Data-driven thermally-induced error compensation method of high-speed and precision five-axis machine tools

• Published in: Mechanical systems and signal processing Vol. 138; p. 106538
• Main Authors: Liu, Jialan, Ma, Chi, Wang, Shilong
• Format: Journal Article
• Language: English
• Published: Berlin Elsevier Ltd 01.04.2020; Elsevier BV
• Subjects: Axes (reference lines); Axis movements; Compensation; Error analysis; Error compensation; Error reduction; Five axis; Five-axis machine tool; High speed; Homogeneous transformation; Independent variables; Machine tools; Machining; Mathematical analysis; Matrix methods; Polynomials; Thermally-induced error; Homogeneous transformation; Five-axis machine tool; Thermally-induced error; Error compensation
• ISSN: 0888-3270; 1096-1216
• DOI: 10.1016/j.ymssp.2019.106538

[Display omitted] •The data-driven compensation method of the 5-axis machine tools is proposed.•The relationship between the thermal error and positioning error is explored. A novel thermal error is proposed for feed drive systems.•The regression analysis is effective to establish the thermal error model.•The pre-stretch can improve the positioning accuracy. The data-driven thermal error compensation method of high-speed and precision five-axis machine tools was proposed based on the homogeneous transformation. The compensation component was obtained by analyzing the error transmission chain of machine tools and was expressed as the transmission matrix consisting of thermal error terms of linear axes and spindle system according to the differential movement of the compensation axis. From the view of thermal error generation mechanism, the thermal error of the linear axis was formulated as the product of the polynomial function with time as its independent variable and the polynomial function with position as its independent variable. Then the thermal errors of the linear axes were decomposed and identified from the measured positioning error. The thermal error of the spindle system was identified by the thermal characteristic experiments. The compensation component in each direction is calculated by substituting the identified thermal error terms of the linear axes and spindle system into the data-driven thermal error model. Finally, to demonstrate the effectiveness of the proposed method, the thermal error at a new working condition was predicted, and then the error compensation and actual machining were carried out. The results show that the machining error is reduced by more than 85% and 37% with the present error compensation compared with that without compensation and that without traditional error compensation, respectively. This research sheds new light on both the generation mechanism and the compensation method of the thermally-induced error of five-axis machine tools.