Digital map and probability compensation model for repeated positioning error of feed axis

Abstract

The randomness of the repeated positioning error of feed shaft is the main reason for the difficulty to address this error. The previous repeated loading and unloading suppression methods will easily do damages to parts. This paper sets out to investigate the probability distribution of all possible error values while feed shaft is at different positions, and determine the maximum value of probability error from the random errors. With feed axis at a certain position, first of all, we count the probability of each error based on a large number of random errors. We also draw the digital map of the repeated positioning error of this error with the error of positive and negative stroke measurement as the x-axis and y-axis coordinates and the probability of each error value as the z-axis coordinates. Secondly, based on the digital map of each position on the feed axis and combined with the dynamic optimization algorithm, we find the highest point on the map and take the coordinate point of this point as the starting point of each position compensation. Then, we set the error probability threshold to control the possibility of compensation errors and output the size and direction of the final compensation value. Finally, we start the compensation command and detect the compensated error. The error data that fail to meet the requirements will enter the probability statistics again, redraw the digital map and update the map. Through real-time detection and feedback, the digital map is dynamically improved to adapt to the changing environment. This probability compensation model of repeated positioning error can end the suppressing repeated positioning errors brought by repeatedly disassembling parts.