Investigation of the effect of vibration in the multi-objective optimization of dry turning of hardened steel

  • Ahmed M
  • Juberi H
  • Bari A
  • et al.
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Abstract

Purpose This study aims to investigate the effect of vibration on ceramic tools under dry cutting conditions and find the optimum cutting condition for the hardened steel machining process in a computer numerical control (CNC) lathe machine. Design/methodology/approach In this research, an integrated fuzzy TOPSIS-based Taguchi L9 optimization model has been applied for the multi-objective optimization (MOO) of the hard-turning responses. Additionally, the effect of vibration on the ceramic tool wear was investigated using Analysis of Variance (ANOVA) and Fast Fourier Transform (FFT). Findings The optimum cutting conditions for the multi-objective responses were obtained at 98 m/min cutting speed, 0.1 mm/rev feed rate and 0.2 mm depth of cut. According to the ANOVA of the input cutting parameters with respect to response variables, feed rate has the most significant impact (53.79%) on the control of response variables. From the vibration analysis, the feed rate, with a contribution of 34.74%, was shown to be the most significant process parameter influencing excessive vibration and consequent tool wear. Research limitations/implications The MOO of response parameters at the optimum cutting parameter settings can significantly improve productivity in the dry turning of hardened steel and control over the input process parameters during machining. Originality/value Most studies on optimizing responses in dry hard-turning performed in CNC lathe machines are based on single-objective optimization. Additionally, the effect of vibration on the ceramic tool during MOO of hard-turning has not been studied yet.

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APA

Ahmed, Md. T., Juberi, H., Bari, A. B. M. M., Rahman, M. A., Rahman, A., Arefin, Md. A., … Quader, N. (2023). Investigation of the effect of vibration in the multi-objective optimization of dry turning of hardened steel. International Journal of Industrial Engineering and Operations Management, 5(1), 26–53. https://doi.org/10.1108/ijieom-11-2022-0059

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