Composite Materials Technology: Neural Network Applications

by S.M. Sapuan (Editor), Iqbal Mohammed Mujtaba (Editor)

Overview

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

Product Details

ISBN-13:	9781040209356
Publisher:	CRC Press
Publication date:	12/23/2009
Sold by:	Barnes & Noble
Format:	eBook
Pages:	368
File size:	7 MB

About the Author

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.

Table of Contents

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.