Abstract
The problem of manufacturing process classification is to identify manufacturing processes that are suitable for a given part design. Previous research on automating manufacturing process classification was limited by small datasets and low accuracy. To solve the first problem, a larger dataset composed of 12,463 examples with four manufacturing processes was constructed. To improve classification accuracy, a deep learning-based method for CAD models was proposed. To begin with, the heat kernel signature (HKS) is computed from a triangle mesh representation of the part. To deal with different numbers of vertices within different models, i.e., different sizes for HKS within each model, two alternative methods are proposed. The first one applies binning to sort all vertices into constant bins, which is further fed into a conventional CNN for classification. The other method searches the most representative local HKS by farthest point sampling, and the representative local HKS are then sent into a pointwise CNN for classification. The scope of this paper focuses on the use of only part shapes for manufacturing process classification, while acknowledging that other information such as size scales, tolerances, and materials, play important roles in manufacturing process selection. Results demonstrate excellent process classification performance with only part shape information.
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Adam, A. (2021). Mesh voxelisation, MATLAB Central File Exchange. Retrieved July 10, 2021, from https://www.mathworks.com/matlabcentral/fileexchange/27390-mesh-voxelisation
Aflalo, Y., Kimmel, R., & Raviv, D. (2013). Scale Invariant Geometry for Nonrigid Shapes. SIAM Journal on Imaging Sciences, 6(3), 1579–1597. https://doi.org/10.1137/120888107
Belongie, S. J., Malik, J., & Puzicha, J. (2000). Shape context: A new descriptor for shape matching and object recognition. In Neural information processing systems (NIPS), Denver, CO, USA, November 2000.
Biasotti, S., Cerri, A., & Bronstein, A. (2016). Recent trends, applications, and perspectives in 3D shape similarity assessment. Computer Graphics Forum, 35(6), 87–119. https://doi.org/10.1111/cgf.12734
Bronstein, M. M., & Kokkinos, I. (2010). Scale-invariant heat kernel signatures for non-rigid shape recognition. In 2010 IEEE Computer Society conference on computer vision and pattern recognition.
Fang, Q., & Boas, D. A. (2009). Tetrahedral mesh generation from volumetric binary and grayscale images. In 2009 IEEE international symposium on biomedical imaging: From nano to macro.
Fang, Y., Xie, J., & Dai, G. (2015). 3D deep shape descriptor. In Proceedings of the IEEE conference on computer vision and pattern recognition, Boston, MA, USA, 7–12 June 2015.
Feng, S. C., & Song, E. Y. (2003). A manufacturing process information model for design and process planning integration. Journal of Manufacturing Systems, 22(1), 1–15. https://doi.org/10.1016/S0278-6125(03)90001-X
Gal, R., Shamir, A., & Cohen-Or, D. (2007). Pose-oblivious shape signature. IEEE Transactions on Visualization and Computer Graphics, 13(2), 261–271. https://doi.org/10.1109/TVCG.2007.45
Gupta, S. K., Chen, Y., & Feng, S. (2003). A system for generating process and material selection advice during embodiment design of mechanical components. Journal of Manufacturing Systems, 22(1), 28–45. https://doi.org/10.1016/S0278-6125(03)90003-3
Hilaga, M., Shinagawa, Y., & Kohmura, T. (2001). Topology matching for fully automatic similarity estimation of 3D shapes. In SIGGRAPH '01: Proceedings of the 28th annual conference on Computer graphics and interactive techniques, San Francisco, CA, USA. https://doi.org/10.1145/383259.383282
Hinton, G. E., & Roweis, S. (2002). Stochastic neighbor embedding. In Advances in neural information processing systems, 15 (NIPS 2002).
Hoefer, M. J., & Frank, M. C. (2018). Automated manufacturing process selection during conceptual design. Journal of Mechanical Design, 140(3), 031701. https://doi.org/10.1115/1.4038686
Ip, C. Y., & Regli, W. C. (2006). A 3D object classifier for discriminating manufacturing processes. Computers & Graphics, 30(6), 903–916. https://doi.org/10.1016/j.cag.2006.08.013
Ip, C. Y., Regli, W. C., & Sieger, L. (2003). Automated learning of model classifications. In Proceedings of the eighth ACM symposium on Solid modeling and applications, Seattle, WA, USA. https://doi.org/10.1145/781606.781659
Johnson, A. (1997). Spin-images: A representation for 3-D surface matching (Publication Number CMU-RI-TR-97–47). Carnegie Mellon University.
JungHyun, H., Pratt, M., & Regli, W. C. (2000). Manufacturing feature recognition from solid models: A status report. IEEE Transactions on Robotics and Automation, 16(6), 782–796. https://doi.org/10.1109/70.897789
LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444. https://doi.org/10.1038/nature14539
LeCun, Y., Bottou, L., & Bengio, Y. (1998). Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86, 2278–2324. https://doi.org/10.1109/5.726791
Li, C., & Ben Hamza, A. (2013). A multiresolution descriptor for deformable 3D shape retrieval. The Visual Computer, 29(6), 513–524. https://doi.org/10.1007/s00371-013-0815-3
Li, X., & Guskov, I. (2005). Multi-scale features for approximate alignment of point-based surfaces. In Proceedings of the third Eurographics symposium on geometry processing, Vienna, Austria.
