Sustainable Materials (eBook, ePUB)

The Role of Artificial Intelligence and Machine Learning
Redaktion: Mishra, Akshansh; Paliwal, Shivangi; S Jatti, Vijaykumar

• Format: ePub

Produktbeschreibung

The book explores the use of AI and ML techniques for the design, characterization, and development of prediction analysis of sustainable polymer composites.

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Produktdetails

• Verlag: Taylor & Francis
• Erscheinungstermin: 25. Oktober 2024
• Englisch
• ISBN-13: 9781040154281
• Artikelnr.: 72275302

Autorenporträt

Akshansh Mishra is pursuing a Master's in Materials Engineering and Nanotechnology at Politecnico Di Milano, Milan, Italy. He works on the application of Artificial Intelligence-based algorithms in the Manufacturing and Materials sectors. His main research interests are Cognitive Computing, Advanced Manufacturing, Explainable Artificial Intelligence (XAI), Machine Learning, Natural Language Processing, Nature-based optimization algorithms, and Composite Materials. Vijaykumar S Jatti is an Associate Professor at Symbiosis Institute of Technology, Pune, India. His main research interests are Machine Learning, Mechanical Design, Material Science, Conventional & Non-Conventional Machining Processes, Additive Manufacturing, and Bio-Materials (Metals, Ceramics and Polymers). He has several publications in WoS and Scopus indexed journals. He has received 18 awards in academics & research works. Shivangi Paliwal is pursuing a Ph.D. in Mechanical Engineering, at the University of Kentucky, USA. Before joining the University of Kentucky, she worked as a Junior Research Fellow at the Indian Institute of Technology, Mumbai, India. Her research work integrates experimental and numerical simulation techniques to leverage the potential of additive manufacturing. Her research work reviews sustainability through the use of non-traditional machining and surface engineering.

Inhaltsangabe

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.