School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, China.
Shanghai Key Laboratory of Intelligent Manufacturing and Robotics, Shanghai University, Shanghai 200444, China.
Sensors (Basel). 2022 Sep 26;22(19):7282. doi: 10.3390/s22197282.
During the assembly process of the rear axle, the assembly quality and assembly efficiency decrease due to the accumulation errors of rear axle assembly torque. To deal with the problem, we proposed a rear axle assembly torque online control method based on digital twin. First, the gray wolf-based optimization variational modal decomposition and long short-term memory network (GWO-VMD-LSTM) algorithm was raised to predict the assembly torque of the rear axle, which solves the shortcomings of unpredictable non-stationarity and nonlinear assembly torque, and the prediction accuracy reaches 99.49% according to the experimental results. Next, the evaluation indexes of support vector machine (SVM), recurrent neural network (RNN), LSTM, and SVM, RNN, and LSTM based on gray wolf optimized variational modal decomposition (GWO-VMD) were compared, and the performance of the GWO-VMD-LSTM is the best. For the purpose of solving the insufficient information interaction capability problem of the assembly line, we developed a digital twin system for the rear axle assembly line to realize the visualization and monitoring of the assembly process. Finally, the assembly torque prediction model is coupled with the digital twin system to realize real-time prediction and online control of assembly torque, and the experimental testing manifests that the response time of the system is about 1 s. Consequently, the digital twin-based rear axle assembly torque prediction and online control method can significantly improve the assembly quality and assembly efficiency, which is of great significance to promote the construction of intelligent production line.
在后桥装配过程中，由于后桥装配扭矩的累积误差，导致装配质量和装配效率降低。针对这一问题，我们提出了一种基于数字孪生的后桥装配扭矩在线控制方法。首先，提出了基于灰狼优化变模态分解和长短期记忆网络（GWO-VMD-LSTM）算法的后桥装配扭矩预测方法，解决了装配扭矩非平稳性和非线性不可预测的问题，实验结果表明预测精度达到 99.49%。其次，对支持向量机（SVM）、递归神经网络（RNN）、长短期记忆网络（LSTM）和基于灰狼优化变模态分解（GWO-VMD）的 SVM、RNN 和 LSTM 的评价指标进行了比较，结果表明 GWO-VMD-LSTM 的性能最优。为了解决装配线信息交互能力不足的问题，开发了后桥装配线的数字孪生系统，实现了装配过程的可视化和监控。最后，将装配扭矩预测模型与数字孪生系统相结合，实现了装配扭矩的实时预测和在线控制，实验测试表明系统的响应时间约为 1s。因此，基于数字孪生的后桥装配扭矩预测和在线控制方法可以显著提高装配质量和装配效率，对推动智能生产线的建设具有重要意义。
