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Springer Machine Learning Applied to Composite Materials - Springer

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Description

Explore the intersection of machine learning and composite materials engineering with this comprehensive resource from Springer. This book examines how artificial intelligence and machine learning techniques can optimise the design, analysis, and performance prediction of composite materials across industries. Ideal for researchers, engineers, and students, the text bridges the gap between computational methods and material science, offering practical insights into applying AI algorithms to solve complex problems in composite material development. Whether you are working on aerospace applications, automotive components, or advanced manufacturing, this volume provides essential knowledge for leveraging machine learning to enhance material properties, reduce development time, and improve predictive accuracy in composite engineering projects.

Key Features

Machine Learning Applied to Composite Materials examines advanced AI techniques for optimising composite material properties and performance.

Springer's comprehensive approach covers both theoretical foundations and practical applications of machine learning in materials science.

The book addresses key topics in composite engineering, including design optimisation, quality control, and predictive modelling using computational methods.

Ideal resource for researchers, academics, and engineers seeking to integrate machine learning methodologies into composite materials research and development.

Part of the Composites Science and Technology series, ensuring peer-reviewed, authoritative content aligned with current industry standards.

Product Specifications

Format
hardcover
Domain
Amazon UK
Release Date
30 November 2022
Listed Since
28 July 2022

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