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Milling Cutter Flank Wear Prediction Using Ensemble of PSO-Optimized SVM and GLM Regression Models

  • B. StalinEmail author
  • M. Ravichandran
  • S. Marichamy
  • C. Anandavel Murugan
Conference paper
Part of the Lecture Notes in Mechanical Engineering book series (LNME)

Abstract

The main aim of this research is to build an accurate wear prediction model for predicting flank wear in milling cutters. Flank wear is predicted based on vibration and acoustic emission signals in the table and spindle of the machining center. The flank wear prediction model is built using regression ensembles that contains support vector regression and generalized linear regression models. The individual regression model in the ensemble is fine-tuned using particle swarm optimization (PSO). Generally, to fine-tune the models, hyper-parameters of the model need to be adjusted. A grid search is commonly used to find the optimum values for the hyper-parameters. Since grid search does not guarantee optimal values, particle swarm optimization is used to find the optimum values. Optimal values for the hyper-parameters results in individual optimal regression models. Then, by stacking the optimal regression models, highly accurate flank wear prediction model is built. The accuracy of the model is demonstrated using a high determination coefficient.

Keywords

Milling Particle swarm optimization Tool wear 

References

  1. 1.
    Shi D, Gindy NN (2007) Tool wear predictive model based on least squares support vector machines. Mech Syst Signal Process 21(4):1799–1814CrossRefGoogle Scholar
  2. 2.
    Benkedjouh T, Medjaher K, Zerhouni N, Rechak S (2015) Health assessment and life prediction of cutting tools based on support vector regression. J Intell Manuf 26(2):213–223CrossRefGoogle Scholar
  3. 3.
    Palanisamy P, Rajendran I, Shanmugasundaram S (2008) Prediction of tool wear using regression and ANN models in end-milling operation. Int J Adv Manuf Technol 37:29–41CrossRefGoogle Scholar
  4. 4.
    Caruana R, Niculescu-Mozil A, Crew G, Ksikes A (2004) Ensemble selection from libraries of models. In: Proceedings of the twenty-first international conference on machine learning, p 18. Banff, CanadaGoogle Scholar
  5. 5.
    Liu Y, Yao X, Higuchi T (2000) Evolutionary ensembles with negative correlation learning. IEEE Trans Evol Comput 4:380–387CrossRefGoogle Scholar
  6. 6.
    Albeahdili HM, Han T, Islam NE (2015) Hybrid algorithm for the optimization of training convolutional neural networks. Int J Adv Comput Sci Appl 6(10):79–85Google Scholar
  7. 7.
    Hadavandi E, Ghanbari A, Abbasian-Naghneh S (2010) Developing a time series model based on particle swarm optimization for gold price forecasting. In: Proceedings of the third international conference on business intelligence and engineering (BIFE), pp 337–340. IEEE Computer Society, Hong Kong, ChinaGoogle Scholar
  8. 8.
    Agogino A, Goebel K (2007) BEST lab. In: UC Berkeley, milling data set. NASA Ames prognostics data repository. http://ti.arc.nasa.gov/project/prognostic-data-repository, NASA Ames Research Center, Moffett Field, CA
  9. 9.
    Dornfeld D, Kannatey Asibu E (1980) Acoustic emission during orthogonal metal cutting. Int J Mech Sci 22(5):285–296CrossRefGoogle Scholar
  10. 10.
    Kamarthi S, Kumara S, Cohen P (2000) Flank wear estimation in turning through wavelet representation of acoustic emission signals. ASME J Manuf Sci Eng 122(1):12–19CrossRefGoogle Scholar
  11. 11.
    Jianming S, Yongxiang L, Gong W, Mengying Z (2016) Milling tool wear monitoring through time-frequency analysis of sensory signals. In: IEEE international conference on prognostics and health management (ICPHM)Google Scholar
  12. 12.
    Orhan S, Osman Er A, Camuşcu N, Aslan E (2007) Tool wear evaluation by vibration analysis during end milling of AISI D3 cold work tool steel with 35 HRC hardness. NDT E Int 40:121–126CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • B. Stalin
    • 1
    Email author
  • M. Ravichandran
    • 2
  • S. Marichamy
    • 3
  • C. Anandavel Murugan
    • 1
  1. 1.Department of Mechanical EngineeringAnna University, Regional Campus MaduraiMaduraiIndia
  2. 2.Department of Mechanical EngineeringK. Ramakrishnan College of EngineeringSamayapuram, TrichyIndia
  3. 3.Department of Mechanical EngineeringCMR Institute of TechnologyHyderabadIndia

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