Computational Technologies in Materials Science (Hardcover, 1)

Chapter 1 Fabrication and Characterization of Materials Chapter 2 Application to Advanced Materials Simulation Chapter 3 Molecular dynamics simulations for structural characterization and property prediction of materials Chapter 4 Desirability approach based Optimization of process parameters in Turning of Aluminium Matrix CompositesChapter 5 Computational Techniques in Materials Science Chapter 6 The Role of Computational Intelligence in Material Science: An Overview Chapter 7 Characterization Techniques for Composites using AI and machine learning techniques Chapter 8 Experimental evaluation on tribological behavior of TiO2 reinforced polyamide composites validated by Taguchi and Machine Learning Methods Chapter 9 Prediction of compressive strength of SCC containing metakaolin and rice husk ash using machine learning algorithms Chapter 10 Predicting Compressive Strength of Concrete Matrix using Engineered Cementitious Composites: A Comparative Study between ANN and RF Models Chapter 11 Estimation of Marshall Stability of Asphalt Concrete Mix using Neural Network and M5P Tree