一种基于数字孪生的重型数控机床碰撞检测方法

目前数控机床碰撞检测通常利用系统的仿真功能对加工G代码进行检测，仅考虑数控机床加工过程中加工轨迹上刀具与理想状况下的工作台、夹具之间是否有干涉现象，难以满足开放式重型数控机床动态的加工环境、装夹方式以及刀具尺寸变化等实际情况。将数字孪生引入重型数控机床碰撞检测，构建了感知演化预测反馈的碰撞检测框架，即通过构建数控机床的数字孪生体，动态感知工件、夹具、刀具等加工要素，并感知数据驱动孪生模型演化，从而预测数控机床加工过程中潜在的干涉现象，提高了机床加工效率，避免加工过程的潜在危害。将所提方法应用于重型数控龙门镗铣机床ZK5520的碰撞检测，证明了其有效性与可行性。

A Collision Detection Method of Heavy-duty CNC Machine Tools Based on Digital Twin

At present, the collision detection of CNCMTs usually used the basic simulation function of the CNC system to detect machining G codes, which only considered whether there were collisions between the tool and the ideal workbench, or the tool and fixtures on the tool path during the processes. It was difficult to meet the actual dynamic processing environments, clamping mode and fixture change of open heavy-duty machine tools. So the digital twin was introduced into the collision detection of open heavy-duty CNCMTs, and a perception-evolutionary prediction-feedback collision detection framework was constructed. By constructing digital twin of CNCMTs and dynamically sensing machining elements such as workpieces ，fixtures and cutting tools, the evolution of the digital twin should be driven by perception data, and the potential interference phenomenon would be predicted in the machining processes. Thus, the efficiency of the CNCMTs was improved, and the potential harm was avoided. The method was applied to the collision detection of heavy-duty CNC gantry boring and milling machine ZK5520, and the effectiveness and feasibility of the proposed method were proved.