Modeling and optimization of cutting process parameters in face milling of EN 31 alloy steel using nanoparticle fluids

With the progression in technologies, conventional cutting of metal with flood cooling is superseded by some other means, viz. minimum quantity lubrication (MQL). Here, in this current work, we present that hexagonal boron nitride (hBN) nano-fluid particles are used in terms of percentage mixed in SAFCO lubricant oil. The factors, viz. cutting velocity, depth and percentage of nanoparticles are varied to obtain the mathematical models and optimisation of responses, viz. temperature and surface roughness. Desirability function analysis (DFA) approach is adopted for the optimisation process, and regression analysis is executed for the mathematical modelling. Also, criteria importance through inter criteria (CRITIC) approach was implemented to evaluate the weightage of responses first before the DFA application. The analysis of variance reveals that all the factors taken into the existent are significantly affecting the outcomes, and nanoparticles percentage being the most one. The percentage increase of nanoparticles in oil leads to better surface conditions, as depicted by the result. The ideal settings for the optimisation of responses undertaken may be expressed as follows: 600 rpm cutting speed, 0.1 mm of depth and 2% nanoparticle percentage in MQL. The mathematical modelling confirms that first-order linear models are sufficient for both the responses under the given set of design of experiments.