Abstract Increasing amount of available process data in the manufacturing industry together with analysis methods like machine learning provides new possibilities to increase the manufacturing efficiency and to rethink existing process structures. For example, assessing the workpiece quality in an early machining stage can be used to alter the quality control strategy, to increase the product quality, and to reduce the number of scrap parts which leads to savings in terms of time, resources, and cost. In this work, torque measurements obtained from the numerical control of a milling-machine in the serial production of hydraulic valves are used to predict the concentricity as well as the diameter of drilled and reamed bores of the valves. Statistical features are determined out of the torque measurements, which are presented as time series. The prediction of the quality is achieved with the machine learning method of random forest (RF) on the basis of the extracted features. The Pearson correlation between the features and the quality characteristics as well as the learning curves of the RF method are studied. It turns out, that a strong correlation comes along with a fast decreasing learning curve of the RF but gives no information over the achievable prediction accuracy with the RF. The obtained predictions are very precise and evaluated with four statistical criteria. A mean absolute error of 17.1 µm for the concentricity and only 0.27 µm for the diameter is achieved. In addition, the coefficients of determination of the concentricity and the diameter are 96.3% and 94.1% respectively, very high. It can be stated, that for the considered use case in this paper, a precise quality prediction on the basis of torque measurements and RF could be implemented which would make a much faster and efficient quality control possible.
Quality Prediction of Drilled and Reamed Bores Based on Torque Measurements and the Machine Learning Method of Random Forest