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.

The word fatigue has been a widely accepted term in engineering vocabulary for catastrophic failure of materials under cyclic loadings for more than a century. Cyclic loading causes damage and material property degradation in a cumulative manner. When a structure is subjected to cyclic stress (often much less than the static yield strength of the material) it fails after a number of cycles of stress application. Therefore, fatigue is the principal failure mechanism for structures under cyclic loading. To demonstrate the fatigue behavior, the composite laminates are modeled using the Finite Element Method (FEM). Using FEM results and Tsai-Hill criterion, fatigue life is computed for various laminate configurations. To validate the results, fatigue life assessed by Tsai-Hill and Tsai-Wu criteria is compared. The variations of fatigue life for different support conditions, load increments and possible lamina stacking sequences are studied. In addition, to present the fatigue behavior under variable amplitude loading, the Rainflow counting method is applied on irregular load history and fatigue life is computed for converted block loading.