GNN-based deep reinforcement learning for MBD product model recommendation

References

• Almasan, P., J. Suã¡rez-Varela, A. Badia-Sampera, K. Rusek, P. Barlet-Ros, and A. Cabellos-Aparicio. 2019. “Deep Reinforcement Learning Meets Graph Neural Networks: Exploring a Routing Optimization Use Case.“ Computer communications 196 (12): 184–94.
   Google Scholar
• Angrish, A., A. Bharadwaj, and B. Starly. 2021. “Mvcnn++: Computer-Aided Design Model Shape Classification and Retrieval Using Multi-View Convolutional Neural Networks.” American Society of Mechanical Engineers Digital Collection 21 (1): 2021. https://doi.org/10.1115/1.4047486.
   Google Scholar
• Bonino, B., F. Giannini, M. Monti, and R. Raffaeli. 2023. “Shape and Context-Based Recognition of Standard Mechanical Parts in Cad Models.” Computer-Aided Design 155:103438. https://doi.org/10.1016/j.cad.2022.103438.
   Web of Science ®Google Scholar
• Bo, H., S. Zhang, H. Rui, X. Li, and Y. Zhang. 2019. “An Effective Retrieval Approach of 3d Cad Models for Macro Process Reuse.” International Journal of Advanced Manufacturing Technology 102 (6): 1067–1089. https://doi.org/10.1007/s00170-018-2968-8.
   Google Scholar
• Chen, J., H. T. Ilies, and C. Ding. 2022. “Graph-Based Shape Analysis for Heterogeneous Geometric Datasets: Similarity, Retrieval and Substructure Matching.” Computer-Aided Design 143:143. https://doi.org/10.1016/j.cad.2021.103125.
   Web of Science ®Google Scholar
• Christiand Christiand, G. K., and A. Baskoro. 2022. “The Digital Twin Application for Micro-Tool Wear Monitoring with Open-Source CAD System.“ IFIP Advances in Information and Communication Technology(IFIP), Gyeongju, Korea, 9: 11–18. https://doi.org/10.1007/978-3-031-16411-8_2.
   Google Scholar
• Colligan, A. R., T. T. Robinson, D. C. Nolan, Y. Hua, and W. Cao. 2022a. “Hierarchical Cadnet: Learning from B-Reps for Machining Feature Recognition.” Computer-Aided Design 147:103226. https://doi.org/10.1016/j.cad.2022.103226.
   Web of Science ®Google Scholar
• Colligan, A. R., T. T. Robinson, D. C. Nolan, Y. Hua, and W. Cao. 2022b. “Hierarchical Cadnet: Learning from B-Reps for Machining Feature Recognition.” Computer-Aided Design 147:147. https://doi.org/10.1016/j.cad.2022.103226.
   Web of Science ®Google Scholar
• Fradi, A., B. Louhichi, M. Ali Mahjoub, and B. Eynard. 2021. “Article a New Approach for Reusable 3d Cad Objects Detection, by Similarity Calculation Based on Models Bayesian Network (Mbn).” International Journal of Computer Integrated Manufacturing 34 (12): 1285–1304. https://doi.org/10.1080/0951192X.2021.1972457.
   Web of Science ®Google Scholar
• Joshi, S., and T. C. Chang. 1988. “Graph-Based Heuristics for Recognition of Machined Features from a 3d Solid Model.” Computer-Aided Design 20 (2): 58–66. https://doi.org/10.1016/0010-4485(88)90050-4.
   Web of Science ®Google Scholar
• Kim, H., C. Yeo, M. Cha, and D. Mun. 2021. “A Method of Generating Depth Images for View-Based Shape Retrieval of 3d Cad Models from Partial Point Clouds.” Multimedia Tools and Applications 80 (1): 1–22. https://doi.org/10.1007/s11042-020-10283-z.
   Google Scholar
• Koch, S. , ”ABC: A Big CAD Model Dataset for Geometric Deep Learning,” 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA, 2019, pp. 9593–9603. https://doi.org/10.1109/CVPR.2019.00983.
   Google Scholar
• Kumar Verma, A., and S. Rajotia. 2010. “A Review of Machining Feature Recognition Methodologies.” International Journal of Computer Integrated Manufacturing 23 (4): 353–368. https://doi.org/10.1080/09511921003642121.
   Web of Science ®Google Scholar
• Kuo, W., A. Angelova, T.-Y. Lin, and A. Dai. ”Patch2cad: Patchwise Embedding Learning for In-The-Wild Shape Retrieval from a Single Image,” 2021 IEEE/CVF International Conference on Computer Vision (ICCV), Montreal, QC, Canada, 2021, pp. 12569–12579. https://doi.org/10.1109/ICCV48922.2021.01236.
   Google Scholar
• Lipman, R., and J. Lubell. 2015. “Conformance Checking of Pmi Representation in Cad Model Step Data Exchange Files.” Computer-Aided Design 66:14–23. https://doi.org/10.1016/j.cad.2015.04.002.
