Machine Learning for Powder-Based Metal Additive Manufacturing

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. Covers machine learning (ML) methods for additive manufacturing (AM) of metals that will improve product quality, optimize manufacturing processes, and reduce costsCombines 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 applicationsDiscusses algorithm development of ML for metal AM, metal AM process modeling and optimization, mathematical and simulation studies of metal AM, and pre- and post-processing smart methods for metal AM INDICE: 1. Overview of Machine learning for additive manufacturing2. ML for Design in AM3. Machine learning for materials developments in metals additive manufacturing4. Geometrical deviation modelling by Machine learning5. Physics informed machine learning modelling of metal AM6. Machine learning enabled powder spreading process7. Machine learning for Metal AM process optimization8. Intelligent monitoring of metal additive manufacturing9. Post-processing optimisation of nano finishing by machine learning10. Data-driven cost estimation by Machine learning

• ISBN: 978-0-443-22145-3
• Editorial: Elsevier
• Encuadernacion: Rústica
• Páginas: 400
• Fecha Publicación: 01/11/2024
• Nº Volúmenes: 1
• Idioma: Inglés