Loriot, S., Rouxel-Labbé, M., & Tournois, J. (2021). CGAL user and reference manual 5.0.3 edition. Polygon mesh processing.
Manay, S., Hong, B.-W., & Yezzi, A. J. (2004). Integral invariant signatures. In European conference on computer Vision (ECCV), Berlin, Heidelberg.
Ning, F., Shi, Y., & Cai, M. (2021). Part machining feature recognition based on a deep learning method. Journal of Intelligent Manufacturing. https://doi.org/10.1007/s10845-021-01827-7
Osada, R., Funkhouser, T., & Chazelle, B. (2002). Shape distributions. ACM Transactions on Graphics, 21(4), 807–832. https://doi.org/10.1145/571647.571648
Qi, C. R., Su, H., & Mo, K. (2017). PointNet: Deep learning on point sets for 3D classification and segmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition, Hawaii, HI, USA, 21–26 July 2017.
Raviv, D., Bronstein, M. M., & Bronstein, A. M. (2010). Volumetric heat kernel signatures. In Proceedings of the ACM workshop on 3D object retrieval, Firenze, Italy. https://doi.org/10.1145/1877808.1877817
Rustamov, R. M. (2007). Laplace-Beltrami eigenfunctions for deformation invariant shape representation. In Proceedings of the fifth Eurographics symposium on geometry processing, Barcelona, Spain.
Sharp, N., Soliman, Y., & Crane, K. (2019). Navigating intrinsic triangulations. Association for Computing Machinery. https://doi.org/10.1145/3306346.3322979
Shi, Y., Zhang, Y., & Baek, S. (2018). Manufacturability analysis for additive manufacturing using a novel feature recognition technique. Computer-Aided Design and Applications, 15(6), 941–952.
Simonyan, K., & Zisserman, A. (2015). Very deep convolutional networks for large-scale image recognition. In International conference on learning representations (ICLR), San Diego, CA, USA.
Sun, J., Ovsjanikov, M., & Guibas, L. (2009). A concise and provably informative multi-scale signature based on heat diffusion. Computer Graphics Forum, 28(5), 1383–1392. https://doi.org/10.1111/j.1467-8659.2009.01515.x
Swift, K. G., & Booker, J. D. (2013). Manufacturing process selection handbook: From design to manufacture. Butterworth-Heinemann.
Tran, A. P., Yan, S., & Fang, Q. (2020). Improving model-based functional near-infrared spectroscopy analysis using mesh-based anatomical and light-transport models. Neurophotonics, 7(1), 1–18. https://doi.org/10.1117/1.NPh.7.1.015008
Verma, A. K., & Rajotia, S. (2010). A review of machining feature recognition methodologies. International Journal of Computer Integrated Manufacturing, 23(4), 353–368. https://doi.org/10.1080/09511921003642121
Wang, Y., Sun, Y., & Liu, Z. (2019). Dynamic graph CNN for learning on point clouds. ACM Transactions on Graphics, 38(5), 1–12. https://doi.org/10.1145/3326362
Wu, D., Rosen, D. W., & Wang, L. (2015). Cloud-based design and manufacturing: A new paradigm in digital manufacturing and design innovation. Computer-Aided Design, 59, 1–14. https://doi.org/10.1016/j.cad.2014.07.006
Wu, Z., Wang, X., & Lin, D. (2019). SAGNet: Structure-aware generative network for 3D-shape modeling. ACM Transactions on Graphics, 38(4), 1–14. https://doi.org/10.1145/3306346.3322956
Xie, J., Fang, Y., Zhu, F. (2015). DeepShape: Deep learned shape descriptor for 3D shape matching and retrieval. In Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), Boston, MA, USA.
Zaman, U. K. U., Rivette, M., & Siadat, A. (2018). Integrated product-process design: Material and manufacturing process selection for additive manufacturing using multi-criteria decision making. Robotics and Computer-Integrated Manufacturing, 51, 169–180. https://doi.org/10.1016/j.rcim.2017.12.005
Zhang, Y., Zhang, Y., & He, K. (2021). Intelligent feature recognition for STEP-NC-compliant manufacturing based on artificial bee colony algorithm and back propagation neural network. Journal of Manufacturing Systems. https://doi.org/10.1016/j.jmsy.2021.01.018
Zhang, Z., Jaiswal, P., & Rai, R. (2018). FeatureNet: Machining feature recognition based on 3D convolution neural network. Computer-Aided Design, 101, 12–22. https://doi.org/10.1016/j.cad.2018.03.006
Zhao, C., Dinar, M., & Melkote, S. N. (2020). Automated classification of manufacturing process capability utilizing part shape, material, and quality attributes. Journal of Computing and Information Science in Engineering, 20(2), 021011. https://doi.org/10.1115/1.4045410
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The authors gratefully acknowledge support from the National Science Foundation, Grant CMMI-2113672. Any opinions, findings, and conclusions or recommendations expressed in this publication are those of the authors and do not necessarily reflect the views of the National Science Foundation.
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Wang, Z., Rosen, D. Manufacturing process classification based on heat kernel signature and convolutional neural networks. J Intell Manuf 34, 3389–3411 (2023). https://doi.org/10.1007/s10845-022-02009-9
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DOI: https://doi.org/10.1007/s10845-022-02009-9