Computational Technologies in Materials Science (Paperback, 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 Aluminum Matrix Composites Chapter 5 Spark Plasma-Induced Combustion Synthesis, Densification, and Characterization of Nanostructured Magnesium Silicide for Mid Temperature Energy Conversion Energy Harvesting Application Chapter 6 The Role of Computational Intelligence in Materials 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