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Machine learning applied to composit...

Kushvaha, Vinod.

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Machine learning applied to composite materials

Record Type:	Electronic resources : Monograph/item
Title/Author:	Machine learning applied to composite materials/ edited by Vinod Kushvaha ... [et al.].
other author:	Kushvaha, Vinod.
Published:	Singapore :Springer Nature Singapore : : 2022.,
Description:	vi, 198 p. :ill., digital ;24 cm.
[NT 15003449]:	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.
Contained By:	Springer Nature eBook
Subject:	Composite materials - Design. -
Online resource:	https://doi.org/10.1007/978-981-19-6278-3
ISBN:	9789811962783

Machine learning applied to composite materials
Machine learning applied to composite materials[electronic resource] /edited by Vinod Kushvaha ... [et al.]. - Singapore :Springer Nature Singapore :2022. - vi, 198 p. :ill., digital ;24 cm. - Composites science and technology,2662-1827. - Composites science and technology..

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.

This book introduces the approach of Machine Learning (ML) based predictive models in the design of composite materials to achieve the required properties for certain applications. ML can learn from existing experimental data obtained from very limited number of experiments and subsequently can be trained to find solutions of the complex non-linear, multi-dimensional functional relationships without any prior assumptions about their nature. In this case the ML models can learn from existing experimental data obtained from (1) composite design based on various properties of the matrix material and fillers/reinforcements (2) material processing during fabrication (3) property relationships. Modelling of these relationships using ML methods significantly reduce the experimental work involved in designing new composites, and therefore offer a new avenue for material design and properties. The book caters to students, academics and researchers who are interested in the field of material composite modelling and design.

ISBN: 9789811962783

Standard No.: 10.1007/978-981-19-6278-3doiSubjects--Topical Terms:

3609288
Composite materials
--Design.

LC Class. No.: TA418.9.C6

Dewey Class. No.: 620.1180285631

Machine learning applied to composite materials
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505 0 $a 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.
520 $a This book introduces the approach of Machine Learning (ML) based predictive models in the design of composite materials to achieve the required properties for certain applications. ML can learn from existing experimental data obtained from very limited number of experiments and subsequently can be trained to find solutions of the complex non-linear, multi-dimensional functional relationships without any prior assumptions about their nature. In this case the ML models can learn from existing experimental data obtained from (1) composite design based on various properties of the matrix material and fillers/reinforcements (2) material processing during fabrication (3) property relationships. Modelling of these relationships using ML methods significantly reduce the experimental work involved in designing new composites, and therefore offer a new avenue for material design and properties. The book caters to students, academics and researchers who are interested in the field of material composite modelling and design.
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