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Manufacturing process classification based on heat kernel signature and convolutional neural networks

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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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Acknowledgements

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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  1. School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA, 30332, USA

    Zhichao Wang & David Rosen

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  1. Zhichao Wang
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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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