|About this Abstract
||7th World Congress on Integrated Computational Materials Engineering (ICME 2023)
||Deep Learning enabled Additive Manufacturing (AM) lattice segmentation
||Michael Juhasz, Nick Calta
|On-Site Speaker (Planned)
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. As a case study, AM produced lattices underwent CT and were subsequently segmented into component pieces. These segmented components can be examined individually or as a larger subset where statistics can be taken, with an eye to drawing comparison and correlation to both ex-situ mechanical properties and in-situ process signatures. This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344.
||Planned: Other (describe below)