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.

This edited book focuses on processing, properties, and applications of bast fiber and its composites written by renowned researchers and academicians. The contents focus on properties such as rheological and dielectric of bast fiber composites. It also discusses its dynamic mechanical analysis, thermal stability of polymer composites reinforced with bast fibers, and water absorption behavior of bast fiber incorporated polymer composites. This book will be beneficial to both the industry and academia as it highlights possible avenues of future research.