| About this Abstract |
| Meeting |
2023 Annual International Solid Freeform Fabrication Symposium (SFF Symp 2023)
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| Symposium
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2023 Annual International Solid Freeform Fabrication Symposium (SFF Symp 2023)
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| Presentation Title |
Detecting Failures in Laser Powder Bed Fusion Additive Manufacturing of Complex Lattice Structures using Multi-sensor Data and Machine Learning |
| Author(s) |
Anis Asad, Benjamin D. Bevans, J-B Forien, Aiden Martin, Nick Calta, Philip DePond, Gabe Guss, Brian Giera, Prahalada Rao |
| On-Site Speaker (Planned) |
Benjamin D. Bevans |
| Abstract Scope |
The goal of this work is to detect the probability of strut failures in complex lattice structures built using laser powder bed fusion (LPBF). In pursuit of this goal, the objective is to predict the probability of strut failure as a function of heterogeneous sensor data from a pyrometer and a photodiode placed coaxially (in-line) via supervised machine learning models. The result is a 3D digital twin of the lattice created to demarcate areas of failure. This model was trained on a single lattice structure with artificially generated broken struts and tested on an additional lattice with smaller broken struts than the training lattice. In this work we show that the developed approach is capable of accurately detecting broken lattice struts with a statistical fidelity exceeding 80% (F-score) even when transferred to a different lattice geometry with finer resolution of breakage. |
| Proceedings Inclusion? |
Definite: Post-meeting proceedings |