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Vinod Kushvaha, Priyanka Madhushri, Priyanka Madhushri et al, M R Sanjay, M. R. Sanjay, M.R. Sanjay...
Machine Learning Applied to Composite Materials
English · Hardback
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Description
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 materialcomposite modelling and design.
List of contents
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.
About the author
Dr. Priyanka Madhushri is the Internet of Things (IoT) Ideation Research Engineer at Stanley Black and Decker (SBD), Atlanta. Dr. Madhushri earned her Ph.D. in Electrical Engineering from the University of Alabama in Huntsville, USA. She works with the innovation team and brings new ideas to various projects. As a researcher, she provides Proof of Concept (POC) to various SBD teams and assists in developing the company's software, hardware, and data analytics. Her research interestsinclude predictive analyses using Machine Learning, material modeling, Internet of Things (IoT), mobile computing, etc. She has published in various engineering fields, including materials journals, where her work focused on utilizing machine learning algorithms to predict and explain the mechanical behavior of advanced engineering materials.
Product details
Assisted by | Vinod Kushvaha (Editor), Priyanka Madhushri (Editor), Priyanka Madhushri et al (Editor), M R Sanjay (Editor), M. R. Sanjay (Editor), M.R. Sanjay (Editor), Ing. habil. Suchart Siengchin (Editor), Suchart Siengchin (Editor) |
Publisher | Springer, Berlin |
Languages | English |
Product format | Hardback |
Released | 01.12.2022 |
EAN | 9789811962776 |
ISBN | 978-981-1962-77-6 |
No. of pages | 198 |
Dimensions | 155 mm x 14 mm x 235 mm |
Illustrations | VI, 198 p. 71 illus., 61 illus. in color. |
Series |
Composites Science and Technology Composites Science and Technol |
Subject |
Natural sciences, medicine, IT, technology
> Technology
> Mechanical engineering, production engineering
|
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