An effective robotic processing errors prediction method considering temporal characteristics

Robotic milling processing has become an important means of advanced manufacturing technology. However, the limited machining accuracy restricts the development of robotic milling processing technology. Errors prediction and compensation are effective means to improve robot accuracy. This paper presents a combined statistical principles and machine learning model that achieves high robot milling errors prediction accuracy, called PSO-ARIMA. It is an Auto-regressive Integrated Moving Average (ARIMA) model with milling force correction that has been optimized by the Particle Swarm Optimization (PSO). Compared to the other five existing algorithms, the proposed method has the highest prediction accuracy. The maximum MAE for pose errors prediction in the four validation tasks is only 0.021 mm and 0.011°, which meets the actual application requirements. It can efficiently and accurately accomplish online prediction of errors to improve the accuracy of robotic milling.