Machining of hardened work materials with appropriate levels of process parameters is still a burning issue in manufacturing sectors and challenging. It is because of pressing demand of surface quality which adversely affected by evolution of tool wear. Therefore, the present investigation is undertaken to make a decision on parametric optimization of multi-responses such as flank wear and surface roughness during machining hardened AISI 52100 steel (55±1) steel using mixed ceramic insert under dry environment through grey relational analysis combined with Taguchi approach. Also predicted mathematical models of 1st and 2nd order have been developed for responses and checked for its accuracy. Second order mathematical model presented higher R 2 value and represents best fit of the model and adequate compared to first order model. Model indicates good correlations between the experimental and predicted results. The proposed grey-based Taguchi methodology has been proved to be efficient for solving multi-attribute decision making problem as a case study in hard machining environment.

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Sustainability is a vital issue for present and future generation, and it aims to obtain overall efficiency in terms of economic, environmental and social aspects. Inconel 825 belongs to the family of nickel-based super alloy and is widely used in the chemical and marine industries. This work attempts to investigate machining performance of Inconel 825 using physical vapor deposition-titanium nitrate inserts, with a focus on sustainable machining. The effect of cutting parameters, viz. cutting speed (v), feed (f) and depth of cut (d) on three aspects of sustainability has been explored in two different machining environments, viz. dry and minimum quantity lubrication (MQL). The experimental results show a significant improvement in MQL machining and tool wear, and cutting power is reduced by 16.57 and 8.47%, respectively, and surface roughness is improved by 10.41%. The interacting effects of parameters on responses are studied using 3-D surface plots; it shows cutting speed and feed are found as dominating parameters on all the three responses. The novelty of this work is to optimize the process for the sustainable production of components by optimizing the process parameters with multiple and conflicting objectives. The sustainable optimization using genetic algorithm provides surface roughness (R a) as 0.49 lm, tool flank wear (VB) as 110.68 lm and cutting power (P) as 5.44 kW with better convergent capability having 4% deviation. For the application of manufacturing industry, an optimization table is generated for selection of optimum process parameters for achieving desired surface roughness with minimum VB or minimum P.

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