   Web of Science ®Google Scholar
• Li, Y., D. Tarlow, M. Brockschmidt, R. Zemel. 2016. ”Gated Graph Sequence Neural Networks.” 4th International Conference on Learning Representations(ICLR 2016), San Juan, Puerto rico. https://doi.org/10.48550/arXiv.1511.05493.
   Google Scholar
• Manda, B., P. Bhaskare, and R. Muthuganapathy. 2021. “A Convolutional Neural Network Approach to the Classification of Engineering Models.” IEEE Access 9:22711–22723. https://doi.org/10.1109/ACCESS.2021.3055826.
   Google Scholar
• Ma, Z., J. Zhou, J. Ma, and T. Li. 2022. “A Novel 3d Shape Recognition Method Based on Double-Channel Attention Residual Network.” Multimedia Tools & Applications 81 (22): 32519–32548. https://doi.org/10.1007/s11042-022-12041-9.
   Web of Science ®Google Scholar
• Mnih, V., K. Kavukcuoglu, D. Silver, A. Graves, I. Antonoglou, D. Wierstra, and M. Riedmiller. 2013. “Quantitative Reactive Modeling and Verification.” Computer Science 28 (4): 331–344. https://doi.org/10.1007/s00450-013-0251-7.
   Google Scholar
• Muraleedharan, L. P., S. S. Kannan, and R. Muthuganapathy. 2019. “Autoencoder-Based Part Clustering for Part-In-Whole Retrieval of Cad Models.” Computers & Graphics 81 (JUN.): 41–51. https://doi.org/10.1016/j.cag.2019.03.016.
   Google Scholar
• Peng, C., X. Wang, J. Pei, and W. Zhu. 2017. “A Survey on Network Embedding.” IEEE Transactions on Knowledge and Data Engineering 31 (5): 833–852. https://doi.org/10.1109/TKDE.2018.2849727.
   Web of Science ®Google Scholar
• Qiao, H., W. Qingyun, Y. Songlin, J. Du, and Y. Xiang. 2019. “A 3d Assembly Model Retrieval Method Based on Assembly Information.” Assembly Automation 39 (4): 556–565. https://doi.org/10.1108/AA-03-2018-047.
   Web of Science ®Google Scholar
• Qin, F., S. Qiu, S. Gao, and J. Bai. 2022. “3d Cad Model Retrieval Based on Sketch and Unsupervised Variational Autoencoder.” Advanced Engineering Informatics 51:101427–. https://doi.org/10.1016/j.aei.2021.101427.
   Web of Science ®Google Scholar
• Sommer, M., J. Stjepandic, S. Stobrawa, and M. V. Soden. 2019. “Automatic Generation of Digital Twin Based on Scanning and Object Recognition.“ Advances in Transdisciplinary Engineering 10 (7): 645–654. https://doi.org/10.3233/ATDE190174.
   Google Scholar
• Tan, F., P. Yan, and X. Guan. 2017. “Deep Reinforcement Learning: From Q-Learning to Deep Q-Learning.” International Conference on Neural Information Processing (ICONIP 2017), Guangzhou, China. https://doi.org/10.1007/978-3-319-70093-9_50.
   Google Scholar
• Van Hasselt, H., A. Guez, and D. Silver. 2015. “Deep Reinforcement Learning with Double Q-Learning.” Computer Ence 30 (1). https://doi.org/10.1609/aaai.v30i1.10295.
   Google Scholar
• Velikovi, P., G. Cucurull, A. Casanova, A. Romero, P. LiÃ2, and Y. Bengio. 2017. “Graph Attention Networks.”
   Google Scholar
• Volodymyr, M., K. Koray, S. David, A. A. Rusu, and V. Joel. 2015. “Human-Level Control Through Deep Reinforcement Learning.” Nature 518 (7540). https://doi.org/10.1038/nature14236.
   Web of Science ®Google Scholar
• Xueyao, G. A. O., L. I. Huinan, Z. H. A. N. G. Chunxiang, and Y. U. Xiaoyang. 2017. “Similarity Calculation of 3d Cad Model Based on Ant Colony Searching.” Journal of Southwest Jiaotong University 30 (2): 416–423.
   Google Scholar
• Zhang, C., and G. Zhou. 2018. “A View-Based 3d Cad Model Reuse Framework Enabling Product Lifecycle Reuse.” Advances in Engineering Software 127 (JAN.): 82–89. https://doi.org/10.1016/j.advengsoft.2018.09.001.
   Google Scholar
• Zhi Xiong, X., L. Cao, X. Liang Chen, C. Xi Li, Y. Liang Zhang, and J. Lai. 2018. “Deep Reinforcement Learning with Sarsa and Q-Learning: A Hybrid Approach.” IEICE Transactions on Information and Systems 101 (9): 2315–2322. https://doi.org/10.1587/transinf.2017EDP7278.
   Google Scholar