| Abstract: |
Industrial finishing of high-strength aluminum alloys is a multi-objective task by nature, with surface roughness, microhardness, dimensional accuracy and energy consumption required to be handled simultaneously, but most burnishing optimization studies published in literature tackle these responses one at a time. This study treats four conflicting responses (numerically, surface roughness Rₐ, Vickers microhardness HV, roundness error, specific burnishing energy) from roller burnishing of Al7075-T6 as a true multi-objective optimization problem (MOP) and determines the Pareto-optimal parameter combination using grey relational analysis (GRA) fused with the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). Five control factors (spindle speed, feed rate, burnishing force, number of passes, and type of lubricant—mineral oil, Al₂O₃ nanofluid, CuO nanofluid, TiO₂ nanofluid, and a 1.0 wt. % Al₂O₃–CuO hybrid nanofluid) were varied at four levels using a Taguchi L₁₆ mixed-level array. Sixteen experiments were conducted in duplicates; the GRA rank is assessed for each grey relational grade and then is validated by applying TOPSIS, independently. The Pareto-optimal input factor combination run 11 corresponding to spindle speed 500 rpm, feed rate 0.05 mm/rev, acting load 200 N, number of pass 3 and nanofluid Al₂O₃–CuO hybrid nanofluid was identified with grey relational grade 0.802 and TOPSIS closeness coefficient 0.847. The dominant control factors were determined by ANOVA and were lubricant type (29.6 %) and feed rate (24.8 %) for the GRG. The results from the confirmation experiment yielded Rₐ = 0.62 μm, HV = 156, roundness error = 14 μm, and specific energy = 0.78 kJ/cm² all of which simultaneously exceed each of the respective single-objective optima reported in parallel literature. These consequences set up a coupled GRA–TOPSIS framework as a trustworthy and low-experiment avenue for multi-objective burnishing optimization of aluminum aerospace all |
