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Experimental study of hardness effects on surface roughness for nanofluid minimum quantity lubrication (NanoMQL) technique using Jaya algorithm

In this paper, the performance of NanoMQL technique in terms of surface roughness was evaluated for hard and soft EN31 steel. The Experiments were conducted by response surface methodology (RSM) using statistical software to develop regression model of surface roughness and optimization was carried out using Jaya algorithm. | Experimental study of hardness effects on surface roughness for nanofluid minimum quantity lubrication NanoMQL technique using Jaya algorithm International Journal of Data and Network Science 2 2018 71 78 Contents lists available at GrowingScience International Journal of Data and Network Science homepage www.GrowingScience.com ijds Experimental study of hardness effects on surface roughness for nanofluid minimum quantity lubrication NanoMQL technique using Jaya algorithm Rahul R. Chakulea and Sharad S. Chaudharia a Department of Mechanical Engineering Yeshwantrao Chavan College of Engineering Nagpur 441110 India CHRONICLE ABSTRACT Article history The NanoMQL technique is used to overcome the limitations of wet grinding due to economic Received May 1 2018 and ecological problems. The performance measure is largely influenced by the process parame- Received in revised format June ters such as table speed depth of cut air pressure coolant flow rate and nanofluid concentration. 16 2018 In this paper the performance of NanoMQL technique in terms of surface roughness was evalu- Accepted August 18 2018 Available online ated for hard and soft EN31 steel. The Experiments were conducted by response surface method- August 18 2018 ology RSM using statistical software to develop regression model of surface roughness and op- Keywords timization was carried out using Jaya algorithm. The result shows that lowest value of surface Grinding roughness was obtained for NanoMQL of hard steel in comparison with soft steel under grinding Jaya algorithm environments such as wet MQL and NanoMQL. Hence to improve the performance of soft steel Modeling the modeling and optimization of surface roughness were carried out. The significant parameters NanoMQL were considered for model development and validity of model determined through ANOVA Surface roughness Analysis of variance . Lastly the optimal values were determined using Jaya algorithm for min- imum surface roughness and the percentage