Effect of SVM kernel functions on classification of vibration signals of a single point cutting tool

The studies on tool condition monitoring along with digital signal processing can be used to prevent damages on cutting tools and workpieces when the tool conditions become faulty. These studies have become more relevant in today’s context where the order realization dates are crunched and deadlines are to be met in order to catch up with the competition. Based on a continuous acquisition of signals with sensor systems it is possible to classify certain wear parameters by the extraction of features. Data mining approach is extensively used to probe into structural health of the tool and the process. This paper discusses condition monitoring of carbide tipped tool using Support Vector Machine and compares the classification efficiency between C-SVC and ν-SVC. It further analyses the results with other classifiers like Decision Tree and Naïve Bayes and Bayes Net. The vibration signals are acquired for various tool conditions like tool-good condition, tip-breakage, etc. The effort is to bring out the better features-classifier combination.

► Usage of statistical features of the vibration signals acquired in turning operation to classify various tool conditions. ► Comparison of kernels of SVM used to classify the vibration signals in a single point cutting tool environment. ► Comparison between C-SVM and ν-SVM kernel and the corresponding time taken to classify. ► Comparison of SVM results with classification accuracy obtained from other classifiers.