Jashanpreet Singh – författare

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.

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.

Sustainability and technological advancements are rapidly gaining traction on a global scale and are becoming increasingly prominent across a wide range of industries, as evidenced by current market trends. Companies are making significant investments in the research and development of smart materials that can adapt to their surroundings in real time, thereby improving their performance and productivity. The heightened interest in environmentally friendly and intelligent materials can be attributed to the recent surge in research, conferences, and patent applications in this field, highlighting the need for a comprehensive resource that can provide a thorough explanation of these developments.

Green Technology and Smart Materials for Engineering Applications provides a thorough examination of smart materials, including their unique properties and applications in sustainable construction. It explores the versatile uses of green materials in different industries, emphasizing sustainable manufacturing practices and resource-efficient materials. The integration of eco-design and innovation is looked at for the creation of sustainable materials, highlighting the importance of green and smart materials in optimizing energy consumption. Additionally, the book offers insights into reuse and recycling techniques that promote circular economy principles and sustainable business models related to green and smart materials. Real-world examples and success stories are used to illustrate the environmental and economic impacts of implementing smart and green materials in various industries.

This book is intended to serve as a comprehensive reference guide for academics, researchers, scholars, and professionals working in the fields of manufacturing processing, material science, and environmental engineering. The primary objective of this book is to showcase sustainability by emphasizing the diverse array of green and smart materials utilized in various engineering applications. Through this publication, readers will gain valuable insights into the importance of incorporating environmentally friendly materials into their work, ultimately contributing to a more sustainable future.

Sustainability and technological advancements are rapidly gaining traction on a global scale and are becoming increasingly prominent across a wide range of industries, as evidenced by current market trends. Companies are making significant investments in the research and development of smart materials that can adapt to their surroundings in real time, thereby improving their performance and productivity. The heightened interest in environmentally friendly and intelligent materials can be attributed to the recent surge in research, conferences, and patent applications in this field, highlighting the need for a comprehensive resource that can provide a thorough explanation of these developments.

Green Technology and Smart Materials for Engineering Applications provides a thorough examination of smart materials, including their unique properties and applications in sustainable construction. It explores the versatile uses of green materials in different industries, emphasizing sustainable manufacturing practices and resource-efficient materials. The integration of eco-design and innovation is looked at for the creation of sustainable materials, highlighting the importance of green and smart materials in optimizing energy consumption. Additionally, the book offers insights into reuse and recycling techniques that promote circular economy principles and sustainable business models related to green and smart materials. Real-world examples and success stories are used to illustrate the environmental and economic impacts of implementing smart and green materials in various industries.

This book is intended to serve as a comprehensive reference guide for academics, researchers, scholars, and professionals working in the fields of manufacturing processing, material science, and environmental engineering. The primary objective of this book is to showcase sustainability by emphasizing the diverse array of green and smart materials utilized in various engineering applications. Through this publication, readers will gain valuable insights into the importance of incorporating environmentally friendly materials into their work, ultimately contributing to a more sustainable future.

As healthcare challenges such as human aging and traffic accidents continue to increase exponentially, the biomedical sector faces a significant obstacle in arranging patient-specific biomedical products. Over the past two decades, printing quality and ease of production of advanced manufacturing have gained global attention, particularly in the development of scaffolds and implants.

This book explores advanced manufacturing technologies, covering topics such as categorization, materials, processing factors, output responses, advantages, challenges, and biomedical applications. Through a critical analysis of past biomedical applications of additive manufacturing technology, the book discusses recent advancements and presents the design, applications, and characterizations of biomedical components using additive manufacturing techniques. Additionally, it discusses the significant applications of additive manufacturing in biomedical, biological, and implant manufacturing, showcasing the medical applications of rapid prototyping and addressing computational and experimental aspects of 3D-printed biomedical devices. The book also provides insights into future developments and the prospects of advanced manufacturing techniques in the field.

Advanced Manufacturing Technologies in Biomedical Science: Practical Applications, Case Studies, and Future Trends provides a comprehensive overview of the latest advancements in biomedical advanced manufacturing. This book is essential for researchers and professionals in the biomedical field, offering valuable insights and serving as a reliable source of information for manufacturers and academic researchers alike.

As healthcare challenges such as human aging and traffic accidents continue to increase exponentially, the biomedical sector faces a significant obstacle in arranging patient-specific biomedical products. Over the past two decades, printing quality and ease of production of advanced manufacturing have gained global attention, particularly in the development of scaffolds and implants.

This book explores advanced manufacturing technologies, covering topics such as categorization, materials, processing factors, output responses, advantages, challenges, and biomedical applications. Through a critical analysis of past biomedical applications of additive manufacturing technology, the book discusses recent advancements and presents the design, applications, and characterizations of biomedical components using additive manufacturing techniques. Additionally, it discusses the significant applications of additive manufacturing in biomedical, biological, and implant manufacturing, showcasing the medical applications of rapid prototyping and addressing computational and experimental aspects of 3D-printed biomedical devices. The book also provides insights into future developments and the prospects of advanced manufacturing techniques in the field.

Advanced Manufacturing Technologies in Biomedical Science: Practical Applications, Case Studies, and Future Trends provides a comprehensive overview of the latest advancements in biomedical advanced manufacturing. This book is essential for researchers and professionals in the biomedical field, offering valuable insights and serving as a reliable source of information for manufacturers and academic researchers alike.

Artificial Intelligence in Tribology presents the use of machine learning, AI, neural networks, and optimization algorithms in studying friction, wear, and lubrication. It explores the selection of materials, modeling of the tribo‑system, prediction of tribological behavior, and assessment and optimization of performance across numerous systems.

Addressing tribological problems from macroscale to nanoparticles, this book discusses AI‑driven methods to improve productivity, detect disasters, and support sustainable engineering. Case studies from various industries, such as automotive, aerospace, renewable energy, and marine engineering, highlight the transformative impact of AI in real‑world scenarios.

This book will interest researchers and graduate students studying AI integration and advances in surface engineering, tribology, and materials science.

Artificial Intelligence in Tribology presents the use of machine learning, AI, neural networks, and optimization algorithms in studying friction, wear, and lubrication. It explores the selection of materials, modeling of the tribo‑system, prediction of tribological behavior, and assessment and optimization of performance across numerous systems.

Addressing tribological problems from macroscale to nanoparticles, this book discusses AI‑driven methods to improve productivity, detect disasters, and support sustainable engineering. Case studies from various industries, such as automotive, aerospace, renewable energy, and marine engineering, highlight the transformative impact of AI in real‑world scenarios.

This book will interest researchers and graduate students studying AI integration and advances in surface engineering, tribology, and materials science.