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<p>In response to growing concerns about environmental sustainability and the need to reduce carbon emissions in manufacturing, this study investigates the optimization of turning process parameters with a focus on minimizing energy consumption. The research develops a comprehensive mathematical model to identify the optimal cutting conditions, considering specific cutting energy, tool wear, and the application of minimum quantity lubrication (MQL) using eco-friendly canola oil. Experiments conducted on AISI 1080 steel using an uncoated carbide tool under varying machining conditions reveal that lubrication and tool wear significantly influence energy consumption. The results demonstrate that MQL not only reduces friction and cutting forces but also prolongs tool life, thereby lowering the specific cutting energy required for machining. By identifying the optimal combination of cutting speed, feed rate, and depth of cut, the study provides a pathway to achieving more sustainable machining practices. The findings highlight the potential for substantial energy savings in turning operations, contributing to the reduction of the carbon footprint associated with manufacturing processes.</p>

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Evaluation of optimum process parameters based on specific cutting energy in turning for sustainability

  • 期刊论文

  • 发表日期：

    2025年1月31日

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  • Rukmini Srikant Revuru,
  • Kethavath Tejasvi Sanjay,
  • Banoth Srinu,
  • Pasam Vamsi Krishna

摘要

In response to growing concerns about environmental sustainability and the need to reduce carbon emissions in manufacturing, this study investigates the optimization of turning process parameters with a focus on minimizing energy consumption. The research develops a comprehensive mathematical model to identify the optimal cutting conditions, considering specific cutting energy, tool wear, and the application of minimum quantity lubrication (MQL) using eco-friendly canola oil. Experiments conducted on AISI 1080 steel using an uncoated carbide tool under varying machining conditions reveal that lubrication and tool wear significantly influence energy consumption. The results demonstrate that MQL not only reduces friction and cutting forces but also prolongs tool life, thereby lowering the specific cutting energy required for machining. By identifying the optimal combination of cutting speed, feed rate, and depth of cut, the study provides a pathway to achieving more sustainable machining practices. The findings highlight the potential for substantial energy savings in turning operations, contributing to the reduction of the carbon footprint associated with manufacturing processes.