Artificial intelligence (AI) techniques and the finite element method (FEM) are both powerful computing tools, which are extensively used for modeling and optimizing manufacturing processes. The combination of these tools has resulted in a new flexible and robust approach as several recent studies have shown. This book aims to review the work already done in this field as well as to expose the new possibilities and foreseen trends. The book is expected to be useful for postgraduate students and researchers, working in the area of modeling and optimization of manufacturing processes.
Artificial intelligence (AI) techniques and the finite element method (FEM) are both powerful computing tools, which are extensively used for modeling and optimizing manufacturing processes. The combination of these tools has resulted in a new flexible and robust approach as several recent studies have shown. This book aims to review the work already done in this field as well as to expose the new possibilities and foreseen trends. The book is expected to be useful for postgraduate students and researchers, working in the area of modeling and optimization of manufacturing processes.
Artikelnr. des Verlages: 80028068, 978-3-642-28084-9
2012
Seitenzahl: 104
Erscheinungstermin: 17. Februar 2012
Englisch
Abmessung: 235mm x 155mm x 7mm
Gewicht: 454g
ISBN-13: 9783642280849
ISBN-10: 3642280846
Artikelnr.: 34690961
Autorenporträt
João P. Davim received his PhD degree in Mechanical Engineering from the University of Porto in 1997 and the Aggregation from the University of Coimbra in 2005. Between 1986/96, he was lecturer in University of Porto. Currently, he is an Aggregate Professor at the Department of Mechanical Engineering of the University of Aveiro and the Head of MACTRIB - Machining and Tribology Research Group . He has more 24 years of teaching and research experience in manufacturing, materials and mechanical engineering with special emphasis in Machining & Tribology. He is the Editor in Chief of six international journals, Guest Editor of journals, books Editor, book Series Editor and Scientific Advisory for many international journals and conferences. Presently, he is an Editorial Board member of 15 international journals and acts as reviewer for than 50 prestigious ISI web Science journals. In addition, he has also published as author and co-author more than 30 book chapters and 300 articles in
journals and conferences.
Inhaltsangabe
Relevance and convenience of hybrid (AI and FEM) modeling and optimization of manufacturing processes.- Approaches for combining AI and FEM.- FEM/AI models.- AI/FEM models.- Other approaches.- Artificial intelligence tools.- AI tools for modeling.- Artificial neural networks.- Fuzzy logic and neuro-fuzzy system.- Bayesian probability.- AI tool for optimization.- Evolutionary algorithms.- Swarm intelligence.- Finite element method for manufacturing modeling.- Plasticity models.- Constitutive equations.- Finite element discretization.- Case of study.- Experimental study.- FEM-based modeling.- AI-based modeling.- Optimization.- Concluding remarks.
Relevance and convenience of hybrid (AI and FEM) modeling and optimization of manufacturing processes.- Approaches for combining AI and FEM.- FEM/AI models.- AI/FEM models.- Other approaches.- Artificial intelligence tools.- AI tools for modeling.- Artificial neural networks.- Fuzzy logic and neuro-fuzzy system.- Bayesian probability.- AI tool for optimization.- Evolutionary algorithms.- Swarm intelligence.- Finite element method for manufacturing modeling.- Plasticity models.- Constitutive equations.- Finite element discretization.- Case of study.- Experimental study.- FEM-based modeling.- AI-based modeling.- Optimization.- Concluding remarks.
Relevance and convenience of hybrid (AI and FEM) modeling and optimization of manufacturing processes.- Approaches for combining AI and FEM.- FEM/AI models.- AI/FEM models.- Other approaches.- Artificial intelligence tools.- AI tools for modeling.- Artificial neural networks.- Fuzzy logic and neuro-fuzzy system.- Bayesian probability.- AI tool for optimization.- Evolutionary algorithms.- Swarm intelligence.- Finite element method for manufacturing modeling.- Plasticity models.- Constitutive equations.- Finite element discretization.- Case of study.- Experimental study.- FEM-based modeling.- AI-based modeling.- Optimization.- Concluding remarks.
Relevance and convenience of hybrid (AI and FEM) modeling and optimization of manufacturing processes.- Approaches for combining AI and FEM.- FEM/AI models.- AI/FEM models.- Other approaches.- Artificial intelligence tools.- AI tools for modeling.- Artificial neural networks.- Fuzzy logic and neuro-fuzzy system.- Bayesian probability.- AI tool for optimization.- Evolutionary algorithms.- Swarm intelligence.- Finite element method for manufacturing modeling.- Plasticity models.- Constitutive equations.- Finite element discretization.- Case of study.- Experimental study.- FEM-based modeling.- AI-based modeling.- Optimization.- Concluding remarks.
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