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.

翻译：数控（CNC）制造是一种利用计算机数控（CNC）机器控制各类工业工具与机械设备运动的过程，涵盖从磨床、车床到铣床及CNC路由器等设备。然而，对人工CNC编程的依赖已成为瓶颈，且专家知识需求导致成本高昂。为此，我们提出一种名为CNC-Net的创新方法，该方法利用深度神经网络模拟CNC机器，并在给定原材料时掌握复杂加工操作。CNC-Net构成一个自监督框架，仅需输入三维模型，即可生成CNC机器构建该物体所需的关键操作参数。该方法有望通过提供一种成本效益高的替代方案来变革制造业自动化——在保持三维物体生产超高精度的同时，避免人工CNC编程的高昂成本。实验充分验证了CNC-Net在利用CNC操作构建目标三维物体中的有效性。值得注意的是，该方法在保留精细局部细节方面表现优异，相较于最先进的三维CAD重建方法，其精度提升显著。