About this Abstract |
Meeting |
2021 TMS Annual Meeting & Exhibition
|
Symposium
|
AI/Data informatics: Design of Structural Materials
|
Presentation Title |
Alloy Design for Additive Manufacturing |
Author(s) |
Mariam Assi, Julien Favre, Anna Fraczkiewicz, Franck Tancret |
On-Site Speaker (Planned) |
Mariam Assi |
Abstract Scope |
In recent years, the development of innovatory processes like “additive manufacturing” (AM) has opened new fields in modern metallurgy. These new processes are also at the origin of development of new processable or printable alloys. The aim of this study is to propose a computational method based on Bayesian machine learning (ML) algorithms combined with a thermodynamic approach (CALPHAD) and integrated in a multi-objective genetic algorithm (GA) to design AM-optimized alloys. In this context, several material criteria influencing the defects commonly observed in AM-fabricated parts, such as solidification cracking, porosity, residual stresses and distortions, were taken into account. The proposed model uses data sets constructed from published literature and industrial material datasheets. Its application to design improved grades of austenitic stainless steels will be shown and discussed. |
Proceedings Inclusion? |
Planned: |