Falut Diagnosis of Single Point Cutting Tool Through Online and Offline Monitoring Techniques

Abstract

Tool condition monitoring plays a crucial role in automated industry to monitor the state of cutting tool. It prevents any hazards occurring to the machine, avoid deterioration of the surface finish on end product and it helps to introduce a new tool in an instant at which the existing tool has worn out toensure safety, productivity and optimum performance of the metal cutting process. In the present research work,fault diagnosis of single point cutting tool is investigated based on the vibration signals and cutting force signals on an engine lathe. Vibration signals and cutting force signals corresponding to a healthy insert (baseline) anddifferent types of industrial practical worn out insertswere recorded. The research work is carried out in three phases. The first phase investigates fault diagnosis of cutting tool using signal processing techniquessuch astime domain, spectrum, cepstrum, continuous wavelet transform (CWT), recurrence plots (RPs) and recurrence quantification analysis (RQA). The result shows that recurrence plots and recurrence quantification analysis were useful for revealing post fault detection and diagnosis of worn states of the inserts. The second phase of research workpresents fault diagnosis of cutting tool using machine learning approach based on vibration signals. From the vibration signals, statisticalfeatures, histogram features, discrete wavelet transform (DWT) features and empirical mode decomposition (EMD) featureswere extracted. Principle component analysis (PCA) and J48 algorithm (decision tree) were used for important feature selection/reduction. Artificial neural network (ANN), Naïve Bayes, Bayes net, support vector machine (SVM), K-star and J48 algorithm classifiers have been used to classify the different fault conditions. Classification accuracy is found to be reasonably good with J48 algorithm feature selection compared to PCA. The third phase presents the results of investigations undertaken to find suitability of vibration signals and cutting forces to detect the condition of tungsten carbide cutting tool insert, surface roughness and type of chip formation. The results show that there is an increase in the level of acceleration and cutting force at faulty tool condition ascompared with the healthy condition of the tool. Based on this finding, cutting tool acceleration and cutting forces can be used to predict the cutting tool condition, surface roughness and chip formation type. Qualitative comparisons of the computational predicted forces are drawn by plotting the trends of the predicted forces together with the measured forces. The Deform-3D has correctly predicted this trend which is consistent with the experimental trends of the cutting forces components. Tool wear analysis has been carried out on the worn tungsten carbide insert cutting tools to find the tool wear mechanisms.Based on SEM micrographsof worn surface of the cutting tool,micro-abrasion, micro-attrition, adhesion and micro-fatigue behaviors are identified as the dominant kinds of wear mechanisms.