Composite Materials Technology
Neural Network Applications
1st Edition
Published on 23. December 2009
368 pages
978-1-4200-9333-9 (ISBN)
Description
Artificial neural networks (ANN) can provide new insight into the study of composite materials and can normally be combined with other artificial intelligence tools such as expert system, genetic algorithm, and fuzzy logic. Because research on this field is very new, there is only a limited amount of published literature on the subject.Compiling in
More details
Edition
1. Auflage
Language
English
Place of publication
Baton Rouge
United States
Publishing group
Taylor & Francis Inc
Target group
Professional and scholarly
Material scientists and engineers, chemists, and mechanical engineers working with composite materials and computation.
Illustrations
N/A, 150 s/w Abbildungen
150 b/w images and Over 100
ISBN-13
978-1-4200-9333-9 (9781420093339)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Book
09/2019
1st Edition
CRC Press
€81.82
Shipment within 15-20 days
Book
12/2009
1st Edition
CRC Press
€267.85
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Persons
S. M. Sapuan is a professor of composite materials and the head of the Department of Mechanical and Manufacturing Engineering, Universiti Putra Malaysia (UPM). He is the vice president and honorary member of Asian Polymer Association; fellow of Institute of Materials, Malaysia; life fellow, International Biographical Association; life member, Institute of Energy, Malaysia; member, Society of Automotive Engineers International; member, International Association of Engineers; member, Plastics and Rubber Institute, Malaysia; and a professional engineer. He has published more than 200 papers in refereed journals, more than 200 papers in conferences/seminars, and six books in engineering.
I. M. Mujtaba is a professor of computational process engineering in the School of Engineering, Design and Technology at the University of Bradford, UK. He is a fellow of the IChemE, a chartered chemical engineer, and a chartered scientist. Professor Mujtaba is actively involved in many research areas like dynamic modeling, simulation, optimization, and control of batch and continuous chemical processes with specific interests in distillation, industrial reactors, refinery processes, and desalination. He has published more than 110 technical papers in major engineering journals, international conference proceedings, and books.
I. M. Mujtaba is a professor of computational process engineering in the School of Engineering, Design and Technology at the University of Bradford, UK. He is a fellow of the IChemE, a chartered chemical engineer, and a chartered scientist. Professor Mujtaba is actively involved in many research areas like dynamic modeling, simulation, optimization, and control of batch and continuous chemical processes with specific interests in distillation, industrial reactors, refinery processes, and desalination. He has published more than 110 technical papers in major engineering journals, international conference proceedings, and books.
Content
Application of Artificial Neural Network in Composites Materials. Network Approaches for Defect Detection in Composite Materials. The Use of Artificial Neural Networks in Damage Detection and Assessment in Polymeric Composite Structures. Damage Identification and Localization of Carbon Fiber-Reinforced Plastic Composite Plate Using Outlier Analysis and Multilayer Perceptron Neural Network. Damage Localization of Carbon Fiber-Reinforced Plastic Composite and Perspex Plates Using Novelty Indices and the Cross-Validation Set of Multilayer Perceptron Neural Network. Impact Damage Detection in a Composite Structure Using Artificial Neural Network. Artificial Neural Networks for Predicting the Mechanical Behavior of Cement-Based Composites after 100 Cycles of Aging. Fatigue Life Prediction of Fiber-Reinforced Composites Using Artificial Neural Networks. Optimizing Neural Network Prediction of Composite Fatigue Life Under Variable Amplitude Loading Using Bayesian Regularization. Free Vibration Analysis and Optimal Design of the Adhesively Bonded Composite Single Lap and Tubular Lap Joints. Determining Initial Design Parameters by Using Genetically. Optimized Neural Network Systems. Development of a Prototype Computational Framework for Selection of Natural Fiber-Reinforced Polymer Composite Materials Using Neural Network. Index.
