Input attribute optimization for thermal deformation of machine-tool spindles using artificial intelligence

Abstract

The heat generated due to internal and external rotating components, electrical parts, and varying ambient temperatures can cause thermal deformations and significantly impact the precision of machine tools (MTs). Thermal error is crucial in industrial processes, corresponding to approximately 60–70% of MT errors. Accordingly, developing an accurate thermal error prediction model for MTs is essential for their high precision. Therefore, this study proposes an artificial neural network (ANN) model to predict the thermal deformation of a high-speed spindle. However, an important feature for the development of a reliable prediction model is the optimization of the input parameters such that the model generates accurate predictions. Hence, the development of an algorithm to determine the optimal input parameters is essential. Therefore, a genetic algorithm (GA)-based optimization model is also developed in this study to select the optimal input combinations (supply coolant temperature, coolant temperature difference between the inlet and outlet of the spindle, and supply coolant flow rate) for different spindle speeds ranging from 10,000 to 24,000 rpm in increments of 2000 rpm. The R² values of the ANN prediction model are in the range of 0.94 to 0.98 for different spindle speeds. Furthermore, the optimized input parameters are used in single- and dual-spindle systems to verify the accuracy of the developed model as per ISO 230-3. For a single-spindle system, the thermal deformation prediction accuracy of the developed model is in the range of 96.26 to 98.82% and within 1.04 μm compared with the experimental findings. Moreover, when applied to a dual-spindle system, the model’s accuracy is improved by 7.31% compared with that of the variable coolant volume (VCV) method. The maximum deviation of the dual-spindle system can be controlled to within 2.52 μm using the optimized input parameters for a single-spindle system without further optimizing the parameters. The results show that the proposed input attribute optimization (IAO) model can also be adopted for dual-spindle systems to achieve greater prediction accuracy and precision of the machining process, and one industrial cooler can be used for multiple spindles of the same type. In dual-spindle systems operating at different spindle speeds, the power consumption could be reduced by 11% to 34%, and the total lifetime CO₂ emissions could be reduced from 72,981 to 52,595.5 kg. These substantial reductions in energy consumption and CO₂ emissions highlight the potential of dual-spindle systems to contribute to sustainable manufacturing.

Graphical abstract