<p>The machining industry has seen a significant shift towards sustainable practices, with green cutting fluids (GCF) emerging as a key component in sustainable machining. This study explores the synergistic effects of GCFs formulated with coconut oil as base oil, garlic oil and almond oil as extreme-pressure/anti-wear and anti-corrosion additives, and cocamidopropylbetaine (CAPB) as emulsifying medium. AISI 1045 steel was machined under minimum quantity lubrication (MQL) using three formulated GCFs through the L<sub>9</sub> orthogonal array. Taguchi-based grey relational analysis (GRA) and regression modeling were utilized to assess the influence of machining parameters on surface roughness, peak cutting force, and peak cutting temperature. The results demonstrated that GCF significantly improved machining performance and resulted in reduced tool wear. B03 emerged as the best performing formulation with a grey relational grade (GRG) of 0.6407. Surface roughness, peak cutting force, and peak cutting temperature were notably reduced by 50.47%, 22.07%, and 54.89%, respectively. Optimum machining parameters were identified by GRA at spindle speed of 410 rpm, 0.75 mm depth of cut, and 0.1 mm/rev feed rate. Depth of cut <InlineEquation ID="IEq1"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="170_2025_16096_Article_IEq1.gif" Format="GIF" Height="19" Rendition="HTML" Resolution="72" Type="Linedraw" Width="23" /> </InlineMediaObject> <EquationSource Format="TEX">$(d)$</EquationSource> <EquationSource Format="MATHML"><math> <mrow> <mo stretchy="false">(</mo> <mi>d</mi> <mo stretchy="false">)</mo> </mrow> </math></EquationSource> </InlineEquation> and feed rate (<InlineEquation ID="IEq2"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="170_2025_16096_Article_IEq2.gif" Format="GIF" Height="17" Rendition="HTML" Resolution="72" Type="Linedraw" Width="14" /> </InlineMediaObject> <EquationSource Format="TEX">$f$</EquationSource> <EquationSource Format="MATHML"><math> <mi>f</mi> </math></EquationSource> </InlineEquation>) significantly affect response variables, with their interaction (<InlineEquation ID="IEq3"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="170_2025_16096_Article_IEq3.gif" Format="GIF" Height="17" Rendition="HTML" Resolution="72" Type="Linedraw" Width="19" /> </InlineMediaObject> <EquationSource Format="TEX">$df$</EquationSource> <EquationSource Format="MATHML"><math> <mrow> <mi mathvariant="italic">df</mi> </mrow> </math></EquationSource> </InlineEquation>) being crucial for predicting outcomes. High determination coefficients (<InlineEquation ID="IEq4"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="170_2025_16096_Article_IEq4.gif" Format="GIF" Height="16" Rendition="HTML" Resolution="72" Type="Linedraw" Width="21" /> </InlineMediaObject> <EquationSource Format="TEX">${R}^{2}$</EquationSource> <EquationSource Format="MATHML"><math> <msup> <mrow> <mi>R</mi> </mrow> <mn>2</mn> </msup> </math></EquationSource> </InlineEquation>) of second-order regression models for response variables validate their predictive capability. Proposed GCF formulations emphasize the potential of sustainable lubricants in modern machining applications with significant improvements in machining performance.</p>

Performance assessment of bio-enhanced coconut oil-based green cutting fluid in AISI 1045 turning: Taguchi-grey relational and regression analysis

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发表日期：

2025年7月17日

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Umair Ashraf,
Sheikh Shahid Saleem

摘要

The machining industry has seen a significant shift towards sustainable practices, with green cutting fluids (GCF) emerging as a key component in sustainable machining. This study explores the synergistic effects of GCFs formulated with coconut oil as base oil, garlic oil and almond oil as extreme-pressure/anti-wear and anti-corrosion additives, and cocamidopropylbetaine (CAPB) as emulsifying medium. AISI 1045 steel was machined under minimum quantity lubrication (MQL) using three formulated GCFs through the L₉ orthogonal array. Taguchi-based grey relational analysis (GRA) and regression modeling were utilized to assess the influence of machining parameters on surface roughness, peak cutting force, and peak cutting temperature. The results demonstrated that GCF significantly improved machining performance and resulted in reduced tool wear. B03 emerged as the best performing formulation with a grey relational grade (GRG) of 0.6407. Surface roughness, peak cutting force, and peak cutting temperature were notably reduced by 50.47%, 22.07%, and 54.89%, respectively. Optimum machining parameters were identified by GRA at spindle speed of 410 rpm, 0.75 mm depth of cut, and 0.1 mm/rev feed rate. Depth of cut $(d)$ $(d)$ and feed rate ( $f$ $f$ ) significantly affect response variables, with their interaction ( $df$ $df$ ) being crucial for predicting outcomes. High determination coefficients ( ${R}^{2}$ $R^{2}$ ) of second-order regression models for response variables validate their predictive capability. Proposed GCF formulations emphasize the potential of sustainable lubricants in modern machining applications with significant improvements in machining performance.