Sustainable Materials : The Role of Artificial Intelligence and Machine Learning

Mishra, Akshansh (EDT)/ S Jatti, Vijaykumar (EDT)/ Paliwal, Shivangi (EDT)

CRC Press（2024/10発売）

• 製本 Hardcover:ハードカバー版／ページ数 208 p.
• 言語 ENG
• 商品コード 9781032568522
• DDC分類 620.118

Full Description

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.

Contents

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.