Bringing together the work of leading researchers from all parts of the world, this book provides readers with an understanding of the various applications of artificial neural networks (ANN) involved in composite material technology. Covering composite mechanics, materials characterization, product design, and other important areas, it first presents a review of the literature and then offers recent research on neural network approaches for defect detection in a variety of composite materials. It also explores techniques for selecting, predicting the behavior, and monitoring composite materials.…mehr
Bringing together the work of leading researchers from all parts of the world, this book provides readers with an understanding of the various applications of artificial neural networks (ANN) involved in composite material technology. Covering composite mechanics, materials characterization, product design, and other important areas, it first presents a review of the literature and then offers recent research on neural network approaches for defect detection in a variety of composite materials. It also explores techniques for selecting, predicting the behavior, and monitoring composite materials.
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.
Inhaltsangabe
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.
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.
Es gelten unsere Allgemeinen Geschäftsbedingungen: www.buecher.de/agb
Impressum
www.buecher.de ist ein Shop der buecher.de GmbH & Co. KG Bürgermeister-Wegele-Str. 12, 86167 Augsburg Amtsgericht Augsburg HRA 13309
