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Vibration-based tool condition monitoring in milling of Ti-6Al-4V using an optimization model of GM(1,N) and SVM

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Abstract

Titanium alloys are the difficult to cut metals due to their low thermal conductivity and chemical affinity with tool material. Since the tool vibration is a replica of tool wear and surface roughness, the present study has proposed a methodology for estimating tool wear and surface roughness based on tool vibration for milling of Ti-6Al-4V alloy using cemented carbide mill cutter. Experiments are conducted at optimum levels of cutting speed, feed per tooth, and depth of cut, and experimental results for the tool vibration, tool wear, and surface roughness are collected until the flank wear reached 0.3 mm (ISO3685:1993). In the next stage, an optimization model of grey prediction GM(1,N) system and support vector machine (SVM) are used and estimated tool wear and surface roughness related to tool vibration. The predicted values of tool wear and surface roughness are compared with the experimental results. The optimization model of GM(1,N) predicted the tool wear and surface roughness with an average error of 3.03% and as 0.7% respectively while the SVM predicted with an average error of 7.67% and 4.45%, respectively.

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Authors and Affiliations

  1. Department of Mechanical Engineering, Vignan’s Foundation for Science, Technology and Research, Vadlamudi, 522213, India

    Kaki Venkata Rao & Lam Suvrna Raju

  2. Department of Mining, Bule Hora University, College of Engineering and Technology, 144, Oromia, Ethiopia

    Yekula Prasanna Kumar

  3. Department of Mechanical Engineering, Madan Mohan Malaviya University of Technology, Gorakhpur, -273010, (U.P), India

    Vijay Kumar Singh

  4. Department of Mechanical Engineering, R.V. College of Engineering, Bengaluru, 560059, India

    Jinka Ranganayakulu

Authors
  1. Kaki Venkata Rao
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  2. Yekula Prasanna Kumar
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  3. Vijay Kumar Singh
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  4. Lam Suvrna Raju
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  5. Jinka Ranganayakulu
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Contributions

Dr. K Venkata Rao designed the experimental plan and supervised the experimentation. Dr. Rao is also involved in optimization of process parameters and writing the manuscript. In addition, tool wear in the study were studied. Dr. Y Prasanna Kumar and Dr. L Suvarna Raju proposed and executed the methodology for tool condition monitoring using the grey prediction model. Dr. Vijay Kumar Singh and Mr. Jinka Ranganayakulu are involved in experimentation and collected experimental results and are also involved in writing the manuscript.

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Correspondence to Yekula Prasanna Kumar.

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Rao, K.V., Kumar, Y.P., Singh, V.K. et al. Vibration-based tool condition monitoring in milling of Ti-6Al-4V using an optimization model of GM(1,N) and SVM. Int J Adv Manuf Technol 115, 1931–1941 (2021). https://doi.org/10.1007/s00170-021-07280-3

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  • DOI: https://doi.org/10.1007/s00170-021-07280-3

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