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.