Knowledge Discovery by Decision Tree Using Experimental Data in High-Speed Turning of Steel with Ceramic Tool Insert

  • A. R. DharEmail author
  • N. Mandal
  • S. S. Roy
Conference paper
Part of the Lecture Notes on Multidisciplinary Industrial Engineering book series (LNMUINEN)


The manufacturing industry is of immense importance. Turning is one of the most basic operations performed across all manufacturing industries till date. Process parameter optimization and modeling in this field, which is very complex, have been investigated by many past researchers. Various methods like statistical techniques, and finite element-based and soft computing-based approaches were used to predict the machinability parameters like flank wear based on the given input cutting conditions like cutting speed, feed rate, depth of cut, etc. Nevertheless, a very few work was done in the area of knowledge discovery with the experimental data. In this work, efforts have been made to extract knowledge automatically using decision tree from the raw experimental data while turning EN24 steel with Cr2O3-doped zirconia toughened alumina (Cr-ZTA) ceramic tool insert. After that, the extracted knowledge in the forms of set of fuzzy rules was fed into a custom-made fuzzy logic control (FLC) system developed for predicting flank wear. The results of predictions are validated with experimental test data, and the capability of the system is stated with scope for improvements.


Tool flank wear Fuzzy c-means classification Decision tree Fuzzy logic control (FLC) system 


  1. 1.
    Mandal, N., Doloi, B., Mondal, B.: Application of back propagation neural network model for predicting flank wear of yttria based zirconia toughened alumina (ZTA) ceramic inserts. Trans. Indian Inst. Met. 68(5), 783–789 (2015)CrossRefGoogle Scholar
  2. 2.
    Guo, Y.B., Liu, C.R.: 3D FEA modeling of hard turning. J. Manuf. Sci. Eng.-Trans. ASME 124, 189–199 (2002)CrossRefGoogle Scholar
  3. 3.
    Çydaş, U.: Machinability evaluation in hard turning of AISI 4340 steel with different cutting tools using statistical techniques. Proc. Inst. Mech. Eng., Part B: J. Eng. Manuf. 224, 1043–1055 (2010)CrossRefGoogle Scholar
  4. 4.
    Wu, D., Jennings, C., Terpenny, J., Gao, R.X., Kumara, S.: A comparative study on machine learning algorithms for smart manufacturing: tool wear prediction using random forests. J. Manuf. Sci. Eng. 139(7), 71018 (2017)CrossRefGoogle Scholar
  5. 5.
    Singh, B.K., Mondal, B., Mandal, N.: Machinability evaluation and desirability function optimization of turning parameters for Cr2O3 doped zirconia toughened alumina (Cr-ZTA) cutting insert in high speed machining of steel. Ceram. Int. 42, 3338–3350 (2015)CrossRefGoogle Scholar
  6. 6.
    Dunn, J.C.: A fuzzy relative of the ISODATA process and its use in detecting compact well separated clusters. J. Cybern. 3(3), 32–57 (1974)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Bezdek, J.C.: A convergence theorem for the fuzzy ISODATA clustering algorithms. IEEE Trans. Pattern Anal. Mach. Intell. 2(1), 1–8 (1980)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Quinlan, J.R.: Induction of decision trees. Mach. Learn. 1, 81–106 (1986)Google Scholar
  9. 9.
    Ross, T.J.: Fuzzy logic with engineering applications, 3rd edn. Wiley, New Jersey (2010)CrossRefGoogle Scholar
  10. 10.
    Wang, C.H.: A study of membership functions on Mamdani-type fuzzy inference system for industrial decision-making. Thesis and dissertation, University of Lehigh (2015)Google Scholar

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© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Department of Mechanical EngineeringNational Institute of Technology DurgapurDurgapurIndia
  2. 2.Materials Processing & Microsystems LaboratoryCSIR-Central Mechanical Engineering Research InstituteDurgapurIndia

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