Computational Technologies in Materials Science
portes grátis
Computational Technologies in Materials Science
Nandi, Ashutosh; Davim, J. Paulo; Tayal, Shubham; Singla, Parveen
Taylor & Francis Ltd
10/2021
238
Dura
Inglês
9780367640576
15 a 20 dias
621
Descrição não disponível.
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
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
M5P Trees;Fabrication of Materials;Ann Model;Crystals structures;Soft Computing Techniques;Material Designing;Metal Matrix Composites;Machine Learning;Tunneling Current;Material Optimization;RF;Computational Intelligence;RAE;Vice Versa;American Chemical Society;Testing Dataset;Compressive Strength;Asphalt Concrete Mix;Engineered Cementitious Composites;MD Simulation;RHA;Computational Intelligence Techniques;FTIR Spectroscopy;PA6 Composite;Marshall Stability;Stir Casting;SPS Temperature;Spindle Speed;Wear Rate;SCC
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
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
M5P Trees;Fabrication of Materials;Ann Model;Crystals structures;Soft Computing Techniques;Material Designing;Metal Matrix Composites;Machine Learning;Tunneling Current;Material Optimization;RF;Computational Intelligence;RAE;Vice Versa;American Chemical Society;Testing Dataset;Compressive Strength;Asphalt Concrete Mix;Engineered Cementitious Composites;MD Simulation;RHA;Computational Intelligence Techniques;FTIR Spectroscopy;PA6 Composite;Marshall Stability;Stir Casting;SPS Temperature;Spindle Speed;Wear Rate;SCC
