Thermal error modelling of motorised spindle in large-sized gear grinding machine. (April 2017)

• Record Type: Journal Article
• Authors: Dai, He; Wang, Shilong; Xiong, Xin; Zhou, Baocang; Sun, Shouli; Hu, Zongyan
• Is Part Of: Proceedings of the Institution of Mechanical Engineers. Volume 231:Number 5(2017)
• Page Start: 768
• Page End: 778
• Publication Date: 2017-04
• Subjects: Thermal error -- large-sized gear grinding machine -- motorised spindle -- adaptive neuro-fuzzy inference system -- particle swarm optimisation support vector machine; Mechanical engineering -- Periodicals; Engineering -- Management -- Periodicals; Manufacturing processes -- Periodicals; 629.8
• DOI: 10.1177/0954405417696335
• Languages: English
• ISSNs: 0954-4054

Abstract

Thermal errors are one of the most significant factors that influence the machining precision of machine tools. For large-sized gear grinding machine tools, thermal errors of beds, columns and rotary tables are decreased by their huge heat capacity. However, different from machine tools of normal sizes, thermal errors increase with greater power in motorised spindles. Thermal error compensation is generally considered as a relatively effective, convenient and cost-efficient approach in thermal error control and reduction. This article proposes two thermal error prediction models for motorised spindles based on an adaptive neuro-fuzzy inference system and support vector machine, respectively. In the adaptive neuro-fuzzy inference system–based model, the temperature values are divided into different groups using subtractive clustering. A hybrid learning scheme is adopted to adjust membership functions so as to learn from the input data. In the particle swarm optimisation support vector machine–based model, particle swarm optimisation is used to optimise the hyperparameters of the established model. Thermal balance experiments are conducted on a large-sized computer numerical control gear grinding machine tool to establish the prediction models. Comparative results show that the adaptive neuro-fuzzy inference system model has higher prediction accuracy (with residual errors within ±2.5 μm in the radial direction and ±3 μm in the axial direction) than the support vector machine model.