| ISBN13: | 9781032568522 |
| ISBN10: | 1032568526 |
| Binding: | Hardback |
| No. of pages: | 214 pages |
| Size: | 234x156 mm |
| Weight: | 560 g |
| Language: | English |
| Illustrations: | 104 Illustrations, black & white; 10 Illustrations, color; 23 Tables, black & white |
| 698 |
Biology in general
Organic chemistry
Engineering in general
Chemical engineering and industry
Electrical engineering and telecommunications, precision engineering
Mechanical Engineering Sciences
Artificial Intelligence
Environmental sciences
Rubber and plastics industry
Further readings in the field of technology
Biology in general (charity campaign)
Organic chemistry (charity campaign)
Engineering in general (charity campaign)
Chemical engineering and industry (charity campaign)
Electrical engineering and telecommunications, precision engineering (charity campaign)
Mechanical Engineering Sciences (charity campaign)
Artificial Intelligence (charity campaign)
Environmental sciences (charity campaign)
Rubber and plastics industry (charity campaign)
Further readings in the field of technology (charity campaign)
Sustainable Materials
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The book explores the use of AI and ML techniques for the design, characterization, and development of prediction analysis of sustainable polymer composites.
The self-learning ability of machine learning algorithms makes the investigations more accurate and accommodates all the complex requirements. Development in neural codes can accommodate the data in all the forms such as numerical values as well as images. The techniques also review the sustainability, life-span, the energy consumption in production polymer, etc. This book addresses the design, characterization, and development of prediction analysis of sustainable polymer composites using machine learning algorithms.
Preface. Artificial Intelligence in Material Science. Data Driven Artificial Intelligence Based Approach for the Determination of Structural Stress Distribution in ASTM D3039 Tensile Specimens of Carbon-Epoxy and Kevlar-Epoxy Based Composite Materials. Image Segmentation for Evaluating the Microstructure Features obtained from Magnesium Composites Processed through Squeeze Casting. Experimental Investigation of Bagasse Ash in Concrete Material. Computational Material Science for Cheminformatics Feature Descriptive Language (CFDL) with Categorical Data. Explicit Dynamic Crash Analysis of a Car using a Metal, Composite Material and an Alloy. Optimizing Friction Stir Spot Welded ABS Weld Strength using JAYA and Cohort Intelligence Algorithm. Supervised Machine Learning Based Classification of Dimensional Deviation of FDM 3D Printed Samples. Polymer Composite Flexural Strength Estimation using K-Nearest Neighbouring Classification Algorithm. Supervised Machine Learning Based Classification of Surface Roughness of Fused Deposition Modeling3D Printed Samples. Polymer Composite Impact Strength Estimation using K-Nearest Neighbouring Classification Algorithm. Index.
