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Abstract The wear of cutting tools during milling processes not only constrains the volume of material that can be removed by a tool but also results in a progressive deterioration of the quality of the workpiece. Although there are established methodologies for predicting tool wear, there is a paucity of knowledge regarding the impact of tool wear on shape error. The authors present a data-driven soft sensor to model the effect of tool wear on shape error, obviating the need for direct tool wear measurement. To evaluate this approach, a milling experiment was conducted, wherein process forces, spindle current, and resulting shape error were measured. Furthermore, a geometrical cutting simulation was conducted in order to obtain cutting conditions, including the volume of material removed. This study examines the contribution of these features to the prediction performance of the proposed soft sensor. Additionally, the transferability from models trained on different tools is investigated to ascertain the impact of tool wear variance on prediction performance. Prediction experiments demonstrate that a soft-sensor based on a combination of simulation and process monitoring data enables a model trained on data from multiple milling tools to account for wear and predict shape error well under varying wear scenarios. The approach presented here has been demonstrated to result in a reduction of the prediction error of up to 60% compared to an average baseline prediction.
Denkena et al. (Tue,) studied this question.