The Thermal Error Estimation of the Machine Tool Spindle Based on Machine Learning

Abstract

Thermal error is one of the main sources of machining error of machine tools. Being a key component of the machine tool, the spindle will generate a lot of heat in the machining process and thereby result in a thermal error of itself. Real-time measurement of thermal error will interrupt the machining process. Therefore, this paper presents a machine learning model to estimate the thermal error of the spindle from its feature temperature points. The authors adopt random forests and Gaussian process regression to model the thermal error of the spindle and Pearson correlation coefficients to select the feature temperature points. The result shows that random forests collocating with Pearson correlation coefficients is an efficient and accurate method for the thermal error modeling of the spindle. Its accuracy reaches to 90.49% based on only four feature temperature points—two points at the bearings and two points at the inner housing—and the spindle speed. If the accuracy requirement is not very onerous, one can select just the temperature points of the bearings, because the installation of temperature sensors at these positions is acceptable for the spindle or machine tool manufacture, while the other positions may interfere with the cooling pipeline of the spindle.

Research on thermal error modeling of CNC milling head for five-axis machine tools based on GA-ACO optimized BP neural network

“…Li et al also used the method in literature [4], with the difference that Li established a comprehensive temperature information (STI) matrix and used the cluster validity indexes (CVI) to determine the number of clusters [5]. Chiu et al selected four characteristic temperature points using Pearson correlation coefficient [6]. Tan et al used the least absolute shrinkage and selection operator (LASSO) to screen heat key points [7].…”

Research on Thermal Error Modeling of Motorized Spindle Based on BP Neural Network Optimized by Beetle Antennae Search Algorithm

“…Li et al [7] proposed a temperature-sensitive point selection method based on integrated temperature information (STI), which solved the problem of incomplete clustering and the same number of temperature-sensitive points with different errors. Chiu et al [8] used the Pearson correlation coefficient method to remove the temperature point with low correlation. Li et al [9] used the method of combining fuzzy clustering with average influence value (FCM-MIV) to group temperature variables and select temperature sensitive points.…”

PMSM Speed Control Based on Particle Swarm Optimization and Deep Deterministic Policy Gradient under Load Disturbance

“…Supervised learning deals mainly with classification and regression problems. It requires well-labelled dataset, such as a convolutional neural network [27], long shortterm memory [28], and random forest [29]. In addition, in the literature [30], a two-layer recursive neural network was used to approximate a motor model, and the network was considered to be a motor speed predictor.…”