CNC-Net: Self-Supervised Learning for CNC Machining Operations
Mohsen Yavartanoo Sangmin Hong Reyhaneh Neshatavar Kyoung Mu Lee
CNC manufacturing is a process that employs computer numerical control (CNC)
machines to govern the movements of various industrial tools and machinery,
encompassing equipment ranging from grinders and lathes to mills and CNC
routers. However, the reliance on manual 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 networks (DNNs) to simulate CNC machines
and grasp intricate operations when supplied with raw materials. CNC-Net
constitutes a self-supervised framework that exclusively takes an input 3D
model and subsequently generates the essential operation parameters required by
the CNC machine to construct the object. Our method has the potential to
transformative automation in manufacturing by offering a cost-effective
alternative to the high costs of manual CNC programming while maintaining
exceptional precision in 3D object production. Our experiments underscore the
effectiveness of our CNC-Net in constructing the desired 3D objects through the
utilization of CNC operations. Notably, it excels in preserving finer local
details, exhibiting a marked enhancement in precision compared to the
state-of-the-art 3D CAD reconstruction approaches.