基于数字孪生机床模型的加工路径优化研究

Research on Machining Path Optimization Based on Digital Twin Machine Tool Model

制造业个性化加工转型对数控机床加工控制提出了更高的要求,针对固定工况构建的数字孪生模型无法适应生产要求进行加工路径的调整问题,本研究提出了以改进的遗传模拟退火算法(IGASA算法)嵌入到数字孪生机床模型,根据实际加工需求,构建了走刀长度与换刀成本综合最小化的目标函数,并设定了数控加工运动干涉约束、重复走刀约束、基元加工顺序约束、刀具寿命约束四种约束,以IGASA算法进行目标求解,以获取到最优加工路径。同时,通过数字孪生机床模型实现对数控机床的虚拟映射,实时获取加工数据与数控机床运行数据,并根据数据变动,采用IGASA算法进行最优加工路径的自适应更新。验证表明,该方法可以重新进行加工路径的更新,案例中的加工路径长度缩小了141.56mm,表明该方法的有效性。

The personalized processing transformation in the manufacturing industry has put forward higher requirements for CNC machine tool processing control.In response to the problem that digital twin models constructed under fixed working conditions cannot adapt to production requirements for adjusting processing paths,this study proposes to embed an improved genetic simulated annealing algorithm(IGASA algorithm)into the digital twin machine tool model.Based on actual processing needs,an objective function is constructed to comprehensively minimize tool length and tool change cost,And four constraints were set for CNC machining motion interference constraints,repeated tool walking constraints,primitive machining sequence constraints,and tool life constraints.The IGASA algorithm was used to solve the goal and obtain the optimal machining path.At the same time,virtual mapping of CNC machine tools is achieved through the digital twin machine tool model,real-time acquisition of machining data and CNC machine tool operation data,and adaptive updating of the optimal machining path using IGASA algorithm based on data changes.The validation shows that this method can update the machining path again,and the length of the machining path in the case has been reduced by 141.56mm,indicating the effectiveness of this method.