Part machining feature recognition based on a deep learning method

• Cao, W., Robinson, T., Hua, Y., Boussuge, F., Colligan, A. R., & Pan, W. (2020). Graph representation of 3D CAD models for machining feature recognition with deep learning. In Proceedings of the ASME 2020 international design engineering technical conferences and computers and information in engineering conference, 46th design automation conference (DAC) (Vol. 11A). https://doi.org/10.1115/DETC2020-22355.

• Ghadai, S., Balu, A., Sarkar, S., & Krishnamurthy, A. (2018). Learning localized features in 3D cad models for manufacturability analysis of drilled holes. Computer Aided Geometric Design, 62(MAY), 263–275.

  Article  Google Scholar 

• Gong, Z., Zhong, P., Yu, Y., Hu, W., & Li, S. (2019). A CNN with multiscale convolution and diversified metric for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing, 57(6), 3599–3618.

  Article  Google Scholar 

• Hashemi, A., Dowlatshahi, M. B., & Nezamabadi-pour, H. (2020). Mgfs: A multi-label graph-based feature selection algorithm via pagerank centrality. Expert Systems with Applications, 142, 46–53.

  Article  Google Scholar 

• Joshi, S., & Chang, T. C. (1988). Graph-based heuristics for recognition of machined features from a 3D solid model. Computer-Aided Design, 20(2), 58–66.

  Article  Google Scholar 

• Krot, K., & Czajka, J. (2018). Processing of design and technological data due to requirements of computer aided process planning systems. In International conference on intelligent systems in production engineering and maintenance (pp. 267–274).

• Ma, Y., Zhang, Y., & Luo, X. (2019). Automatic recognition of machining features based on point cloud data using convolution neural networks. In Proceedings of the 2019 international conference on artificial intelligence and computer science (pp. 229–235).

• Malhan, R. K., Kabir, A. M., Shah, B., & Gupta, S. K. (2019). Identifying feasible workpiece placement with respect to redundant manipulator for complex manufacturing tasks. In 2019 international conference on robotics and automation (ICRA) (pp. 5585–5591).

• Malyshev, A., Slyadnev, S., & Turlapov, V. (2017). Graph-based feature recognition and suppression on the solid models. In GraphiCon (Vol. 17, pp. 319–322).

• Maturana, D., & Scherer, S. (2015).Voxnet: A 3D convolutional neural network for real-time object recognition. In IEEE/RSJ international conference on intelligent robots and systems (pp. 922–928).

• Ning, F., Shi, Y., Cai, M., Xu, W., & Zhang, X. (2020). Manufacturing cost estimation based on the machining process and deep-learning method. Journal of Manufacturing Systems, 56, 11–22.

  Article  Google Scholar 

• Rai, S., & Vairaktarakis, G. (2019). NP-complete problems and proof methodology. Encyclopedia of Optimization. https://doi.org/10.1007/978-0-387-74759-0_462

  Article  Google Scholar 

• Sebastian, R. (2016). An overview of gradient descent optimization algorithms. arXiv:1609.04747

• Shi, Y., Zhang, Y., Baek, S., De Backer, W., & Harik, R. (2018). Manufacturability analysis for additive manufacturing using a novel feature recognition technique. Computer-Aided Design and Applications, 15(6), 941–952.

  Article  Google Scholar 

• Shi, P., Qi, Q., Qin, Y., Scott, P. J., & Jiang, X. (2020). A novel learning-based feature recognition method using multiple sectional view representation. Journal of Intelligent Manufacturing, 31, 96.

  Article  Google Scholar 

• Sunil, V. B., Agarwal, R., & Pande, S. S. (2010). An approach to recognize interacting features from b-rep cad models of prismatic machined parts using a hybrid (graph and rule based) technique. Computers in Industry, 61(7), 686–701.

  Article  Google Scholar 

• Venuvinod, P. K., & Wong, S. Y. (1994). A graph-based expert system approach to geometric feature recognition. Journal of Intelligent Manufacturing, 6, 401–413.

  Google Scholar 

• Weise, J., Benkhardt, S., & Mostaghim, S. (2018). A survey on graph-based systems in manufacturing processes. In 2018 IEEE symposium series on computational intelligence (SSCI) (pp. 112–119). IEEE.

• Wu, N. Z., Song, S., Khosla, A., Yu, N. F., Zhang, N. L., & Tang, N. X., et al. (2015). 3D Shapenets: A deep representation for volumetric shape modeling. In IEEE conference on computer vision and pattern recognition. arXiv preprint arXiv:1406.5670.

• Xu, X., Caulfield, S., Amaro, J., Falcao, G., & Moloney, D. (2019). 1.2 Watt classification of 3D voxel based point-clouds using a cnn on a neural compute stick. Neurocomputing. https://doi.org/10.1016/j.neucom.2018.10.114

  Article  Google Scholar 

• Yuen, F., & Venuvinod, P. K. (1999). Geometric feature recognition: Coping with the complexity and infinite variety of features. International Journal of Computer Integrated Manufacturing, 12(5), 439–452.

  Article  Google Scholar 

• Zhang, L., Yang, F., Zhang, Y. D., & Zhu, Y. J. (2016). Road crack detection using deep convolutional neural network. In 2016 IEEE international conference on image processing (ICIP) (pp. 3708–3712).

• Zhang, Z., Jaiswal, P., & Rai, R. (2018). Featurenet: Machining feature recognition based on 3d convolution neural network. Computer-Aided Design, 101, 12–22.

  Article  Google Scholar 

• Zhao, X., Zhang, H., Zhu, G., You, F., Kuang, S., & Sun, L. (2019). A multi-branch 3D convolutional neural network for eeg-based motor imagery classification. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 27(10), 2164–2177.

  Article  Google Scholar 

• Zhou, G., Yang, X., Zhang, C., Li, Z., & Xiao, Z. (2019). Deep learning enabled cutting tool selection for special-shaped machining features of complex products. Advances in Engineering Software, 133, 1–11. https://doi.org/10.1016/j.advengsoft.2019.04.007

  Article  Google Scholar 

Download references