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Tool Condition Monitoring in Face Milling Process Using Decision Tree and Statistical Features of Vibration Signal
Published October 11, 2019 by SAE International in United States
This content contains downloadable datasetsAnnotation ability available
Event: International Conference on Advances in Design, Materials, Manufacturing and Surface Engineering for Mobility
In milling process, the quality of the machined component is highly influenced by the condition of the tool. Hence, monitoring the condition of the tool becomes essential. A suitable mechanism needs to be devised in order to monitor the condition of the tool. To achieve this, condition monitoring of milling tool is taken up for the study. In this work, the condition of the tool is classified as good tool and tool with common faults in face milling process such as flank wear, worn out and breakage of the tool based on machine learning approach using statistical feature and decision tree technique. Vibration signals of the milling tool are obtained during machining of mild steel. Statistical features are extracted from the obtained signal, in which the important features are selected using decision tree. The selected features are given as the input to the same algorithm. The output of the algorithm is utilized for classifying the different conditions of the tool. The experimental results show that the accuracy of decision tree technique is at the acceptable level and can be recommended for fault diagnosis of face milling tool. The final results are also compared with standard bench mark algorithm i.e., Artificial Neural Network (ANN).
CitationDurairaj, P. and Vaithiyanathan, M., "Tool Condition Monitoring in Face Milling Process Using Decision Tree and Statistical Features of Vibration Signal," SAE Technical Paper 2019-28-0142, 2019.
Data Sets - Support Documents
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- Ali, S.M., Hui, K.H., Hee, L.M., and Leong, M.S. , “Automated Valve Fault Detection Based on Acoustic Emission Parameters and Support Vector Machine,” Alexandria Engineering Journal 57(1):491-498, 2018.
- Altobi, M.A.S., Bevan, G., Wallace, P., Harrison, D. and Ramachandran, K.P. , “Fault Diagnosis of a Centrifugal Pump Using MLP-GABP and SVM with CWT,” Engineering Science and Technology, an International Journal 2019. (In press)
- Ambhore, N., Kamble, D., Chinchanikar, S., and Wayal, V. , “Tool Condition Monitoring System: A Review,” Materials Today: Proceedings 2(4):3419-3428, 2015.
- Giorgi, M.G.D., Campilongo, S., and Ficarella, A. , “A Diagnostics Tool for Aero-Engines Health Monitoring Using Machine Learning Technique,” Energy Procedia 148:860-867, 2018.
- Karam, S., Centobelli, P., D’Addona, D.M., and Teti, R. , “Online Prediction of Cutting Tool Life in Turning via Cognitive Decision Making,” Procedia CIRP 41:927-932, 2016.
- Muralidharan, V., Sugumaran, V., and Sakthivel, N.R. , “Wavelet Decomposition and Support Vector Machine for Fault Diagnosis of Monoblock Centrifugal Pump,” International Journal of Data Analysis Techniques and Strategies 3(2):159-177, 2011.
- Muralidharan, V., Sugumaran, V., and Indira, V. , “Fault Diagnosis of Monoblock Centrifugal Pump Using SVM,” Engineering Science and Technology, an International Journal 17(3):152-157, 2014.
- Madhusudana, C.K., Budati, S., Gangadhar, N., Kumar, H., and Narendranath, S. , “Fault Diagnosis Studies of Face Milling Cutter Using Machine Learning Approach,” Journal of Low Frequency Noise, Vibration and Active Control 35(2):128-138, 2016.
- Madhusudana, C.K., Kumar, H., and Narendranath , “Condition Monitoring of Face Milling Tool Using K-star Algorithm and Histogram Features of Vibration Signal,” Engineering Science and Technology, an International Journal 19(3):1543-1551, 2016.
- Painuli, S., Elangovan, M., and Sugumaran, V. , “Tool Condition Monitoring Using K-star Algorithm,” Expert Systems with Applications 41(6):2638-2643, 2014.
- Praveenkumar, T., Saimurugan, M., and Ramachandran, K. , “Comparison of Vibration, Sound and Motor Current Signature Analysis for Detection of Gear Box Faults,” International Journal of Prognostics and Health Management 8(2):1-10, 2017.
- Rehorn, A.G., Jiang, J., and Orban, P.E. , “State-of-the-Art Methods and Results in Tool Condition Monitoring: A Review,” The International Journal of Advanced Manufacturing Technology 26(7):693-710, 2005.
- Sugumaran, V., Muralidharan, V., Ravi Teja, C., and Hegde, B.K. , “Intelligent Process Selection for NTM-a Neural Network Approach,” International Journal of Industrial Engineering Research and Development 1(1):87-96, 2010.