Machine Learning for Powder-Based Metal Additive Manufacturing 1st Edition

Bold new resource for engineers and researchers working at the intersection of artificial intelligence and manufacturing: Machine Learning for Powder-Based Metal Additive Manufacturing, 1st Edition by Gurminder Singh, Farhad Imani, Asim Tewari, and Sushil Mishra presents a rigorous, application-focused roadmap for bringing data-driven methods into metal 3D printing workflows.

Start here if you want a practical, science-backed guide to applying machine learning across the powder-based metal AM value chain. The book translates core ML concepts into actionable strategies for defect prediction, process optimization, and materials design—covering supervised and unsupervised learning, deep learning, feature engineering, and model validation framed specifically for metal powder bed fusion and directed energy deposition processes.

Readers gain clear, real-world insights into building robust datasets, integrating in-situ process monitoring, mapping process-structure-property relationships, and deploying predictive models that support quality control and production scaling. Case studies and industry-relevant examples show how ML can reduce trial-and-error, shorten development cycles, and improve first-pass yield—benefits relevant to manufacturers, research labs, and engineering teams across North America, Europe, and Asia’s advanced manufacturing hubs.

Concise yet comprehensive, this edition is tailored for graduate students, R&D engineers, production managers, and academics seeking to bridge materials science and machine learning in additive manufacturing. With a focus on practical workflows and reproducible results, it’s an essential reference for anyone aiming to accelerate metal AM adoption with modern data science techniques.

Add this title to your professional library to start turning AM data into reliable, repeatable performance improvements.

Note: eBooks do not include supplementary materials such as CDs, access codes, etc.