Scholarlink

<p>Machining plays a vital role in manufacturing high-precision components for aerospace, biomedical, automotive, and energy industries. Effective control of cutting temperature is essential to ensure dimensional accuracy, surface integrity, and tool longevity, particularly when machining advanced alloys such as Ti-6Al-4&#xa0;V, which are widely used due to their superior mechanical properties. Excessive heat generation during machining leads to accelerated tool wear, reduced surface quality, dimensional inaccuracies, and increased production costs. This issue is especially critical for Ti-6Al-4&#xa0;V, a titanium alloy characterized by poor thermal conductivity and high cutting temperatures, making it difficult to machine efficiently. This study aims to experimentally measure tool temperature during CNC turning of Ti-6Al-4&#xa0;V and to predict the tool–chip interface temperature using finite element analysis (FEA), thereby developing a reliable predictive model for temperature control and process optimization. Tool temperature was experimentally measured using a specially designed thermocouple during CNC turning operations. Finite element simulations were conducted using DEFORM 3D to predict tool–chip interface temperature, and the results were validated through transient thermal analysis in ANSYS. A Taguchi L27 orthogonal array was employed to evaluate the influence of cutting speed, feed, and depth of cut. Statistical analysis of variance (ANOVA) was performed to determine the contribution of each parameter, and a regression model was developed to predict tool temperature. ANOVA results revealed that feed rate is the most influential parameter affecting tool temperature, followed by cutting speed, while depth of cut has the least contribution. The developed regression model achieved a high coefficient of determination (R<sup>2</sup> = 0.90), indicating strong predictive capability. Finite element predictions showed good agreement with experimental results, with deviations generally within 12%, while confirmation experiments demonstrated prediction errors below 10%. The findings highlight the critical importance of feed rate control in minimizing tool temperature and demonstrate that integrating experimental measurements with validated FEA provides a robust framework for optimizing machining of Ti-6Al-4&#xa0;V and other difficult-to-cut aerospace alloys.</p> Graphical Abstract <p></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

Finite element analysis of cutting temperature in turning-aerospace alloy Ti6Al4V

  • 期刊论文

  • 发表日期：

    2026年4月11日

查看全文
  • Pralhad Bhaskar Patole

摘要

Machining plays a vital role in manufacturing high-precision components for aerospace, biomedical, automotive, and energy industries. Effective control of cutting temperature is essential to ensure dimensional accuracy, surface integrity, and tool longevity, particularly when machining advanced alloys such as Ti-6Al-4 V, which are widely used due to their superior mechanical properties. Excessive heat generation during machining leads to accelerated tool wear, reduced surface quality, dimensional inaccuracies, and increased production costs. This issue is especially critical for Ti-6Al-4 V, a titanium alloy characterized by poor thermal conductivity and high cutting temperatures, making it difficult to machine efficiently. This study aims to experimentally measure tool temperature during CNC turning of Ti-6Al-4 V and to predict the tool–chip interface temperature using finite element analysis (FEA), thereby developing a reliable predictive model for temperature control and process optimization. Tool temperature was experimentally measured using a specially designed thermocouple during CNC turning operations. Finite element simulations were conducted using DEFORM 3D to predict tool–chip interface temperature, and the results were validated through transient thermal analysis in ANSYS. A Taguchi L27 orthogonal array was employed to evaluate the influence of cutting speed, feed, and depth of cut. Statistical analysis of variance (ANOVA) was performed to determine the contribution of each parameter, and a regression model was developed to predict tool temperature. ANOVA results revealed that feed rate is the most influential parameter affecting tool temperature, followed by cutting speed, while depth of cut has the least contribution. The developed regression model achieved a high coefficient of determination (R2 = 0.90), indicating strong predictive capability. Finite element predictions showed good agreement with experimental results, with deviations generally within 12%, while confirmation experiments demonstrated prediction errors below 10%. The findings highlight the critical importance of feed rate control in minimizing tool temperature and demonstrate that integrating experimental measurements with validated FEA provides a robust framework for optimizing machining of Ti-6Al-4 V and other difficult-to-cut aerospace alloys.

Graphical Abstract