Abstract
The productivity and quality in the turning process can be improved by utilizing the predicted performance of the cutting tools. This research incorporates condition monitoring of a non-carbide tool insert using vibration analysis along with machine learning and fuzzy logic approach. A non-carbide tool insert is considered for the process of cutting operation in a semi-automatic lathe, where the condition of tool is monitored using vibration characteristics. The vibration signals for conditions such as heathy, damaged, thermal and flank were acquired with the help of piezoelectric transducer and data acquisition system. The descriptive statistical features were extracted from the acquired vibration signal using the feature extraction techniques. The extracted statistical features were selected using a feature selection process through J48 decision tree algorithm. The selected features were classified using J48 decision tree and fuzzy to develop the fault diagnosis model for the improved predictive analysis. The decision tree model produced the classification accuracy as 94.78% with five selected features. The developed fuzzy model produced the classification accuracy as 94.02% with five membership functions. Hence, the decision tree has been proposed as a suitable fault diagnosis model for predicting the tool insert health condition under different fault conditions.
Highlights
Tool wear during metal cutting operation is a cause of major concern in manufacturing industry, as it degrades the quality of product during manufacturing process and would lead to economic losses for the manufacturing unit
The feature selection process was carried out using J48 decision tree algorithm
Eleven statistical parameters namely standard error, sample variance, kurtosis, skewness, standard deviation, minimum, maximum, mean, median, range and sum were extracted from the acquired vibration signals
Summary
Tool wear during metal cutting operation is a cause of major concern in manufacturing industry, as it degrades the quality of product during manufacturing process and would lead to economic losses for the manufacturing unit. Alonso et al studied the possibilities of vibration signature for monitoring the tool wear [9] Data modeling using machine learning approach are normally employed to solve such problems. The vibration signals obtained under all fault conditions were processed for extracting features. There is a limited study on the tool condition monitoring using machine learning and fuzzy logic. An effort has been made for monitoring the tool condition using the decision tree and the fuzzy inference engine. The selected features were classified using the decision tree algorithm and the fuzzy model. (ii) Section 3 explains about machine learning approach which includes the feature extraction, feature selection and feature classification process (iii) Section 4 demonstrates the result and discussion.
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