ABSTRACT
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
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.
