Share two new journal papers: one extending the fabrication-aware convolution learning framework to a broader class of 3D geometries for 3D printing accuracy control, and the other providing accuracy control for Wire and Arc Additive Manufacturing:
- Yuanxiang Wang*, Cesar Ruiz*, and Q. Huang, 2022, “Learning and Predicting Shape Deviations of Smooth and Non-Smooth 3D Geometries through Mathematical Decomposition of Additive Manufacturing, ” IEEE Transactions on Automation Science and Engineering, DOI: 10.1109/TASE.2022.3174228, in press.
- Cesar Ruiz*, Davoud Jafari, Vignesh V. Subramanian, Tom H.J. Vaneker, Wei Ya, and Qiang Huang, 2022, “Prediction and Control of Product Shape Quality in Wire and Arc Additive Manufacturing Using Generalized Additive Models,” ASME Transactions, Journal of Manufacturing Science and Engineering, DOI: 10.1115/1.4054721, in press.
