About this Abstract |
Meeting |
2024 Annual International Solid Freeform Fabrication Symposium (SFF Symp 2024)
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Symposium
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2024 Annual International Solid Freeform Fabrication Symposium (SFF Symp 2024)
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Presentation Title |
Toward Rapid Process Qualification of Laser Powder Bed Fusion Additive Manufacturing Using Physics-Based Model Predictive Control |
Author(s) |
Prahalada K. Rao, Alex Riensche, Benjamin Bevans, Antonio Carrington, Kaustubh Deshmukh, Kamden Shephard, John Sions, Kyle Snyder, Yuri Plotnikov, Kevin Cole |
On-Site Speaker (Planned) |
Prahalada K. Rao |
Abstract Scope |
We developed and applied a physics-guided model predictive control approach to autonomously optimize the processing parameters for an LPBF part before it is printed. The control approach creates a customized layer-by-layer processing plan for each LPBF part shape, such that the model-predicted thermal history of a part matches a predetermined ideal or target thermal history. Currently, LPBF processing parameters are optimized through empirical studies based on simple test coupons. However, processing parameters optimized for test coupons seldom transfer to practical parts necessitating further testing. We demonstrate, with relatively complex stainless steel 316L parts processed on a commercial EOS M290 LPBF machine, the following advantageous outcomes from using the physics-based model predictive control approach: (i) elimination of anchoring supports in parts with prominent overhang features; (ii) improvement in geometric accuracy and surface integrity of hard to access internal features; and (iii) reduction in microstructure heterogeneity resulting in consistent part properties. |
Proceedings Inclusion? |
Definite: Post-meeting proceedings |