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Performance evaluation of magnetic-field-assisted MQL turning of structural eco-composites: a hybrid optimization approach
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DOI：10.1080/10426914.2026.2674592.png)
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Abstract
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The machining performance of AA6082-T6 hybrid aluminum composites reinforced with 2 wt% Al₂O₃ and 3 wt% fly ash was investigated under dry, MQL, and magnetorheological MQL (MR-MQL) environments. An electromagnet-assisted tool system was developed to regulate nanolubricant behavior using an external magnetic field. A D-optimal experimental design with fuzzy logic was employed to analyze the influence of cutting parameters on cutting force, feed force, and surface roughness. Compared with dry machining, MR-MQL reduced cutting and feed forces by about 30–35%, while MQL achieved 18–20% reduction. Surface roughness improved by 45–55% under MR-MQL conditions. SEM, EDS, and optical profilometry analyses confirmed reduced adhesion, built-up edge formation, adhesive wear, and surface irregularities. The developed quadratic models showed excellent prediction capability with R² values close to 0.99 and validation errors below 10%.
Keywords:
Magnetic-field-assisted MQL
Hybrid aluminum composites
Cutting force
Surface roughness
Fuzzy logic optimization
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