Sensor placement utilizing a digital twin for thermal error compensation of machine tools

Abstract

Thermal errors in machine tools significantly impact precision and, therefore, productivity. Mitigating these errors often results in a trade-off between energy efficiency and accuracy. While data-driven compensation models show promise in addressing this challenge and achieving sustainable precision, their effectiveness hinges on the careful selection and placement of sensors as model inputs. This paper introduces a novel temperature sensor positioning method for thermal error compensation that leverages a digital twin framework to virtually determine ideal sensor positions and their effects on the compensation model. By accurately identifying temperature-sensitive points, our approach improves compensation accuracy and reduces the number of sensors required, thus enhancing both model robustness and operational efficiency. For choosing this set not only one simulation model is used but an ensemble with varying boundary conditions and thus model properties. Validation results show that the proposed method outperforms traditional, manually determined sensor placement strategies, providing a scalable solution for adaptable, energy-efficient thermal management in precision manufacturing. The selected sensor set based on a hybrid singular value decomposition and Least Absolute Shrinkage and Selection Operator approach yields a more robust compensation using only 7 instead of the manually chosen 22 temperature sensors. The thermal error reduction ranges from 77%–94% using simulated data with a corresponding reduction of 75%–85% achieved on the physical machine.