Mill condition monitoring based on instantaneous identification of specific force coefficients under variable cutting conditions

Abstract

Following the necessity of increased performance and availability requirements for manufacturing systems, research is becoming more and more attracted by monitoring solutions for cutting tools. In this paper, a robust unsupervised strategy for milling tool wear monitoring under variable process parameters and lubrication conditions is presented. The proposed method is completely unsupervised, thus not requiring any kind of training procedure, and is validated on different machine tools. The solution is based upon the online estimation of specific force coefficients (SFC) from instantaneous cutting forces in high-feed milling of Ti6Al4V workpiece. This avoids the need for continuously variable feed per tooth during cutting tests, necessitated for the application of reference literature approach. For this purpose, a novel high-feed mill mechanistic model was conceived and developed. Five run-to-failures were performed in different lubrication conditions – cryogenic and traditional lubrication – with different cutting speeds (50 m/min, 70 m/min and 125 m/min) on two different machine tools. Principal Component Regression was introduced in order to deal with the variability of the estimated coefficients. Self-starting tabular cusum control charts were implemented and demonstrated high accuracy and reliability in the prediction of notch wear phenomena as well as chipping of tool cutting edges for all the cases considered. The solution detected an out-of-control conditions ranging from 166μm to 499μm of maximum flank wear for the analysed tests. The mean prediction error with respect to the 600μm threshold is of −45% with a peak of −72%, whereas reference literature algorithms reach −57% and −66%, respectively. A sensitivity analysis of control chart threshold was performed with reference to the maximum flank wear at the detection point. In a supervised scenario, the threshold can be increased to obtain a less conservative approach: for instance, a mean prediction error of −41% was reached by doubling the threshold.