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Surface grindingSurface grinding is widely utilized in the manufacturing industry to achieve smooth finishes on flat surfaces. In this process, two critical performance characteristics are surface quality (Ra) and the material removal rate (MRR). In the present work, an attempt has been made to optimize the machining conditions for surface roughnessSurface roughness and MRRMRR based on 23Factorial design factorial designsDesign. Experiments were carried out under varying depth of cut, feed rate, and transverse speed. Analysis of varianceAnalysis of variance (ANOVA) (ANOVA) were employed to study the MRRMRR and Ra in the surface grinding process of M2 HSS Steel. It was observed that the feed rate was the most influential factor for Ra, while the depth of cut and feed rate were the most influential factors for MRRMRR. To validate the study, a confirmation experiment was conducted using the optimal set of parameters, and the predicted resultsResult closely matched the experimental findings.

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Optimization of Surface Grinding Parameters for M2 HSS Steel Using 23 Factorial Design

  • 会议论文

  • 发表日期：

    2025年

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  • Rahul Shukla,
  • Brajesh Kumar Lodhi

摘要

Surface grindingSurface grinding is widely utilized in the manufacturing industry to achieve smooth finishes on flat surfaces. In this process, two critical performance characteristics are surface quality (Ra) and the material removal rate (MRR). In the present work, an attempt has been made to optimize the machining conditions for surface roughnessSurface roughness and MRRMRR based on 23Factorial design factorial designsDesign. Experiments were carried out under varying depth of cut, feed rate, and transverse speed. Analysis of varianceAnalysis of variance (ANOVA) (ANOVA) were employed to study the MRRMRR and Ra in the surface grinding process of M2 HSS Steel. It was observed that the feed rate was the most influential factor for Ra, while the depth of cut and feed rate were the most influential factors for MRRMRR. To validate the study, a confirmation experiment was conducted using the optimal set of parameters, and the predicted resultsResult closely matched the experimental findings.