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Selected, peer reviewed papers from the 2nd International Conference on Key Engineering Materials and Computer Science (KEMCS 2013), March 3-4, 2013, Phuket, Thailand
This introductory text is intended to provide undergraduate engineering students with the background needed to understand the science of structure-property relationships, as well as address the engineering concerns of materials selection in design. A computer diskette is included.
Contains collection of papers from the below symposia held during the 10th Pacific Rim Conference on Ceramic and Glass Technology (PacRim10), June 2-7, 2013, in Coronado, California 2012: Novel, Green, and Strategic Processing and Manufacturing Technologies Polymer Derived Ceramics and Composites Advanced Powder Processing and Manufacturing Technologies Synthesis and Processing of Materials Using Electric Fields/Currents
Mechanical properties of composite materials can be improved by tailoring their microstructures. Optimal microstructures of composites, which ensure desired properties of composite materials, can be determined in computational experiments. The subject of this book is the computational analysis of interrelations between mechanical properties (e.g., strength, damage resistance stiffness) and microstructures of composites. The methods of mesomechanics of composites are reviewed, and applied to the modelling of the mechanical behaviour of different groups of composites. Individual chapters are devoted to the computational analysis of the microstructure- mechanical properties relationships of particle reinforced composites, functionally graded and particle clusters reinforced composites, interpenetrating phase and unidirectional fiber reinforced composites, and machining tools materials.
Combining different perspectives from materials science, engineering, and computer science, this reference provides a unified view of the various aspects necessary for the successful realization of intelligent systems. The editors and authors are from academia and research institutions with close ties to industry, and are thus able to offer first-hand information here. They adopt a unique, three-tiered approach such that readers can gain basic, intermediate, and advanced topical knowledge. The technology section of the book is divided into chapters covering the basics of sensor integration in materials, the challenges associated with this approach, data processing, evaluation, and validation, as well as methods for achieving an autonomous energy supply. The applications part then goes on to showcase typical scenarios where material-integrated intelligent systems are already in use, such as for structural health monitoring and smart textiles.
Failure modes and effects analysis (FMEA); Reliability; Product Development; Design Process; Test Procedures "Explore Product Design and Testing for Automotive Engineering: Volume II, an essential guide reshaping vehicle manufacturing with unprecedented reliability. As part of SAE International’s DOE for Product Reliability Growth series, this practical resource introduces cutting-edge methodologies crucial for predicting and improving product reliability in an era of automotive electrification. The book navigates statistical tolerance design, showcasing how variability in part fabrication and assembly can enhance reliability and sustainability. Key topics include: - Statistical tolerance design's impact on manufacturing and material selection, focusing on non-normal distributions' effects on product assembly and cost. Methods like maximum likelihood estimators and Monte Carlo simulations are used for assembly strategy synthesis. - Reliability DOEs using log-location-scale distributions to estimate lifetimes of non-normally distributed components, especially in accelerated life testing. It covers transformations optimizing parts and system designs under the lognormal distribution. - Weibull distribution (DOE-W) for characterizing lifetimes affected by various failure modes, detailing parameter assessment methods and real-world applications. The book also introduces reliability design of experiments based on the exponential distribution (DOE-E). - Importance of predicting lifecycles and enhancing reliability through qualitative and stepwise accelerated life tests. Integration of physics of failure with statistical methods like Weibull statistics and lognormal approximation enhances analysis credibility. - Inferential mechanisms such as the Arrhenius and Eyring models in predicting automotive component lifecycles, refining product life prediction based on reliability DOEs. Whether you're an engineer, researcher, or automotive professional, this book equips you to navigate reliability engineering confidently. Revolutionize your approach to product design and testing with Product Design and Testing for Automotive Engineering, your definitive companion in shaping the future of automotive reliability." (ISBN 9781468607703 ISBN 9781468607697 ISBN 9781468607727 DOI 10.4271/9781468607697)
Continuous improvements in machining practices have created opportunities for businesses to develop more streamlined processes. This not only leads to higher success in day-to-day production, but also increases the overall success of businesses. Non-Conventional Machining in Modern Manufacturing Systems provides emerging research exploring the theoretical and practical aspects of technological advancements in industrial environments and applications in manufacturing. Featuring coverage on a broad range of topics such as optimization techniques, electrical discharge machining, and hot machining, this book is ideally designed for business managers, engineers, business professionals, researchers, and academicians seeking current research on non-conventional and technologically advanced machining processes.
Selected, peer reviewed papers from the Seventh International Conference on Advanced Computational Engineering and Experimenting (ACE-X 2013), July 1-4, 2013, Madrid, Spain
In the face of escalating environmental challenges such as climate change, air and water pollution, and natural disasters, traditional approaches to understanding and addressing these issues have yet to be proven sufficient. Academic scholars are compelled to seek innovative solutions that marry digital intelligence and natural ecosystems. Reshaping Environmental Science Through Machine Learning and IoT serves as a comprehensive exploration into the transformative potential of Machine Learning (ML) and the Internet of Things (IoT) to address critical environmental challenges. The book establishes a robust foundation in ML and IoT, explaining their relevance to environmental science. As the narrative unfolds, it delves into diverse applications, providing theoretical insights alongside practical knowledge. From interpreting weather patterns to predicting air and water quality, the book navigates through the intricate web of environmental complexities. Notably, it unveils approaches to disaster management, waste sorting, and climate change monitoring, showcasing the symbiotic relationship between digital intelligence and natural ecosystems. This book is ideal for audiences from students and researchers to data scientists and disaster management professionals with a nuanced understanding of IoT, ML, and Artificial Intelligence (AI).