| About this Abstract |
| Meeting |
2023 Annual International Solid Freeform Fabrication Symposium (SFF Symp 2023)
|
| Symposium
|
2023 Annual International Solid Freeform Fabrication Symposium (SFF Symp 2023)
|
| Presentation Title |
Additive Manufacturing (AM) Lattice Segmentation and Analysis Enabled through Deep Learning |
| Author(s) |
Michael Juhasz, Gabe Guss, J. B. Forien, Nick Calta |
| On-Site Speaker (Planned) |
Michael Juhasz |
| Abstract Scope |
Ex-situ computed tomography (CT) analysis of Additive Manufacturing (AM) produced parts is commonplace as a means of Non-Destructive Evaluation (NDE) quality assurance. Most CT examinations focus on porosity, both from keyholing or entrained gas. With the recent acceleration in image processing enabled through Deep Learning/Machine Learning (ML/DL), this presentation suggests expanding CT analysis of AM parts to extend beyond porosity analysis to encompass the study of other requirement-driven, critical geometries. This was applied to AM produced lattices which underwent CT and were subsequently segmented into component pieces. These segmented components were then registered to in-situ diagnostic signals for comparison where dependence and correlation was assessed, and it is those results which will be presented. This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344. |
| Proceedings Inclusion? |
Definite: Post-meeting proceedings |