Machining performance analysis and optimization in the milling of mold steel under MQL with nanofluid

Abstract

The presented study investigates the milling performance of DIN-1.2738 steel under various cutting speeds, feeds, dry, minimum quantity lubrication (MQL) and nanographene-reinforced nanofluid-assisted MQL (N-MQL) cutting conditions. The results of cutting temperature, cutting force, feed force and surface roughness were obtained using a full-factorial experimental design. Under the N-MQL cutting conditions, the cutting temperature, cutting force, feed force and surface roughness improved by 30.1%, 22.3%, 26.3% and 40.2%, respectively. The most effective parameters for cutting temperature, feed force and surface roughness turned out to be the cooling conditions, with 81.6%, 41.7% and 72% contribution ratios, respectively. Also, feed had the strongest effect on cutting force, with a 44.7% contribution ratio. Using different weight ratios, the Gray Wolf algorithm optimized the milling parameters and cooling conditions for output parameters. The optimization process used five scenarios, weight-prioritizing each output parameter and incorporating the entropy method. The optimum cutting condition and feed were 1% Graphene N-MQL and 0.04 mm/rev across all scenarios. The optimal cutting speeds varied based on different priorities.