Given a part design, the task of manufacturing process classification identifies an appropriate manufacturing process to fabricate it. Our previous research proposed a large dataset for manufacturing process classification and achieved accurate classification results based on a combination of a convolutional neural network (CNN) and the heat kernel signature for triangle meshes. In this paper, we constructed a classification method based on rotation invariant shape descriptors and a neural network, and it achieved better accuracy than all previous methods. This method uses a point cloud part representation, in contrast to the triangle mesh representation used in our previous work. The first step extracted rotation invariant features consisting of a set of distances between points in the point cloud. Then, the extracted shape descriptors were fed into a CNN for the classification of manufacturing processes. In addition, we provide two visualization methods for interpreting the intermediate layers of the neural network. Last, the performance of the method was tested on some ambiguous examples and their performances were consistent with expectations. In this paper, we have considered only shape information, while non-shape information like materials and tolerances were ignored. Additionally, only parts that require one manufacturing process were considered in this research. Our work demonstrates that part shape attributes alone are adequate for discriminating between different manufacturing processes considered.
This content will become publicly available on April 16, 2025
Manufacturing process selection based on similarity search: incorporating non-shape information in shape descriptor comparison
Given a part design, the task of manufacturing process selection chooses an appropriate manufacturing process to fabricate it. Prior research has traditionally determined manufacturing processes through direct classification. However, an alternative approach to select a manufacturing process for a new design involves identifying previously produced parts with comparable shapes and materials and learning from them. Finding similar designs from a large dataset of previously manufactured parts is a challenging problem. To solve this problem, researchers have proposed different spatial and spectral shape descriptors to extract shape features including the D2 distribution, spherical harmonics (SH), and the Fast Fourier Transform (FFT), as well as the application of different machine learning methods on various representations of 3D part models like multi-view images, voxel, triangle mesh, and point cloud. However, there has not been a comprehensive analysis of these different shape descriptors, especially for part similarity search aimed at manufacturing process selection. To remedy this gap, this paper presents an in-depth comparative study of these shape descriptors for part similarity search. While we acknowledge the importance of factors like part size, tolerance, and cost in manufacturing process selection, this paper focuses on part shape and material properties only. Our findings show that SH performs the best among non-machine learning methods for manufacturing process selection, yielding 97.96% testing accuracy using the proposed quantitative evaluation metric. For machine learning methods, deep learning on multi-view image representations is best, yielding 99.85% testing accuracy when rotational invariance is not a primary concern. Deep learning on point cloud representations excels, yielding 99.44% testing accuracy when considering rotational invariance.
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- PAR ID:
- 10502723
- Publisher / Repository:
- Springer
- Date Published:
- Journal Name:
- Journal of Intelligent Manufacturing
- ISSN:
- 0956-5515
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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