About this Abstract |
Meeting |
2022 Annual International Solid Freeform Fabrication Symposium (SFF Symp 2022)
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Symposium
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2022 Annual International Solid Freeform Fabrication Symposium (SFF Symp 2022)
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Presentation Title |
Transferring Printability Knowledge Across SS316L and IN718 in Laser Directed Energy Deposition using Machine Learning |
Author(s) |
Nandana Menon, Sudeepta Mondal, Amrita Basak |
On-Site Speaker (Planned) |
Nandana Menon |
Abstract Scope |
Laser-directed energy deposition additive manufacturing processes have several parameters that impact the melt pool properties, which in turn affect the microstructure of the part. Computational investigations are regularly implemented; however, these investigations must be repeated for each material of interest. In this paper, a transfer learning approach is proposed to address this challenge. Using an analytical model, input-output data pairs are generated for a nickel-based alloy, IN718, and an iron-based alloy, SS316L. A baseline neural network is trained for each alloy. The capability of these baseline networks is interchangeably analyzed with a parametric retraining of the percentage of data used and the number of retrained layers of the baseline network. With just 10% data, and one retrained layer, accuracies above 90% are observed. The results show that the acquired printability knowledge can be transferred across material systems without requiring a significant amount of data from the second material system. |
Proceedings Inclusion? |
Definite: Post-meeting proceedings |