Deep learning-based semantic segmentation of machinable volumes for cyber manufacturing service

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.

Automated summary

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.