Research Catalog

Title

Hybrid modeling and optimization of manufacturing : combining artificial intelligence and finite element method / Ramón Quiza, Omar López-Armas, J. Paulo Davim.

Author

Quiza, Ramón.

Publication

Heidelberg ; New York : Springer, ©2012.

Items in the Library & Off-site

Details

Additional Authors

• López-Armas, Omar.
• Davim, J. Paulo.

Description

viii, 95 pages : illustrations; 23 cm.

Summary

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.

Series Statement

SpringerBriefs in applied sciences and technology. Computational mechanics, 2191-5342

Uniform Title

SpringerBriefs in applied sciences and technology. Computational mechanics

Subject

• Manufacturing processes > Mathematical models
• Hybrid computer simulation
• Artificial intelligence
• Finite element method
• Artificial Intelligence
• artificial intelligence

Bibliography (note)

• Includes bibliographical references and index.

Contents

Machine generated contents note: 1. Introduction -- 1.1. Relevance and Convenience of Hybrid Modeling and Optimization of Manufacturing Processes -- 1.2. Approaches for Combining AI and FEM -- 1.2.1. FEM/AI Models -- 1.2.2. AI/FEM Models -- 1.2.3. Hybrid Approaches for Optimization -- 1.2.4. Fuzzy FEM -- References -- 2. Finite Element in Manufacturing Processes -- 2.1. Basis of the Finite Element Method -- 2.2. FEM for Linear Elastostatic Problems -- 2.3. FEM for Plasticity -- 2.3.1. Plasticity Fundamentals -- 2.3.2. Material Behavior Models -- 2.3.3. Yielding Criteria -- 2.3.4. Governing Equations -- 2.3.5. FEM Formulation -- 2.4. Thermal Analysis -- 2.5. Friction Models -- 2.6. Fracture -- References -- 3. Artificial Intelligence Tools -- 3.1. Preliminary Concepts -- 3.2. Artificial Neural Networks -- 3.2.1. Biological Foundations and Neuron Model -- 3.2.2. Network Topology and Learning -- 3.2.3. Multilayer Perceptron -- 3.2.4. Radial Basis Function Networks -- 3.2.5. Hopfield Networks -- 3.2.6. Adaptive Resonance Theory and Self-Organizing Maps -- 3.2.7. Warnings and Shortcomings in the Use of Neural Networks -- 3.3. Fuzzy Logic -- 3.4. Neuro-Fuzzy Systems -- 3.5. Metaheuristic Optimization -- 3.5.1. Optimization Basis -- 3.5.2. Evolutionary Computation -- 3.5.3. Evolutionary Multi-Objective Optimization -- 3.5.4. Swarm Intelligence -- References -- 4. Case of Study -- 4.1. Case Description -- 4.2. Finite Element Method Based Modeling -- 4.2.1. Model Description -- 4.2.2. Outcomes of the FEM -- 4.3. Empirical Modeling -- 4.3.1. Statistical Modeling -- 4.3.2. Neural Network-Based Modeling -- 4.4. Optimization -- 4.5. Concluding Remarks -- References.

ISBN

• 9783642280849
• 3642280846

LCCN

2012931420

OCLC

• ocn794912618
• 794912618
• SCSB-9321431

Owning Institutions

Princeton University Library