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<p>This study presents a comprehensive evaluation of sustainable turning of AISI 316L stainless steel using Minimum Quantity Lubrication (MQL) with an eco-friendly castor oil-based nanofluid containing 0.5 wt% Al₂O₃ nanoparticles with an average size of 30&#xa0;nm. The effects of cutting speed (164–370&#xa0;m/min), depth of cut (0.25–0.75&#xa0;mm), and lubrication method (dry, conventional soluble oil, and nanofluid) on surface roughness were analyzed using a Taguchi L9 orthogonal array. The optimal parameters 370&#xa0;m/min speed, 0.5&#xa0;mm depth, and nanofluid achieved a minimum surface roughness (Ra) of 0.532&#xa0;µm. ANOVA revealed coolant type as the most influential factor, contributing 60.57% to surface quality variance. Novelty lies in the integration of a biodegradable Al₂O₃-castor oil nanofluid with finite element modelling and its application to AISI 316L, a widely used biomedical alloy. DEFORM-3D simulations validated experimental trends, with predicted cutting forces and tool-chip interface temperatures deviating by less than 8% from measured values, confirming strong correlation. This dual approach underscores the nanofluid’s superior tribological performance and affirms its role as a green alternative for high-precision, low-carbon machining aligned with Industry 4.0 goals.</p>

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Performance evaluation of Al₂O₃-based castor oil nanofluid in MQL-assisted turning of AISI 316L stainless steel

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年9月30日

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  • Mithun Dhaval Shah,
  • Abhay Utpat

摘要

This study presents a comprehensive evaluation of sustainable turning of AISI 316L stainless steel using Minimum Quantity Lubrication (MQL) with an eco-friendly castor oil-based nanofluid containing 0.5 wt% Al₂O₃ nanoparticles with an average size of 30 nm. The effects of cutting speed (164–370 m/min), depth of cut (0.25–0.75 mm), and lubrication method (dry, conventional soluble oil, and nanofluid) on surface roughness were analyzed using a Taguchi L9 orthogonal array. The optimal parameters 370 m/min speed, 0.5 mm depth, and nanofluid achieved a minimum surface roughness (Ra) of 0.532 µm. ANOVA revealed coolant type as the most influential factor, contributing 60.57% to surface quality variance. Novelty lies in the integration of a biodegradable Al₂O₃-castor oil nanofluid with finite element modelling and its application to AISI 316L, a widely used biomedical alloy. DEFORM-3D simulations validated experimental trends, with predicted cutting forces and tool-chip interface temperatures deviating by less than 8% from measured values, confirming strong correlation. This dual approach underscores the nanofluid’s superior tribological performance and affirms its role as a green alternative for high-precision, low-carbon machining aligned with Industry 4.0 goals.