About this Abstract |
Meeting |
2023 TMS Annual Meeting & Exhibition
|
Symposium
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AI/Data Informatics: Computational Model Development, Validation, and Uncertainty Quantification
|
Presentation Title |
Developing a Physics-informed Machine Learning Model to Predict Melt-pool Shape in Additive Manufacturing |
Author(s) |
Mohammad Parsazadeh, Sharma Shashank, Sameehan S Joshi, Venkata mani Krishna Karri, Narendra Dahotre |
On-Site Speaker (Planned) |
Mohammad Parsazadeh |
Abstract Scope |
The laser powder bed fusion process is powder-based additive manufacturing, which is capable to produce 3D-printed parts from a 3D CAD model. Processing parameters are highly affecting the quality of the 3D-printed parts, which are linked to the shape of the melt pools. In this study, an extensive database is obtained by a single-track deposition of Ti-6Al-4V at various processing conditions. The micrographs of these single-track depositions are used to find the melt pool shapes. A set of dimensionless numbers, affecting the melt pool shapes is identified using scaling analysis. The melt pool shapes are then linked to these dimensionless numbers using a supervised machine learning model. The model is later used to predict the melt pool shape and the dominant heat transfer mode during melt pool formation. |
Proceedings Inclusion? |
Planned: |
Keywords |
Additive Manufacturing, Titanium, Machine Learning |