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Fault Diagnosis of a Single Point Cutting Tool using Statistical Features by Simple CART Classifier


  • School of Mechanical and Building Sciences, VIT University, Chennai - 600127, Tamil Nadu, India
  • Department of Mechanical Engineering, Amrita School of Engineering, Coimbatore. Amrita Vishwa Vidyapeetham, Amrita University, Coimbatore – 641112, Tamil Nadu, India


Objective: Tool condition monitoring is an important aspect of the modern day manufacturing system. It plays a significant role in increasing the efficiency of machining operation by identifying defects at a very early stage. Tool wear decreases the life of the tool considerably, increases the length of the machining process, also affects the surface finish and the dimensional accuracy of the product. To identify whether the tool is in a good or faulty condition, a monitoring system is essential. Method/Analysis: The fault diagnosis of the single point cutting tool was accomplished with the vibration signals obtained from auniaxial accelerometer attached to the cutting tool in a lathe machine. In this study, three different spindle speeds, feed rates and depth of cuts and four different wear levels of cutting tool are considered. Statistical data obtained from the signals is classified using a decision tree algorithm to get substantial features. The recognized features are considered in classifying data by using Simple CART classifier. Findings: The accuracy of the classifier was found to be 73.38% for the model with all the signals combined. The classification accuracy was observed to improve with the reduction in complexity of the model. The classification accuracy obtained for the model with only varying feed rate and depth of cut was in the range of 81–87 %. On further reduction of the model to have varying depth of cut was found to have a classification accuracy in the range of 81.5–91 %. The model with all the parameters independent yielded classification accuracy in the range of 81–100 %. Applications/Improvements: This study broadly analysed the use of simple CART classifier to diagnose fault in the cutting tool during machining. It can be used to increase productivity and reduce machine downtime. The improvements can be made to this study by considering different feature extraction techniques for more reliability.


Decision Tree, Feature Extraction, Simple CART, Statistical Features, Tool Condition Monitoring

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  • Elangovan M, Ramachandran KI, Sugumaran V. Studies on Bayes classifier for condition monitoring of single point carbide tipped tool based on statistical and histogram features. Expert Systems with Applications. 2010 Mar; 37(3):2059–65.
  • Gangadhar N, Kumar H, Narendranath S, Sugumaran V. Fault diagnosis of single point cutting tool through vibration signal using decision tree algorithm. Procedia Materials Science. 2014 Dec; 5:1434–41.
  • Grote KH, Antonsson EK. Springer handbook of mechanical engineering. Berlin Heidelberg: Springer; 2009.
  • Thamizhmanii S, Hasan S. Measurement of surface roughness and flank wear on hard martensitic stainless steel by CBN and PCBN cutting tools. Journal of Achievements in Materials and Manufacturing Engineering. 2008 Dec; ,31(2):415–21.
  • Sundaram S, Senthilkumar P, Kumaravel A, Manoharan N. Study of flank wear in single point cutting tool using acoustic emission sensor techniques. ARPN Journal of Engineering and Applied Sciences. 2008 Aug; 3(4):32–6.
  • Teli S, Kanikar P. A survey on decision tree based approaches in data mining. International Journal of Advanced Research in Computer Science and Software Engineering. 2015 Apr; 5(4):613–7.
  • Kalmegh S. Analysis of WEKA data mining algorithm REPTree, Simple CART and RandomTree for classification of Indian news. International Journal of Innovative Science, Engineering and Technology. 2015 Feb; 2(2):438–46.
  • Gordon L. Using classification and Regression trees (CART) in SAS® Enterprise MinerTM for applications in public health. SAS Global Forum; 2013. p. 1–8.
  • Aher SB, Lobo LM. Comparative study of classification algorithms. International Journal of Information
  • Technology. 2012 Jul-Dec; 5(2):307–10.


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