An improved displacement sensor accuracy calibration method for ball screw positioning error compensation

In a ball screw precision positioning system, a high-precision external displacement sensor can effectively identify the positioning error of the ball screw. However, high accuracy comes with a high cost. Ultrasonic sensors offer an inexpensive, in-line technique that can be integrated with neural networks, to improve the precision of ultrasonic sensor. In this paper, an ultrasonic sensor accuracy calibration mothed is proposed, namely, frequency feature extraction deep residual shrinkage network (FFE-DRSN), which used probability statistics and test data to enhance the resolution of ultrasonic sensors. The efficacy of the developed methods is validated through experiments of the ball screw positioning error compensation with different work conditions. The experimental results verify that the FFE-DRSN precision correction model improves positioning accuracy by at least 78% compared to the uncompensated case.