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...

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...

This book presents recent advancements of machine learning methods and their applications in material science and nanotechnologies. It provides an introduction to the field and for those who wish to explore machine learning in modeling as well as conduct data analyses of material characteristics. The book discusses ways to enhance the material’s electrical and mechanical properties based on available regression methods for supervised learning and optimization of material attributes. In summary, the growing interest among academics and professionals in the field of machine learning methods...