Robotic machining has been increasingly applied in intelligent manufacturing production lines. Compared with the traditional machine tools, commissioning for robotic machining system (RMS) is particularly important due to the low accuracy of industrial robots (IRs). Traditional site commissioning has large workload and is difficult to handle the multi-source errors. Since digital twin (DT) provides strategies for staying synchronized with the physical entities in whole lifecycle, a DT-driven virtual commissioning (VC) system for RMS is developed in this study to improve machining accuracy and reduce the difficulty of commissioning. Firstly, the framework of DT-driven VC system is designed including several function modules such as interaction, data pre-processing, DT model of RMS (RMSDT), and optimization service. Since RMSDT is the kernel of precise VC, a machine learning-enhanced RMSDT oriented to actual machining path prediction is then constructed based on a proposed joint error equivalent strategy, which can fully consider the coupled multi-source errors of machining robot. After that, a practical consistency retention method for RMSDT is proposed based on a stepwise updating strategy, where the model performance can be maintained with low updating costs. Finally, a visual VC system is developed for the experimental 6-degree of freedom robotic milling platform to verify the feasibility and effectiveness of the VC framework. Multiple experiments are also performed to test the performance of RMSDT and contour error compensation. This study has useful reference for the enterprises engaged in RMS and has positive significance for promoting the robotic machining.

中文翻译：

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通过机器学习增强的机器人加工数字孪生驱动的虚拟调试


机器人加工已越来越多地应用于智能制造生产线。与传统机床相比，由于工业机器人 （IR） 的精度较低，机器人加工系统 （RMS） 的调试尤为重要。传统站点调测工作量大，多源错误难以处理。由于数字孪生 （DT） 提供了在整个生命周期中与物理实体保持同步的策略，因此本研究开发了一种 DT 驱动的 RMS 虚拟调试 （VC） 系统，以提高加工精度并降低调试难度。首先，设计了数字孪生驱动的VC系统框架，包括交互、数据预处理、RMS数字孪生模型（RMSDT）和优化服务等多个功能模块;由于 RMSDT 是精密 VC 的内核，然后基于所提出的关节误差等效策略构建面向实际加工路径预测的机器学习增强 RMSDT，该策略可以充分考虑加工机器人的耦合多源误差。在此之后，提出了一种基于逐步更新策略的实用的RMSDT一致性保持方法，该方法可以在保持模型性能的同时保持更新成本较低。最后，为实验性 6 自由度机器人铣削平台开发了可视化 VC 系统，以验证 VC 框架的可行性和有效性。还进行了多次实验来测试 RMSDT 和轮廓误差补偿的性能。本研究对从事 RMS 的企业具有有益的参考价值，对推动机器人加工具有积极意义。