Е-Публикации
Технически университет - София

Abstract: The aim of most efforts in these machining processes is to establish the optimal parameters to obtain the maximum material removal rate with minimum surface roughness which represents two of the main quality responses. This paper focuses on the optimization of process parameters in dry turning of Inconel 718, a nickel-based superalloy with PVD-coated carbide inserts based on single-objective optimization Taguchi technique, desirability function approach combined with response surface methodology (RSM), which is known as the multi-objective Desirability Optimization Methodology (DOM). Taguchi's orthogonal-array design L9 (33) and ANOVA analysis of variance are used to study the relationship between cutting parameters (cutting speed, feed rate and depth of cut) and the dependent output variables i.e., the arithmetic mean deviation of the profile's surface roughness (Ra) and material removal rate (MRR). A regression analysis was used to develop a mathematical model based on the first-order model to predict the Ra and MRR model. Using multiple regression analysis, first order linear prediction model was obtained to find the correlation between surface roughness and MRR with independent variables. In the range of parameters investigated, the obtained mathematical models accurately represent the response index, and the results of the experiments demonstrate that the feed rate and the depth of cut are the most important factors influencing Ra and MRR, respectively. Finally, confirmatory tests proved that Taguchi's method, desirability function approach combined with linear regression models was successful in optimizing turning parameters for minimum surface roughness and maximum MRR.

References

1. A.H. Suhail, N.I., Optimization of cutting parameters based on surface roughness and assistance of workpiece surface temperature in turning process. Am. J. Eng. Appl. Sci., 3, 2010.
2. Andrea De Bartolomeis, S.T., Future research directions in the machining of Inconel 718. J. Mater. Process. Technol., 2021.
3. Andrea la Monaca, J.W.-L., Surface integrity in metal machining - Part II: functional performance. Int. J. Mach. Tools Manuf., 2021.
4. Bandyopadhyay, S., Saha, S., Some Single- and Multiobjective Optimization Techniques. Unsupervised Classification, 2013, Springer, Berlin, Heidelberg.
5. C, H. (2021). Taguchi Method as a Robust Design Tool.
6. Chakradhar, P.S., Influence of cryogenic coolant on turning performance characteristics: a comparison with wet machining. Mater. Manuf. Process., 2017.
7. D'Addonaa DM, R.S., High speed machining of Inconel 718: tool wear and surface roughness analysis. Procedia CIRP, 2017.
8. Davide Chicco, M.J., The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation. PeerJ Comput. Sci., 2021.
9. Davis, R., 2018. Application of Taguchi-Based Design of Experiments for Industrial Chemical Processes.
10. Derringer, G., Suich, R., Simultaneous optimization of several response variables. J. Qual. Technol. 12 (1980), 214–219.
11. Franko Puh, Z.J. (2016). Optimizacija parametara obrade tokarenja s više kriterija kvalitete uporabom Grey relacijske analize Tehnički vjesnik 23. Slavonski Brod: Strojarski fakultet; Osijek: Fakultet elektrotehnike računarstva i informacijskih tehnologija; Građevinski i arhitektonski fakultet.
12. Hassan, K., Experimental investigation of material removal rate in CNC turning using Taguchi method. Int. J. Eng. Res. Appl., 2012.
13. J. Francis Xavier, B.R., Experimental study on surface roughness and flank wear in turning of Nimonic C263 under Dry Cutting Conditions. Hindawi J. Nanomater., 2021.
14. Klocke, F.K., Cutting Tool Materials and Tools. Manufacturing Processes 1. RWTHedition, 2011, Springer, Berlin, Heidelberg.
15. Krishnaiah K, a S., 2012. Applied Design of Experiments and Taguchi Methods. PHI Learning Private Limited, New Delhi.
16. Kuang-Hua Chang, 2015. Multiobjective Optimization and Advanced Topics in e-Design, e-Design, Computer-Aided Engineering Design, pp. 1105–1173.
17. Kubilay Aslantas, M.D., 2020. Investigations on Surface Roughness and Tool Wear Characteristics in Micro-Turning of Ti-6Al-4V Alloy. Materials (Basel).
18. M. Mia, A.K., High-pressure coolant on flank and rake surfaces of tool in turning of Ti-6Al-4V: investigations on surface roughness and tool wear. Int. J. Adv. Manuf. Technol., 2017.
19. M. Sarikaya, A.G., Taguchi design and response surface methodology based analysis of machining parameters in CNC turning under MQL. J. Clean. Prod., 2016.
20. MS, M.C., Statistical analysis: sample size and power estimations. BJA Educ., 2016.
21. Phadke, M.S., Madhav S Phadke. Quality Engineering Using Robust Design, 1989, Prentice Hall.
22. Qazi, M.I., Experimental investigation and multi-response optimization of machinability of AA5005H34 using composite desirability coupled with PCA. Metals, 11, 2021, 235.
23. Rajit, K., Roy, C.S., Design of Experiments Using The Taguchi Approach 16 Steps to Product and Process Improvement. 2022, John Wiley & Sons.
24. Raut, W.K., optimization of end milling process parameters for minimization of surface roughness of AISI P20 steel. Asian J. Sci. Technol. Vol.07:Issue 04 (2016), 2777–2787.
25. Raykar, S.J., Machining performance analysis of turning of Inconel 718 under different coolant environment. Indian J. Eng. Mater. Sci., 2022.
26. Řehoř, J.F., ANOVA analysis for estimating the accuracy and surface roughness of precisely drilled holes of steel 42CrMo4 QT. Int J. Adv. Manuf. Technol., 2023.
27. Sap, E., Usca, Ü., Gupta, M., Kunto ˘glu, M., Sarıkaya, M., Pimenov, D., Mia, M., Parametric optimization for improving the machining process of Cu/Mo-SiCP composites produced by powder metallurgy. Materials, 2021.
28. Sarikaya M, G.M., Taguchi design and response surface methodology based analysis of machining parameters in CNC turning under MQL. J. Clean. Prod., 2014.
29. Serope Kalpakjian, S.S., 2009. Manufacturing Engineering & Technology 7th Edition.
30. Slawomir Swirada, R.W., Determining the effect of ball burnishing parameters on surface roughness using the Taguchi method. Procedia Manuf., 2019.
31. Wassila Frifita, S.B., Optimization of machining parameters in turning of Inconel 718 Nickel-base super alloy. Mech. Ind. 21, 203(2020), 2020 (© AFM, EDP Sciences).

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