Artificial Intelligence and Machine Learning for Material Characterizations and Mechanical Properties

The Introduction chapter serves as a starting point, providing a brief introduction to the topic and highlighting the importance of AI and ML in material characterizations and mechanical properties. Then it expands on the concept of AI and ML in material science, giving a general overview of these technologies and discussing their potential applications in the field. Moreover, it focuses on the fundamentals of material properties and characterizations. It explains the mechanical properties of materials and discusses common material characterization techniques such as Support Vector Machine (SVM), k-Nearest Neighbor (k-NN), and Artificial Neural Networks (ANNs). In Chapter 4 , the authors delve into the predictive modeling aspect using AI and ML techniques. It covers specific algorithms such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Deep Belief Networks (DBNs), and Deep Transfer Learning. These algorithms are explored in the context of material characterizations and predicting mechanical properties. Overall, this book aims to provide a comprehensive understanding of how AI and ML techniques can be effectively used in material science for characterizations and predictions of mechanical properties.