About this Abstract |
Meeting |
2023 TMS Annual Meeting & Exhibition
|
Symposium
|
AI/Data Informatics: Computational Model Development, Validation, and Uncertainty Quantification
|
Presentation Title |
M-11: Optimized Print Parameter Prediction by Machine Learning |
Author(s) |
Kevin Graydon, Yongho Sohn |
On-Site Speaker (Planned) |
Kevin Graydon |
Abstract Scope |
While laser powder bed fusion offers the opportunity for novel engineering designs, determination of material printability through optimization studies remains expensive in terms of time and cost. Machine learning algorithms such as neural networks have been employed to minimize these two parameters and to predict the optimal print parameters for a given material. Utilizing our wide, in-house, print parameter dataset of ferrous and non-ferrous alloys and ICME tools such as Thermo-Calc, models are trained on the thermophysical properties, experimental print parameters, and their resulting relative densities. Modelling with thermophysical properties allows for use not only with current engineering alloys but also extends capabilities to future novel and made-for-AM materials. In this way, print parameters granting fully dense parts can be predicted thereby reducing development time and cost. |
Proceedings Inclusion? |
Planned: |
Keywords |
Additive Manufacturing, Machine Learning, ICME |