Predicting Surface Roughness and Flank Wear in Turning Processes

In advanced manufacturing, the tool quality is critical to ensure the product quality, and thus needs to be closely monitored. However, directly measuring the tool quality can be time-consuming and impractical for continuous manufacturing. In this work, leveraging on the Internet of Things (IoT) infrastructure and machine learning techniques, two important quality metrics for turning machinery tools, namely surface roughness and flank wear, are modeled using easily available sensory data from turning machines, including vibration data, force data, and cutting parameter information. A regression-based prediction model is proposed incorporating both time domain and frequency domain features as selected using sequential replacement feature selection algorithm. The regression model itself is selected using cross validation from multiple models including linear regression, quadratic regression, random forest, and Gradient Boosting Machine (GBM). Experiment using actual manufacturing data collected with a prototype IoT setup showed that the proposed model achieved high prediction accuracy of 0.860 and low variance as indicated by Adj-R2 of 0.722 for flank wear, and similarly for surface roughness the accuracy is 0.9525 on average and Adj-R2 is 0.7175 on average. This work demonstrated the prediction of the quality metrics of turning machinery tools, which are conventionally difficult to measure in continuous manufacturing, based on IoT sensory data and machine learning techniques.