Machine Learning for Powder-Based Metal Additive Manufacturing

Herausgeber: Singh, Gurminder; Mishra, Sushil; Tewari, Asim; Imani, Farhad

• Broschiertes Buch

Produktbeschreibung

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.

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Produktdetails

• Verlag: Elsevier Science
• Seitenzahl: 290
• Erscheinungstermin: 9. September 2024
• Englisch
• Gewicht: 450g
• ISBN-13: 9780443221453
• ISBN-10: 0443221456
• Artikelnr.: 70254125

Inhaltsangabe

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