Scholarlink

<p>The GTD450 stainless steel is highly valued in aerospace and turbine applications for its exceptional mechanical and thermal properties; however, its low machinability and the scarcity of data on optimal cutting and lubrication conditions present a significant industrial challenge. While the environmentally friendly Minimum Quantity Lubrication (MQL) method is a promising candidate for machining this alloy, a comprehensive study on the application and optimization of its key parameters—specifically oil concentration and spray pressure, in conjunction with standard cutting variables—was absent. To address this research gap, this study employed a Taguchi L25 orthogonal array to experimentally investigate the simultaneous effects of these parameters. The results demonstrated that fluid concentration improved surface roughness by up to 12%, while MQL pressure reduced tool wear by up to 37.5%. Furthermore, multi-objective optimization using the Grey Relational Grade (GRG) method yielded a 38.8% improvement in the overall performance index, identifying the optimal parameter set as follows: 15% concentration, 900&#xa0;mm/min feed rate, 180&#xa0;m/min cutting speed, 1.5&#xa0;mm depth of cut, and 10&#xa0;bar pressure. These significant findings are now being successfully implemented in relevant manufacturing sectors.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Multi response optimization in MQL milling of GTD450 stainless steel using an integrated Taguchi grey relational analysis

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年12月15日

查看全文
  • Masoud Saberi,
  • Seyed Ali Niknam,
  • Ali Hajaliakbari,
  • Behnam Davoodi,
  • Ramin Hashemi

摘要

The GTD450 stainless steel is highly valued in aerospace and turbine applications for its exceptional mechanical and thermal properties; however, its low machinability and the scarcity of data on optimal cutting and lubrication conditions present a significant industrial challenge. While the environmentally friendly Minimum Quantity Lubrication (MQL) method is a promising candidate for machining this alloy, a comprehensive study on the application and optimization of its key parameters—specifically oil concentration and spray pressure, in conjunction with standard cutting variables—was absent. To address this research gap, this study employed a Taguchi L25 orthogonal array to experimentally investigate the simultaneous effects of these parameters. The results demonstrated that fluid concentration improved surface roughness by up to 12%, while MQL pressure reduced tool wear by up to 37.5%. Furthermore, multi-objective optimization using the Grey Relational Grade (GRG) method yielded a 38.8% improvement in the overall performance index, identifying the optimal parameter set as follows: 15% concentration, 900 mm/min feed rate, 180 m/min cutting speed, 1.5 mm depth of cut, and 10 bar pressure. These significant findings are now being successfully implemented in relevant manufacturing sectors.