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<p>This study bridges the gap between Corporate Social Responsibility (CSR) principles and machining optimization, focusing on CNC turning operations for AISI 304 stainless steel. As industries increasingly prioritize sustainability and responsible production, this research aims to optimize machining parameters cutting speed, depth of cut, feed rate, and lubricant quantity to enhance operational efficiency and environmental stewardship. The optimization targets improved production metrics and key CSR factors, such as reducing energy consumption, minimizing material waste, and promoting worker safety. The integration of CSR into machining is achieved by aligning environmental impact metrics (e.g., energy consumption, tool wear, and waste reduction) with performance indicators like Material Removal Rate (MRR) and surface roughness (Ra). By optimizing these parameters using the Taguchi method, the study demonstrates how sustainable machining can simultaneously lower environmental footprints and enhance production outcomes. The Adaptive Neuro-Fuzzy Inference System (ANFIS) model was used to predict machining performance, with minimal deviation between predicted and experimental results, confirming the model’s accuracy. The optimal settings obtained through single-objective optimization are: MRR- cutting speed: 45&#xa0;m/min, depth of cut: 0.4&#xa0;mm, feed rate: 0.65&#xa0;mm/rev, lubricant quantity: 0.6&#xa0;ml/sec; Surface Roughness (Ra)- cutting speed: 65&#xa0;m/min, depth of cut: 0.6&#xa0;mm, feed rate: 0.35&#xa0;mm/rev, lubricant quantity: 1&#xa0;ml/sec; Cutting Force (CF)- cutting speed: 35&#xa0;m/min, depth of cut: 0.4&#xa0;mm, feed rate: 0.20&#xa0;mm/rev, lubricant quantity: 1&#xa0;ml/sec. This study presents a novel approach to integrating CSR-driven sustainability into machining processes, offering a framework where operational efficiency and social responsibility reinforce each other in modern manufacturing environments.</p>

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Experimental investigation of turning parameters of AISI 304 stainless steel optimized using Taguchi and ANFIS models based on CSR

  • 期刊论文

  • 发表日期：

    2026年3月21日

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  • Chandrakumarmangalam Subramaniam ,
  • Mohankumar Velusamy,
  • Soorya Prakash Kumarasamy,
  • Sivasubramanian Palanismy,
  • Aravindhan Alagarsamy,
  • Saleh A. Alfarraj,
  • Mezigebu Belay,
  • Mohamed Abbas,
  • Shaeen Kalathil,
  • Sulaiman Ali Alharbi

摘要

This study bridges the gap between Corporate Social Responsibility (CSR) principles and machining optimization, focusing on CNC turning operations for AISI 304 stainless steel. As industries increasingly prioritize sustainability and responsible production, this research aims to optimize machining parameters cutting speed, depth of cut, feed rate, and lubricant quantity to enhance operational efficiency and environmental stewardship. The optimization targets improved production metrics and key CSR factors, such as reducing energy consumption, minimizing material waste, and promoting worker safety. The integration of CSR into machining is achieved by aligning environmental impact metrics (e.g., energy consumption, tool wear, and waste reduction) with performance indicators like Material Removal Rate (MRR) and surface roughness (Ra). By optimizing these parameters using the Taguchi method, the study demonstrates how sustainable machining can simultaneously lower environmental footprints and enhance production outcomes. The Adaptive Neuro-Fuzzy Inference System (ANFIS) model was used to predict machining performance, with minimal deviation between predicted and experimental results, confirming the model’s accuracy. The optimal settings obtained through single-objective optimization are: MRR- cutting speed: 45 m/min, depth of cut: 0.4 mm, feed rate: 0.65 mm/rev, lubricant quantity: 0.6 ml/sec; Surface Roughness (Ra)- cutting speed: 65 m/min, depth of cut: 0.6 mm, feed rate: 0.35 mm/rev, lubricant quantity: 1 ml/sec; Cutting Force (CF)- cutting speed: 35 m/min, depth of cut: 0.4 mm, feed rate: 0.20 mm/rev, lubricant quantity: 1 ml/sec. This study presents a novel approach to integrating CSR-driven sustainability into machining processes, offering a framework where operational efficiency and social responsibility reinforce each other in modern manufacturing environments.