Description:
Machine Learning for Powder-based Metal Additive Manufacturing outlines machine learning (ML) methods for additive manufacturing (AM) of metals that will improve product quality, optimize manufacturing processes, and reduce costs. The book combines ML and AM methods to develop intelligent models that train AM techniques in pre-processing, process optimization, and post-processing for optimized microstructure, tensile and fatigue properties, and biocompatibility for various applications. The book covers ML for design in AM, ML for materials development and intelligent monitoring in metal AM, both geometrical deviation and physics informed machine learning modeling, as well as data-driven cost estimation by ML.
In addition, optimization for slicing and orientation, ML to create models of materials for AM processes, ML prediction for better mechanical and microstructure prediction, and feature extraction by sensing data are all covered, and each chapter includes a case study.Brief description: Prof. Gurminder Singh is currently working as an assistant professor at the mechanical engineering department, Indian Institute of Technology Bombay, India. He worked as a postdoc researcher in the school of mechanical and materials engineering at University College Dublin (UCD), Dublin, Ireland. Before joining UCD, Dr Singh was a Postdoc fellow at SIMAP Lab, University of Grenoble Alpes, France, where his research focused on the experimental and simulation studies of the extrusion 3D printing of metals. Dr Singh was Gandhian young Technological Innovation Award for the development of a 3D printing method for the fabrication of patient-specific stents in 2020. He has published over 26 articles in peer-reviewed international journals, conference proceedings and books. He has published in leading journals such as Additive Manufacturing, Materials Science and Engineering: A, Journal of Manufacturing Processes, Rapid Prototyping, etc.