Faculty Publications
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Item Fault Diagnosis of Face Milling Tool using Decision Tree and Sound Signal(Elsevier Ltd, 2018) Madhusudana, C.K.; Kumar, K.; Narendranath, S.The monitoring of machining process can improve the quality of product and economy of production. The monitoring system helps to recognize and monitor the surface roughness, dimensional tolerance and tool condition. In this way, the condition monitoring system provides precise dimensional products, high productivity and enhanced machine tool life. This paper presents the classification of healthy and faulty conditions of the face milling tool using Decision tree (J48 algorithm) technique through machine learning approach. The sound signals of the face milling tool under healthy and faulty conditions are acquired. A set of discrete wavelet features are extracted from the sound signals using discrete wavelet transform (DWT) method. Decision tree technique is used to select prominent features out of all extracted features. The selected features are fed to the same algorithm for classification. Output of the algorithm is used to study and categorize the tool conditions. The decision tree model has provided a good classification accuracy of about 81% for the given sound signals and can be considered for fault diagnosis/condition monitoring. From the experimental results, it is suggested that the proposed method which comprises of decision tree and DWT techniques with sound signals can be recommended for the applications of fault diagnosis of the face milling tool. © 2017 Elsevier Ltd. All rights reserved.Item Condition monitoring of face milling tool using K-star algorithm and histogram features of vibration signal(Elsevier B.V., 2016) Madhusudana, C.K.; Kumar, H.; Narendranath, S.This paper deals with the fault diagnosis of the face milling tool based on machine learning approach using histogram features and K-star algorithm technique. Vibration signals of the milling tool under healthy and different fault conditions are acquired during machining of steel alloy 42CrMo4. Histogram features are extracted from the acquired signals. The decision tree is used to select the salient features out of all the extracted features and these selected features are used as an input to the classifier. K-star algorithm is used as a classifier and the output of the model is utilised to study and classify the different conditions of the face milling tool. Based on the experimental results, K-star algorithm is provided a better classification accuracy in the range from 94% to 96% with histogram features and is acceptable for fault diagnosis. © 2016 Karabuk UniversityItem Face milling tool condition monitoring using sound signal(Springer, 2017) Madhusudana, C.K.; Kumar, H.; Narendranath, S.This article presents the fault diagnosis of the face milling tool using sound signal. During milling, sound signals of the face milling tool under healthy and fault conditions are acquired. Discrete wavelet transform (DWT) features are extracted from the acquired sound signals. The support vector machine (SVM) technique is used to classify the face milling tool conditions using the extracted DWT features. Also, a comparison of classification efficiencies of different classifiers with respect to different features extraction methods is carried out. It is shown that, all extracted DWT features demonstrate better results than those obtained from selected statistical features and empirical mode decomposition features. The SVM technique is the best classifier as it has given an encouraging result in this study when compared to other classifiers, and it has provided 83% classification accuracy for the given experimental conditions and workpiece of steel alloy 42CrMo4. Hence, the SVM method and DWT technique can be put forward for the applications of condition monitoring of the face milling tool with sound signal. © 2017, The Society for Reliability Engineering, Quality and Operations Management (SREQOM), India and The Division of Operation and Maintenance, Lulea University of Technology, Sweden.Item Use of discrete wavelet features and support vector machine for fault diagnosis of face milling tool(Tech Science Press sale@techscience.com, 2018) Madhusudana, C.K.; Gangadhar, N.; Kumar, H.; Narendranath, S.This paper presents the fault diagnosis of face milling tool based on machine learning approach. While machining, spindle vibration signals in feed direction under healthy and faulty conditions of the milling tool are acquired. A set of discrete wavelet features is extracted from the vibration signals using discrete wavelet transform (DWT) technique. The decision tree technique is used to select significant features out of all extracted wavelet features. C-support vector classification (C-SVC) and ?-support vector classification (?-SVC) models with different kernel functions of support vector machine (SVM) are used to study and classify the tool condition based on selected features. From the results obtained, C-SVC is the best model than ?-SVC and it can be able to give 94.5% classification accuracy for face milling of special steel alloy 42CrMo4. © © 2018 Tech Science Press..