Tool Failure Monitoring in Turning by Pattern Recognition Analysis of AE Signals

Abstract

Sensing of both gradual and catastrophic tool failure is a key aspect in producing high quality parts on fully automated machine tool systems. Acoustic emission provides a means of sensing tool failure, since it is generated from the processes that cause tool failure (e.g., tool wear, tool fracture). A linear discriminant function-based technique for detection of tool wear, tool fracture, or chip disturbance events is developed using the spectra of signals generated by these sources. In addition, a methodology for determining the feature dimensionality, the selection of best features, and the minimum training sample size is presented. The concepts of classification error minimization and manufacturing cost minimization have been applied to design classifiers using a hierarchical decision strategy to improve the performance of tool failure sensing. Results of an application indicate an 84 to 94 percent reliability for detecting tool failure of any type.