Thermal displacement prediction model with a structural optimized transfer learning technique

Thermal deformation of the spindle accounts for a large proportion of existing errors. After gathering data on thermal deformation through an experiment with a machine tool, AI algorithms were used in this study to predict the displacement of a cutting tool caused by heat deformation. Thermal displacement and temperature data were entered into models constructed using several machine learning algorithms. These models were then quantitatively evaluated in terms of their accuracy and compared to each other. Subsequently, transfer learning and hyperparameter tuning were conducted to produce a model with optimal prediction capability. The experimental results revealed that after machine learning models were trained using data collected on the first day of the experiments, their predictions based on data collected on the second day of the experiments were rife with severe prediction errors. This outcome indicated that experimental data gathered at different times weakened the models' predictive abilities. Thus, to increase the prediction accuracy and prevent time from being wasted on repeated training, transfer learning were incorporated with model optimization. Finally, this approach achieved excellent R2 scores of 0.99941, 0.99964, and 0.99902 for the prediction of displacement in the x-, y-, and z-directions.

Automated summary

Using AI algorithms, this study predicted the displacement of a cutting tool caused by thermal deformation based on data collected from machine tool experiments. Multiple machine learning models were constructed and evaluated for accuracy. The incorporation of transfer learning and model optimization was found to improve prediction accuracy and mitigate the negative effects of data collected at different times.