Machine Learning Applied to Composite Materials
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Beschreibung
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.
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.
Introduces the approach of Machine Learning (ML) based predictive models in the design of composite materials Presents a design methodology of advanced composite materials based on different applications Provides an avenue for engineers and researchers working in field of composite materials design
Autor*in
Vinod Kushvaha
Themen in »Machine Learning Applied to Composite Materials«
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)