Multi-response optimization on the effect of wet and eco-friendly cryogenic turning of D2 steel using Taguchi-based grey relational analysis

Material removal processes, including turning and milling, are still commonly used operations for manufacturing most of mechanical components in modern industry. Apart from the cutting parameters, the cooling method has the great impact on the technological effects and, above all, on the environmental friendliness of production. In this study, multi-response optimization on the effect of wet and cryogenic machining is performed during the turning of AISI D2 steel samples. Spindle speeds, feed rates, depths of cut, and cutting fluid types varied in a Taguchi mixed design L16 orthogonal array. Statistics, such as an analysis of variance (ANOVA) and a regression model, were applied to the obtained data on the metal removal rate and surface roughness. By employing a grey relational analysis, the best cutting factors for a set of several responses were determined. Among the many factors influencing the rate at which material is removed, analysis of variance revealed that the feed rate was the most influential factor (46.67%), followed by spindle speed (46.65%). Analysis of the factors influencing surface roughness pointed to the feed, cutting condition, and spindle speed as the most essential at 56.66%, 26.04%, and 11.7%, respectively. ANOVA of grey relational analysis shows that speed followed by cutting conditions is the most predominant factor, with a percentage contribution of 71.9% and 14.14%, respectively. From grey relational analysis, a level setting of 4-4-1-2 was identified as the best possible combination of multi-response process parameters. A close agreement is observed between the predicted value of GRG 0.7927 and the experimental validation value of GRG 0.8031. Moreover, the validation test reveals that the percentage errors in estimating material removal rate, surface roughness, and GRG, respectively, are 4.33%, 9.09%, and 1.29%, from predicted values. A study on metallographic observations revealed that parts after wet machining have more tool marks on the treated surface than parts after cryogenic machining.

Automated summary

In this study, the effects of wet and cryogenic machining on turning of AISI D2 steel samples were investigated using multi-response optimization. The results showed that the feed rate had the greatest influence on metal removal rate (46.67%), followed by spindle speed (46.65%). Surface roughness was mainly affected by feed rate (56.66%), cutting condition (26.04%), and spindle speed (11.7%). The predicted values were in good agreement with the experimental validation values.