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<p>Additive manufacturing (AM) of titanium alloys offers significant benefits, including design flexibility and material efficiency; however, its poor surface quality often necessitates subsequent machining. Traditional cooling techniques have shown limited effectiveness, particularly with challenging materials like Titanium grade 2 (Ti-grade 2). In this study, graphene quantum dots (GQDs) were prepared using citric acid and employed as the minimum quantity lubrication (MQL) fluid, which was compared with MQL and cryogenic liquid nitrogen (cryo-LN<sub>2</sub>) in the turning of Additive Manufactured-Titanium-grade 2 (AMed-Ti Grade 2). In addition, machine learning (ML) models, including multilayer perceptron (MLP), support vector machine (SVM), and random forest (RF), were utilized to predict machining responses [surface roughness (Ra), temperature (Tc), and flank wear (Vb)]. GQD–MQL demonstrated superior tool wear resistance by forming a stable tribo-film, minimizing Vb and crater wear. In contrast, MQL exhibited high wear due to inadequate lubrication, while cryo-LN<sub>2</sub> provided moderate control through effective cooling. However, in terms of Tc, cryo-LN<sub>2</sub> produced the lowest temperature in the cutting area when compared to other conditions. The ML models revealed that the MLP model accurately predicted Ra and Tc, whereas SVM outperformed the other models in predicting Vb.</p>

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Tribological and predictive performance of graphene quantum dots as lubricant in machining LMD processed Ti-grade 2

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年10月18日

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  • M. Naresh Babu,
  • V. Anandan,
  • M. Dinesh Babu,
  • N. L. Parthasarathi,
  • Ruby Thomas,
  • N S Ross

摘要

Additive manufacturing (AM) of titanium alloys offers significant benefits, including design flexibility and material efficiency; however, its poor surface quality often necessitates subsequent machining. Traditional cooling techniques have shown limited effectiveness, particularly with challenging materials like Titanium grade 2 (Ti-grade 2). In this study, graphene quantum dots (GQDs) were prepared using citric acid and employed as the minimum quantity lubrication (MQL) fluid, which was compared with MQL and cryogenic liquid nitrogen (cryo-LN2) in the turning of Additive Manufactured-Titanium-grade 2 (AMed-Ti Grade 2). In addition, machine learning (ML) models, including multilayer perceptron (MLP), support vector machine (SVM), and random forest (RF), were utilized to predict machining responses [surface roughness (Ra), temperature (Tc), and flank wear (Vb)]. GQD–MQL demonstrated superior tool wear resistance by forming a stable tribo-film, minimizing Vb and crater wear. In contrast, MQL exhibited high wear due to inadequate lubrication, while cryo-LN2 provided moderate control through effective cooling. However, in terms of Tc, cryo-LN2 produced the lowest temperature in the cutting area when compared to other conditions. The ML models revealed that the MLP model accurately predicted Ra and Tc, whereas SVM outperformed the other models in predicting Vb.