CNC-Net: Self-Supervised Learning for CNC Machining Operations

Mohsen Yavartanoo, Sangmin Hong, Reyhaneh Neshatavar, Kyoung Mu Lee

Abstract: CNC manufacturing is a process that employs computer numerical control (CNC) machines to govern the move-ments of various industrial tools and machinery, encom-passing equipment ranging from grinders and lathes to mills and CNC routers. However, the reliance on man-ual CNC programming has become a bottleneck, and the requirement for expert knowledge can result in significant costs. Therefore, we introduce a pioneering approach named CNC-Net, representing the use of deep neural net-works (DNNs) to simulate CNC machines and grasp intri-cate operations when supplied with raw materials. CNC-Net constitutes a self-supervised framework that exclu-sively takes an input 3D model and subsequently gener-ates the essential operation parameters required by the CNC machine to construct the object. Our method has the potential to transformative automation in manufac-turing by offering a cost-effective alternative to the high costs of manual CNC programming while maintaining ex-ceptional precision in 3D object production. Our ex-periments underscore the effectiveness of our CNC-Net in constructing the desired 3D objects through the uti-lization of CNC operations. Notably, it excels in pre-serving finer local details, exhibiting a marked enhance-ment in precision compared to the state-of-the-art 3D CAD reconstruction approaches. The codes are available at https://github.com/myavartanoo/CNC-Net_PyTorch.