Machine Learning for Powder-Based Metal Additive Manufacturing

Erscheinungsjahr 2024
ISBN: 978-0-443-22145-3
Verlag: Elsevier Science

Buch, Englisch, 290 Seiten, Format (B × H): 150 mm x 229 mm, Gewicht: 417 g

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.

Autoren/Hrsg.

Singh, Gurminder

Imani, Farhad

Tewari, Asim

Mishra, Sushil

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen
• Technische Wissenschaften Verfahrenstechnik | Chemieingenieurwesen | Biotechnologie Metallurgie
• Technische Wissenschaften Maschinenbau | Werkstoffkunde Technische Mechanik | Werkstoffkunde Materialwissenschaft: Metallische Werkstoffe
• Technische Wissenschaften Maschinenbau | Werkstoffkunde Produktionstechnik Computergestützte Fertigung
• Technische Wissenschaften Verfahrenstechnik | Chemieingenieurwesen | Biotechnologie Pulvertechnologische Verfahren

Inhaltsverzeichnis

1. Overview of Machine learning for additive manufacturing
2. ML for Design in AM
3. Machine learning for materials developments in metals additive manufacturing
4. Geometrical deviation modelling by Machine learning
5. Physics informed machine learning modelling of metal AM
6. Machine learning enabled powder spreading process
7. Machine learning for Metal AM process optimization
8. Intelligent monitoring of metal additive manufacturing
9. Post-processing optimisation of nano finishing by machine learning
10. Data-driven cost estimation by Machine learning

Über Autor(innen)

Mishra, Sushil
Prof. Sushil Mishra is currently Professor in the Department of Mechanical Engineering, IIT Bombay. Prior to joining this position, he worked as Senior Manager in Aditya Birla Science and Technology Center, Mumbai and as senior researcher at General Motors Global R&D, Bangalore. He has more than four years of post doctorate research experience in corporate R&D. The focus of his research is multiscale physics based formability studies of metallic alloys. Currently he is heading Micro- Forming lab ( www.microforming.in )in IIT Bombay, where more than 15 postgraduate students are working in the area of forming, Additive manufacturing, microstructure, microtexture and materials modeling. He has published more than 70 peer reviewed international journal papers and 3 patents. Under his supervision 12 PhD and 15 M Tech students are graduated and currently 15 students are working under him as a postgraduate student at various sponsored project.

Singh, Gurminder
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.

Tewari, Asim
Prof. Asim Tewari is a Professor in the Department of Mechanical Engineering and a faculty member of the Center for Machine Intelligence and Data Science (C-MInDS) at the Indian Institute of Technology Bombay, Mumbai. Prior to this, he was a staff researcher at General Motors, Global R&D center in Bangalore. At IIT Bombay, he has been instrumental in setting up the National Center for Aerospace Innovation and Research and Center for Technical Textiles. He teaches short courses to industries on theory and application of machine learning data analytics and applied Finite Element Analysis. His personal area of research is in mathematical models for microstructural-mechanics. He has over 100 international journal & conference publications and ten international patents. His pioneering work in 3D microscopy imaging has been widely cited, including reproduction in ASM handbooks. He is on the editorial board of several international Journals, including Metallurgical and Materials Transactions and Image Analysis & Stereology. He is an advisory committee member for various national & international research boards and has won several awards and recognition for his research and teaching.

Imani, Farhad
Prof. Farhad Imani is an assistant professor in the Department of Mechanical Engineering at the University of Connecticut (UConn). He is also an affiliated faculty in Institute of Material Science and Management and Engineering for Manufacturing programs at UConn. His research interests focus on data analytics, machine learning, statistical learning, and decision theory for process monitoring and control, system diagnostics and prognostics, quality and reliability improvement with applications in advanced manufacturing). Since his start of his appointment at University of Connecticut from 2020, he has obtained fundings (in total more than $4M) from multiple federal agencies, including ONR National Institute for Undersea Vehicle Technology (NIUVT), ARMY Combat Capabilities Development Command (DEVCOM) Ground Vehicle Systems Center (GVSC), Department of Army, and Department of Education. He has published over 40 articles in peer-reviewed international journals and conference proceedings such as Journal of Manufacturing Processes, ASME Journal of Manufacturing Science and Engineering, Journal of Manufacturing Systems, Smart and Sustainable Manufacturing Systems, IEEE Design Automation Conference, and International Solid Freeform Fabrication Symposium.