Tool Wear Monitoring in Turning Operation Using Vibration and Strain Measurement With Neural Network

Abstract

The selection of appropriate monitoring processes is an important decision to be made. Monitoring of manufacturing processes plays a very important role to avoid down time of the machine, or prevent unwanted conditions such as chatter, excessive tool wear or breakage. Most monitoring systems developed up to date employ force, acoustic emission and vibration, or a combination of these and other techniques with a sensor integration strategy. In this work, the implementation of a monitoring system utilizing simultaneous vibration and strain measurements on the tool tip is investigated for the average flank wear of coated carbide tools which are used in finishing turning process, with cast iron shaft as a work-piece. Data from the manufacturing processes were recorded with one piezoelectric strain sensor and an accelerometer, each coupled to the data acquisition card. There are 24 features indicative of tool wear were extracted from the original signal. These include features from the time domain, frequency domains, time-series model coefficients and four packet features extracted from wavelet packet analysis. The (2 × 3) self organizing map (SOM) neural network was employed to identify the tool state as a result of the present work, the tool wear classified by applied two independent wear test as a training tests, and checked the results of the SOM by applied an independent test, finally we have an SOM model can classifying the tool wear with minimal error.