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Multi-Response Optimization of Surface Grinding Process Parameters of AISI 4140 Alloy Steel Using Response Surface Methodology and Desirability Function under Dry and Wet Conditions

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Department of Industrial and Production Engineering, Jashore University of Science and Technology, Jashore 7408, Bangladesh
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Department of Industrial and Production Engineering, Bangladesh University of Textile, Dhaka 1208, Bangladesh
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Department of Industrial and Production Engineering, Bangladesh University of Engineering and Technology, Dhaka 1000, Bangladesh
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Department of Chemical Engineering, COMSATS University Islamabad, Lahore 54000, Pakistan
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Department of Nanotechnology and Advanced Materials Engineering, Sejong University, Seoul 05006, Korea
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Authors to whom correspondence should be addressed.
Academic Editor: Joel Rech
Coatings 2022, 12(1), 104; https://doi.org/10.3390/coatings12010104
Received: 30 November 2021 / Revised: 4 January 2022 / Accepted: 7 January 2022 / Published: 17 January 2022
(This article belongs to the Special Issue Recent Advances in Bioactive Film Coatings)
The effect of four controllable input process parameters of AISI 4140 steel, cross-feed, workpiece velocity, wheel velocity, and the depth of cut were experimentally investigated under dry and wet conditions. Three responses, contact temperature, material removal rate (MRR), and machining cost during surface grinding of AISI 4140 steel, were considered. The process was optimized using a recently developed combined methodology based on response surface methodology (RSM) and desirability functional approach (DFA). RSM generated the models of the responses for prediction while DFA solved these multi-response optimization problems. The DFA approach employed an objective function known as the desirability function, which converts an estimated response into a scale-free value known as desirability. The optimum parameter was attained at the maximum overall desirability. An analysis of variance (ANOVA) was conducted to confirm the model adequacy. From the results of the study, for equal weights of responses, the corresponding optimal values of the input parameters cross-feed, workpiece velocity, the wheel or cutting velocity and the depth of cut were found to be 6 mm/pass, 12 m/min, 15 m/s, and 0.095 mm respectively in wet conditions. The corresponding predicted output responses were: 134.55 °C for the temperature, and 7.366 BDT (Taka, Currency of Bangladesh) for the total cost with an overall desirability of 0.844. Confirmation testing of optimized parameters, i.e., checking the validity of optimal set of predicted responses with the real experimental run were conducted, and it was found that the experimental value for temperature and total cost were 140.854 °C and 8.36 BDT, respectively, with an overall desirability of 0.863. Errors of the predicted value from the experimental value for equal weightage scheme were 4.47% for the temperature and 7.37% for the total cost. It was also found that if the temperature was prioritized, then the wet condition dominated the overall desirability, which was expected. However, if the cost was given high weightage, dry condition achieved the highest overall desirability. This can be attributed to the cutting in the wet condition which was more expensive due to the application of cutting fluid. The proposed model was found to be new and highly flexible in the sense that there was always an option at hand to focus on a particular response if needed. View Full-Text
Keywords: RSM; DFA; dry grinding; wet grinding; surface grinding RSM; DFA; dry grinding; wet grinding; surface grinding
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MDPI and ACS Style

Roy, R.; Ghosh, S.K.; Kaisar, T.I.; Ahmed, T.; Hossain, S.; Aslam, M.; Kaseem, M.; Rahman, M.M. Multi-Response Optimization of Surface Grinding Process Parameters of AISI 4140 Alloy Steel Using Response Surface Methodology and Desirability Function under Dry and Wet Conditions. Coatings 2022, 12, 104. https://doi.org/10.3390/coatings12010104

AMA Style

Roy R, Ghosh SK, Kaisar TI, Ahmed T, Hossain S, Aslam M, Kaseem M, Rahman MM. Multi-Response Optimization of Surface Grinding Process Parameters of AISI 4140 Alloy Steel Using Response Surface Methodology and Desirability Function under Dry and Wet Conditions. Coatings. 2022; 12(1):104. https://doi.org/10.3390/coatings12010104

Chicago/Turabian Style

Roy, Rakesh, Sourav K. Ghosh, Tanvir I. Kaisar, Tazim Ahmed, Shakhawat Hossain, Muhammad Aslam, Mosab Kaseem, and Md M. Rahman. 2022. "Multi-Response Optimization of Surface Grinding Process Parameters of AISI 4140 Alloy Steel Using Response Surface Methodology and Desirability Function under Dry and Wet Conditions" Coatings 12, no. 1: 104. https://doi.org/10.3390/coatings12010104

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