Sponsor: Industry (2019-2021)
This project is focused using machine learning methods to conduct reverse engineering of microstructures of 3D printed materials. Reverse engineering a product in high quality requires not only reverse engineering the shape but also the microstructure. Use of machine learning methods on optical microstructures, scanning electron micrographs and CT-scans can allow reconstructing the tool path of the speicmen to build an exact replica. The project is considering several technologies, including fused filament fabrication (FFF or FDM) and direct metal laser sintering (DMLS) for tool path reconstruction of polymer matrix composites and metallic materials, respectively.
Participants:
- Gary Mac, Ph.D. Candidate
- Guan Lin Chen, M.S. Student
- Alexander Zwiren (M.S. Graduated)
Publications:
- Kaushik Yanamandra, Guan Lin Chen, Xianbo Xu, Gary Mac, Nikhil Gupta, Reverse engineering of additive manufactured composite part by toolpath reconstruction using imaging and machine learning, Composites Science and Technology, Volume 198, 2020, paper #108318.