| Deep learning predicts path-dependent plasticity M Mozaffar, R Bostanabad, W Chen, K Ehmann, J Cao, MA Bessa Proceedings of the National Academy of Sciences 116 (52), 26414-26420, 2019 | 428 | 2019 |
| Data-driven multi-scale multi-physics models to derive process–structure–property relationships for additive manufacturing W Yan, S Lin, OL Kafka, Y Lian, C Yu, Z Liu, J Yan, S Wolff, H Wu, ... Computational Mechanics 61, 521-541, 2018 | 229 | 2018 |
| On the potential of recurrent neural networks for modeling path dependent plasticity MB Gorji, M Mozaffar, JN Heidenreich, J Cao, D Mohr Journal of the Mechanics and Physics of Solids 143, 103972, 2020 | 191 | 2020 |
| Data-driven prediction of the high-dimensional thermal history in directed energy deposition processes via recurrent neural networks M Mozaffar, A Paul, R Al-Bahrani, S Wolff, A Choudhary, A Agrawal, ... Manufacturing letters 18, 35-39, 2018 | 181 | 2018 |
| A real-time iterative machine learning approach for temperature profile prediction in additive manufacturing processes A Paul, M Mozaffar, Z Yang, W Liao, A Choudhary, J Cao, A Agrawal 2019 IEEE International Conference on Data Science and Advanced Analytics …, 2019 | 81 | 2019 |
| Mechanistic artificial intelligence (mechanistic-AI) for modeling, design, and control of advanced manufacturing processes: Current state and perspectives M Mozaffar, S Liao, X Xie, S Saha, C Park, J Cao, WK Liu, Z Gan Journal of Materials Processing Technology 302, 117485, 2022 | 63 | 2022 |
| Geometry-agnostic data-driven thermal modeling of additive manufacturing processes using graph neural networks M Mozaffar, S Liao, H Lin, K Ehmann, J Cao Additive Manufacturing 48, 102449, 2021 | 48 | 2021 |
| Acceleration strategies for explicit finite element analysis of metal powder-based additive manufacturing processes using graphical processing units M Mozaffar, E Ndip-Agbor, S Lin, GJ Wagner, K Ehmann, J Cao Computational Mechanics 64, 879-894, 2019 | 44 | 2019 |
| Sustainable manufacturing with cyber-physical discrete manufacturing networks: Overview and modeling framework DJ Garcia, M Mozaffar, H Ren, JE Correa, K Ehmann, J Cao, F You Journal of Manufacturing Science and Engineering 141 (2), 021013, 2019 | 20 | 2019 |
| Improving the accuracy of double-sided incremental forming simulations by considering kinematic hardening and machine compliance D Leem, N Moser, H Ren, M Mozaffar, KF Ehmann, J Cao Procedia Manufacturing 29, 88-95, 2019 | 15 | 2019 |
| Differentiable simulation for material thermal response design in additive manufacturing processes M Mozaffar, S Liao, J Jeong, T Xue, J Cao Additive Manufacturing 61, 103337, 2023 | 12 | 2023 |
| Toolpath design for additive manufacturing using deep reinforcement learning M Mozaffar, A Ebrahimi, J Cao arXiv preprint arXiv:2009.14365, 2020 | 9 | 2020 |
| Efficient GPU-accelerated thermomechanical solver for residual stress prediction in additive manufacturing S Liao, A Golgoon, M Mozaffar, J Cao Computational Mechanics 71 (5), 879-893, 2023 | 8 | 2023 |
| Additive manufacturing process design with differentiable simulations M Mozaffar, J Cao arXiv preprint arXiv:2107.10919, 2021 | 5 | 2021 |
| Toward Neural Network Models to Model Multi-phase Solids MB Gorji, JN Heidenreich, M Mozaffar, D Mohr Forming the Future: Proceedings of the 13th International Conference on the …, 2021 | 1 | 2021 |
| Artificial intelligence in metal forming J Cao, M Bambach, M Merklein, M Mozaffar, T Xue CIRP Annals, 2024 | | 2024 |
| Physics-Informed Data-Driven Prediction and Design in Advanced Manufacturing Processes M Mozaffar Northwestern University, 2021 | | 2021 |
