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<p>This paper presents a comprehensive analysis of milling operation of 15 − 5 Precipitation hardened stainless steel using Response Surface Methodology. The objective was to develop a robust empirical model relating critical process parameters namely spindle speed, feed, and depth of cut to a key response variable, cutting temperature. A designed experiment was executed, and the data were analysed to fit a quadratic model. The analysis of variance confirmed the high significance of the model (p-value &lt; 0.005, R² = 99.40%, Predicted R² = 95.73%). The depth of cut is the most significant factor comparing the other variables that positively contribute to temperature. Furthermore, significant two-factor interactions and a quadratic effect for depth of cut were identified. Contour and surface plots are utilized to visualize the complex relationships between factors. The derived model demonstrates excellent predictive capability and is deemed effective for process optimization and control within the defined design space.</p>

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Mathematical model development for cutting temperature of AMS 5659- 15-5 PH stainless steel in milling operation using response surface methodology

  • 期刊论文

  • 发表日期：

    2026年3月21日

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  • P. V. Abhilash kumar,
  • K. Leo Dev Wins,
  • D Philip Selvaraj

摘要

This paper presents a comprehensive analysis of milling operation of 15 − 5 Precipitation hardened stainless steel using Response Surface Methodology. The objective was to develop a robust empirical model relating critical process parameters namely spindle speed, feed, and depth of cut to a key response variable, cutting temperature. A designed experiment was executed, and the data were analysed to fit a quadratic model. The analysis of variance confirmed the high significance of the model (p-value < 0.005, R² = 99.40%, Predicted R² = 95.73%). The depth of cut is the most significant factor comparing the other variables that positively contribute to temperature. Furthermore, significant two-factor interactions and a quadratic effect for depth of cut were identified. Contour and surface plots are utilized to visualize the complex relationships between factors. The derived model demonstrates excellent predictive capability and is deemed effective for process optimization and control within the defined design space.