Research on Machine Tool Thermal Error Compensation Based on an Optimized LSTM Model

Thermal error is a significant factor affecting the machining accuracy of machine tools, and error compensation is an economical and effective method to improve machine tool accuracy. However, traditional modeling methods face challenges such as insufficient nonlinear mapping capability and difficulty in parameter optimization when processing time-series data. This paper establishes a thermal error model using a Long Short-Term Memory (LSTM) neural network optimized by the Particle Swarm Optimization (PSO) algorithm (PSO-LSTM). Through thermal characteristic experiments, thermal error data and temperature rise data at various points of the T55II-500 CNC machine tool during actual machining were collected. First, fuzzy clustering and global sensitivity analysis were employed to identify the temperature-sensitive points of the machine tool. Using the temperature rise data of these sensitive points and the thermal errors of machined workpieces as data samples and optimizing the LSTM prediction model with the PSO algorithm, a PSO-LSTM thermal error prediction model was established. To verify its superiority and practicality, this paper conducts a comparative analysis with traditional thermal error prediction models based on Backpropagation (BP) neural network, Long Short-Term Memory (LSTM) network, Multiple Linear Regression (MLR), and Multivariate Nonlinear Regression (MNR). The results show that the PSO-LSTM model outperforms the other models in terms of relative error, average residual, maximum residual, and mean squared error. On this basis, a real-time thermal error compensation system was developed. Under the conditions of near-constant temperature (19.34–20.36 °C), warm natural ventilation (20.63–22.13 °C), and a wider variable temperature range (18.64–28.24 °C), the compensated thermal errors converge from 52 μm, 57 μm, and 67 μm to 4–12 μm, 6–11 μm, and 5–9 μm, respectively, with precision improved by 86%, 88%, and 86%. This effectively reduces the impact of thermal errors and improves the machining accuracy of the machine tool.