This book details the emerging area of the induction of expert systems in thermal spray technology, replacing traditional parametric optimization methods like numerical modeling and simulation. It promotes, enlightens, and hastens the digital transformation of the surface engineering industry by discussing the contribution of expert systems like Machine Learning (ML) and Artificial Intelligence (AI) toward achieving durable Thermal Spray (TS) coatings. Artificial Intelligence and Machine Learning in the Thermal Spray Industry: Practices, Implementation, and Challenges highlights how AI and ML techniques are used in the TS industry. It sheds light on AI's versatility, revealing its applicability in solving problems related to conventional simulation and numeric modeling techniques. This book combines automated technologies with expert machines to show several advantages, including decreased error and greater accuracy in judgment, and prediction, enhanced efficiency, reduced time consumption, and lower costs. Specific barriers preventing AI's successful implementation in the TS industry are also discussed. This book also looks at how training and validating more models with microstructural features of deposited coating will be the center point to grooming this technology in the future. Lastly, this book thoroughly analyzes the digital technologies available for modeling and achieving high-performance coatings, including giving AI-related models like Artificial Neural Networks (ANN) and Convolutional Neural Networks (CNN) more attention. This reference book is directed toward professors, students, practitioners, and researchers of higher education institutions working in the fields that deal with the application of AI and ML technology.

· Highlights how Artificial Intelligence and Machine Learning techniques are used in the Thermal Spray industry to predict future research directions · Sheds light on Artificial Intelligence’s versatility, revealing its applicability in solving problems related to conventional simulation and numeric modeling techniques · Combines automated technologies with expert machines to show several advantages, including decreased error and greater accuracy in judgment, and prediction, enhanced efficiency, reduced time consumed, and lower costs · Discusses how certain barriers are preventing the successfully implementing of Artificial Intelligence in the Thermal Spray industry Looks at how training and validating more models with microstructural features of deposited coating will be the center point to grooming this technology in the future · Offers a thorough analysis of the digital technologies available for modeling and achieving high-performance coatings including giving Artificial intelligence-related models like ANN and CNN more attention

This book explains various methods needed to overcome the challenges faced during environmental remediation with a focus on nanotechnology. The book comprises ten edited chapters that aim to inform and educate readers about recent technologies that are beneficial for pollution control. Starting with an introduction to environmental remediation, the book covers innovative nanomaterials including spinel nanoferrites, carbonaceous quantum dots, carbon nanotubes and nanobioadsorbents. In addition to highlighting the environmental benefits of these materials, the book includes chapters on the potential of nanotechnology for harnessing the environment to generate energy through nanogenerators and piezoelectric energy harvesting devices. Key features of the book include notes on fundamental issues and challenges regarding environmental remediation, easy to read content with pictorial illustrations and scholarly references for each chapter. The book is an informative resource for students and academicians in science, technology and environmental science discipline.

This book contains the works connected with the key advances in Industrial Artificial Intelligence presented at IITI 2023, the Seventh International Scientific Conference on Intelligent Information Technologies for Industry held on September 25-30, 2023 in St. Petersburg, Russia. The works were written by the experts in the field of applied artificial intelligence including topics such as Machine Learning, Explainable AI, Decision-Making, Fuzzy Logic, Multi-Agent and Bioinspired Systems. The following industrial application domains were touched: railway automation, cyber security, intelligent medical systems, navigation and energetic systems. The editors believe that this book will be helpful for all scientists and engineers interested in the modern state of applied artificial intelligence.

Research can face artificial intelligence (AI) as an issue of technology development but also as an issue of enacted technology at work. Human-centered design of AI gives emphasis to the expertise and needs of human beings as a starting point of technology development or as an outcome of AI-based work settings. This is an important goal, as expressed, for example, by the international labor organization's call for a "human-centered agenda" for the future of AI and automation collaboration. This Research Topic raises the question of what human-centricity means, i.e. what are the criteria and indicators of human-centered AI and how can they be considered and implemented?

The five-volume set IFIP AICT 630, 631, 632, 633, and 634 constitutes the refereed proceedings of the International IFIP WG 5.7 Conference on Advances in Production Management Systems, APMS 2021, held in Nantes, France, in September 2021.* The 378 papers presented were carefully reviewed and selected from 529 submissions. They discuss artificial intelligence techniques, decision aid and new and renewed paradigms for sustainable and resilient production systems at four-wall factory and value chain levels. The papers are organized in the following topical sections: Part I: artificial intelligence based optimization techniques for demand-driven manufacturing; hybrid approaches for production planning and scheduling; intelligent systems for manufacturing planning and control in the industry 4.0; learning and robust decision support systems for agile manufacturing environments; low-code and model-driven engineering for production system; meta-heuristics and optimization techniques for energy-oriented manufacturing systems; metaheuristics for production systems; modern analytics and new AI-based smart techniques for replenishment and production planning under uncertainty; system identification for manufacturing control applications; and the future of lean thinking and practice Part II: digital transformation of SME manufacturers: the crucial role of standard; digital transformations towards supply chain resiliency; engineering of smart-product-service-systems of the future; lean and Six Sigma in services healthcare; new trends and challenges in reconfigurable, flexible or agile production system; production management in food supply chains; and sustainability in production planning and lot-sizing Part III: autonomous robots in delivery logistics; digital transformation approaches in production management; finance-driven supply chain; gastronomic service system design; modern scheduling and applications in industry 4.0; recent advances in sustainable manufacturing; regular session: green production and circularity concepts; regular session: improvement models and methods for green and innovative systems; regular session: supply chain and routing management; regular session: robotics and human aspects; regular session: classification and data management methods; smart supply chain and production in society 5.0 era; and supply chain risk management under coronavirus Part IV: AI for resilience in global supply chain networks in the context of pandemic disruptions; blockchain in the operations and supply chain management; data-based services as key enablers for smart products, manufacturing and assembly; data-driven methods for supply chain optimization; digital twins based on systems engineering and semantic modeling; digital twins in companies first developments and future challenges; human-centered artificial intelligence in smart manufacturing for the operator 4.0; operations management in engineer-to-order manufacturing; product and asset life cycle management for smart and sustainable manufacturing systems; robotics technologies for control, smart manufacturing and logistics; serious games analytics: improving games and learning support; smart and sustainable production and supply chains; smart methods and techniques for sustainable supply chain management; the new digital lean manufacturing paradigm; and the role of emerging technologies in disaster relief operations: lessons from COVID-19 Part V: data-driven platforms and applications in production and logistics: digital twins and AI for sustainability; regular session: new approaches for routing problem solving; regular session: improvement of design and operation of manufacturing systems; regular session: crossdock and transportation issues; regular session: maintenance improvement and lifecycle management; regular session: additive manufacturing and mass customization; regular session: frameworks and conceptual modelling for systems and services efficiency; regular session: optimization of production and transportation systems; regular session: optimization of supply chain agility and reconfigurability; regular session: advanced modelling approaches; regular session: simulation and optimization of systems performances; regular session: AI-based approaches for quality and performance improvement of production systems; and regular session: risk and performance management of supply chains *The conference was held online.