| Shape deviation generator—a convolution framework for learning and predicting 3-D printing shape accuracy Q Huang, Y Wang, M Lyu, W Lin IEEE Transactions on Automation Science and Engineering 17 (3), 1486-1500, 2020 | 57 | 2020 |
| Efficiently registering scan point clouds of 3D printed parts for shape accuracy assessment and modeling N Decker, Y Wang, Q Huang Journal of Manufacturing Systems 56, 587-597, 2020 | 39 | 2020 |
| Geometric accuracy prediction and improvement for additive manufacturing using triangular mesh shape data N Decker, M Lyu, Y Wang, Q Huang Journal of Manufacturing Science and Engineering 143 (6), 061006, 2021 | 30 | 2021 |
| A non-destructive resonant acoustic testing and defect classification of additively manufactured lattice structures AF Obaton, Y Wang, B Butsch, Q Huang Welding in the World 65 (3), 361-371, 2021 | 23 | 2021 |
| Learning and predicting shape deviations of smooth and non-smooth 3d geometries through mathematical decomposition of additive manufacturing Y Wang, C Ruiz, Q Huang IEEE Transactions on Automation Science and Engineering 20 (3), 1527-1538, 2022 | 13 | 2022 |
| Extended Fabrication-Aware Convolution Learning Framework for Predicting 3D Shape Deformation in Additive Manufacturing Y Wang, C Ruiz, Q Huang 2021 IEEE 17th International Conference on Automation Science and …, 2021 | 8 | 2021 |
| A Shape Registration Methodology for Geometric Deviation Correction in Additive Manufacturing Y Wang, C Ruiz, S Park, KH Shin, JH Kim, Q Huang International Manufacturing Science and Engineering Conference 85802 …, 2022 | 2 | 2022 |
Qiang HuangUniversity of Southern California, Professor of ISE, FIISE, FASME, SMNAI在 usc.edu 的电子邮件经过验证
