, innovation, and a renewed appreciation for the ability of technology to redefine, improve, and transform the foundations of production engineering and management

This book applies artificial intelligence to lean production and shows how to practically combine the advantages of these two disciplines. Lean manufacturing originated in Japan and is a well-known tool for improving manufacturers' competitiveness. Prevalent tools for lean manufacturing include Kanban, Pacemaker, Value Stream Map, 5s, Just-in-Time and Pull Manufacturing. Lean Manufacturing and the Toyota Manufacturing System has been successfully applied to various factories and supply chains around the world. A lean manufacturing system can not only reduce wastes and inventory, but also respond to customer needs more immediately. Artificial intelligence is a subject that has attracted much attention recently. Many researchers and practical developers are working hard to apply artificial intelligence to our daily lives, including in factories. For example, fuzzy rules have been established to optimize machine settings. Bionic algorithms have been proposed to solve production sequencing and scheduling problems. Machine learning technologies are applied to detect possible product quality problems and diagnose the health of a machine. This book will be of interest to production engineers, managers, as well as students and researchers in manufacturing engineering.