These proceedings bring together both industrial and academic professionals to exchange and share their experiences, most recent innovations, trends and research results on all aspects of Advances in Materials, Manufacturing and Machine Learning.

The main aim of the 2nd international conference on recent advances in materials manufacturing and machine learning processes-2023 (RAMMML-23) is to bring together all interested academic researchers, scientists, engineers, and technocrats and provide a platform for continuous improvement of manufactur□ing, machine learning, design and materials engineering research. RAMMML 2023 received an overwhelm□ing response with more than 530 full paper submissions. After due and careful scrutiny, about 120 of them have been selected for presentation. The papers submitted have been reviewed by experts from renowned institutions, and subsequently, the authors have revised the papers, duly incorporating the suggestions of the reviewers. This has led to significant improvement in the quality of the contributions, Taylor & Francis publications, CRC Press have agreed to publish the selected proceedings of the conference in their book series of Advances in Mechanical Engineering and Interdisciplinary Sciences. This enables fast dissemina□tion of the papers worldwide and increases the scope of visibility for the research contributions of the authors.

The role of manufacturing in a country’s economy and societal development has long been established through their wealth generating capabilities. To enhance and widen our knowledge of materials and to increase innovation and responsiveness to ever-increasing international needs, more in-depth studies of functionally graded materials/tailor-made materials, recent advancements in manufacturing processes and new design philosophies are needed at present. The objective of this volume is to bring together experts from academic institutions, industries and research organizations and professional engineers for sharing of knowledge, expertise and experience in the emerging trends related to design, advanced materials processing and characterization, and advanced manufacturing processes.

The main aim of the 2nd international conference on recent advances in materials manufacturing and machine learning processes-2023 (RAMMML-23) is to bring together all interested academic researchers, scientists, engineers, and technocrats and provide a platform for continuous improvement of manufactur□ing, machine learning, design and materials engineering research. RAMMML 2023 received an overwhelm□ing response with more than 530 full paper submissions. After due and careful scrutiny, about 120 of them have been selected for presentation. The papers submitted have been reviewed by experts from renowned institutions, and subsequently, the authors have revised the papers, duly incorporating the suggestions of the reviewers. This has led to significant improvement in the quality of the contributions, Taylor & Francis publications, CRC Press have agreed to publish the selected proceedings of the conference in their book series of Advances in Mechanical Engineering and Interdisciplinary Sciences. This enables fast dissemina□tion of the papers worldwide and increases the scope of visibility for the research contributions of the authors.

This book presents the select proceedings of the 2nd International Conference on Advances in Materials and Manufacturing Technology (ICAMMT 2022). The book covers the latest trends in existing and new materials, manufacturing processes, evaluation of materials properties for the application in automotive, aerospace, marine, locomotive, automotive and energy sectors. The topics covered include advanced metal forming, bending, welding and casting techniques, recycling and re-manufacturing of materials and components, materials processing, characterization and applications, multi-physics coupling simulation, and optimization, alternate materials /material substitution, thermally-enhanced processes, and materials, composites and polymer manufacturing, powder metallurgy and ceramic forming, numerical modeling and simulation, advanced machining processes, functionally graded materials, non-destructive examination, optimization techniques, engineering materials, heat treatment, material testing, MEMS integration, energy materials, bio-materials, metamaterials, metallography, nanomaterial, SMART materials and super alloys. In addition, it discusses industrial applications and covers theoretical and analytical methods, numerical simulations and experimental techniques in the area of advanced materials and their applications. It also covers the application of artificial intelligence in advanced materials and manufacturing technology. The book will be a valuable reference for researchers and industry professionals alike.

Machine learning methods have lowered the cost of exploring new structures of unknown compounds, and can be used to predict reasonable expectations and subsequently validated by experimental results. As new insights and several elaborative tools have been developed for materials science and engineering in recent years, it is an appropriate time to present a book covering recent progress in this field. Searchable and interactive databases can promote research on emerging materials. Recently, databases containing a large number of high-quality materials properties for new advanced materials discovery have been developed. These approaches are set to make a significant impact on human life and, with numerous commercial developments emerging, will become a major academic topic in the coming years. This authoritative and comprehensive book will be of interest to both existing researchers in this field as well as others in the materials science community who wish to take advantage of these powerful techniques. The book offers a global spread of authors, from USA, Canada, UK, Japan, France, Russia, China and Singapore, who are all world recognized experts in their separate areas. With content relevant to both academic and commercial points of view, and offering an accessible overview of recent progress and potential future directions, the book will interest graduate students, postgraduate researchers, and consultants and industrial engineers.

These proceedings bring together both industrial and academic professionals to exchange and share their experiences, most recent innovations, trends and research results on all aspects of Advances in Materials, Manufacturing and Machine Learning.

This volume comprises the select proceedings of the 3rd Biennial International Conference on Future Learning Aspects of Mechanical Engineering (FLAME) 2022. It aims to provide a comprehensive and broad-spectrum picture of the state-of-the-art research and development in thermal, fluids, energy and process engineering, mechatronics, control and robotics, material science and engineering, solid mechanics and structural engineering, dynamics and control, engineering design, manufacturing and industrial engineering, automobile engineering. This volume will prove a valuable resource for researchers and professionals in mechanical engineering and allied fields.

This book highlights selected papers from the Mechanical Engineering track, with a focus on mechatronics and manufacturing, presented at the “Malaysian Technical Universities Conference on Engineering and Technology” (MUCET 2019). The conference brings together researchers and professionals in the fields of engineering, research and technology, providing a platform for future collaborations and the exchange of ideas.

This compendium provides a comprehensive collection of the emergent applications of big data, machine learning, and artificial intelligence technologies to present day physical sciences ranging from materials theory and imaging to predictive synthesis and automated research. This area of research is among the most rapidly developing in the last several years in areas spanning materials science, chemistry, and condensed matter physics.Written by world renowned researchers, the compilation of two authoritative volumes provides a distinct summary of the modern advances in instrument — driven data generation and analytics, establishing the links between the big data and predictive theories, and outlining the emerging field of data and physics-driven predictive and autonomous systems.