Machine Learning Applied to Composite Materials
portes grátis
Machine Learning Applied to Composite Materials
Sanjay, M. R.; Madhushri, Priyanka; Kushvaha, Vinod; Siengchin, Suchart
Springer Verlag, Singapore
11/2022
198
Dura
Inglês
9789811962776
15 a 20 dias
Descrição não disponível.
Importance of machine learning in material science.- Machine Learning: A methodology to explain and predict material behavior.- Effect of aspect ratio on dynamic fracture toughness of particulate polymer composite using artificial neural network.- Methodology of K-Nearest Neighbor for predicting the fracture toughness of polymer composites.- Forward machine learning technique to predict dynamic fracture behavior of particulate composite.- Predictive modelling of fracture behavior in silica-filled polymer composite subjected to impact with varying loading rates.- Machine learning approach to determine the elastic modulus of Carbon fiber-reinforced laminates.- Effect of weight ratio on mechanical behaviour of natural fiber based biocomposite using machine learning.- Effect of natural fiber's mechanical properties and fiber matrix adhesion strength to design biocomposite.- Comparison of various machine learning algorithms to predict material behavior in GFRP.
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Machine Learning (ML);Materials Modelling;Composite Material Design;Artificial Neural Network;Fracture Toughness Prediction;Particulate Polymer Composite;Silica-Filled Polymer Composite;Carbon Fiber-Reinforced Laminates;Natural Fiber Biocomposite;Glass Fiber Reinforced Polymer (GFRP)
Importance of machine learning in material science.- Machine Learning: A methodology to explain and predict material behavior.- Effect of aspect ratio on dynamic fracture toughness of particulate polymer composite using artificial neural network.- Methodology of K-Nearest Neighbor for predicting the fracture toughness of polymer composites.- Forward machine learning technique to predict dynamic fracture behavior of particulate composite.- Predictive modelling of fracture behavior in silica-filled polymer composite subjected to impact with varying loading rates.- Machine learning approach to determine the elastic modulus of Carbon fiber-reinforced laminates.- Effect of weight ratio on mechanical behaviour of natural fiber based biocomposite using machine learning.- Effect of natural fiber's mechanical properties and fiber matrix adhesion strength to design biocomposite.- Comparison of various machine learning algorithms to predict material behavior in GFRP.
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
