Multi-objective optimization of hard milling process of AISI H13 in terms of productivity, quality, and cutting energy under nanofluid minimum quantity lubrication condition

Abstract

This paper presents the multi-objective optimization of the hard milling process of AISI H13 steel under minimum quality lubricant with graphite nanoparticle. The cutting speed, feed per tooth, depth of cut, and hardness of workpiece were taken as the process parameters, while surface roughness, cutting energy, cutting temperature, and material removal rate were considered as technological responses. Response surface or Kriging approximate models were applied to generate the mathematical regression models showing the relationship between machining inputs and outputs obtained by physical experiments. Then, multi-objective particle swarm optimization algorithm in conjunction with the Pareto approach and engineering data mining was adopted to figure out the feasible solutions. The research results show that cutting energy can be reduced up to around 14% compared to the worst case. Based on the Pareto plot, the appropriate selection of machining parameters can help the machine tool operator to increase machining productivity and energy efficiency.

Strenx 1100 Çeliğinin MMY Yardımıyla Sert Frezelenmesinde Enerji Tüketimi Üzerine Çalışma

“…This effect can be explained with the increasing material removal rate. Higher depth of cut and cutting speed values produces more cutting power, thereby higher cutting energy consumed (Vu et al, 2020). Also, the effect of depth of cut and cutting speed on energy consumption can be observed in hard milling of the Ti6Al4V alloy under MQL conditions (Jamil et al, 2021).…”

“…According to the paper from Jamil et al when comparing the energy efficiency of different cooling/lubricating conditions, MQL showed its superiority both down and up milling of Ti6Al4V (Jamil et al, 2021). Several parameters considered were tried by a researcher team (Vu et al, 2020) who performed experiments under MQL conditions for AISI H13 steel using cutting parameters and workpiece hardness. Khan et al optimized energy consumption during small quantity cooling condition of milling process for AISI 1045 steel (Khan et al, 2019).…”

Modeling and multiple optimization in face milling of hardfacing welding applied steel: Force, roughness, power

“…Hence, it is superior to optimize all responses simultaneously for maximum efficiency in multiple response processes since each response is critical at a certain level. In this context, response surface methodology, desirability function approach, 31 genetic algorithm, 32 particle swarm optimization, 33 grey relational analysis (GRA), or hybrid optimization algorithms are used. 34 However, Taguchi-based GRA method is the most widely used multiple response optimization in practice due to being proficient, flexible, and favorable.…”

Experimental Investigation on Nano Oil Added Fluid Influence for the Machining of Hardened AISI H13 Hot Work Tool Steel

“…The study in [19] presented an experimental investigation on the effects of rounded cutting-edge radius and machining parameters on surface roughness and tool wear in milling of AISI H13 tool steel (52HRC) in dry and cryogenic machining. Hard milling on AISI H13 steel in terms of productivity, quality, and cutting energy under nanofluid MQL condition was investigated in [20]. [21] focused on the optimization of drilling parameters using the Taguchi technique to obtain minimum surface roughness (Ra) and thrust force (Ff).…”