期刊
JOURNAL OF MANUFACTURING SYSTEMS
卷 72, 期 -, 页码 16-25出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.jmsy.2023.11.005
关键词
Semantic segmentation; Unsupervised learning; Convolutional neural network; Process planning; Feature recognition
This paper extends prior work by developing a semantic segmentation approach for machinable volume decomposition using pre-trained generative process capability models, providing manufacturability feedback and labels of candidate machining operations for query 3D parts.
Enabling the vision of on-demand cyber manufacturing-as-a-service requires a new set of cloud-based computational tools for design manufacturability feedback and process selection to connect designers with manufacturers. In our prior work, we demonstrated a generative modeling approach in voxel space to model the shape transformation capabilities of machining operations using unsupervised deep learning. Combining this with a deep metric learning model enabled quantitative assessment of the manufacturability of a query part. In this paper, we extend our prior work by developing a semantic segmentation approach for machinable volume decomposition using pre-trained generative process capability models, which output per-voxel manufacturability feedback and labels of candidate machining operations for a query 3D part. Using three types of complex parts as case studies, we show that the proposed method accurately identifies machinable and non-machinable volumes with an average intersection-over-union (IoU) of 0.968 for axisymmetric machining operations, and a class average F1 score of 0.834 for volume segmentation by machining operation.
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