journal article·2023-07

Parametric Evaluation and Optimization of Laser Machining of SS304 Using Response Surface Methodology

Vikas B. Magdum, Jayant K. Kittur, Sachin C. Kulkarni

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Abstract

This study presents parametric evaluation and optimization of laser machining parameters of austenitic stainless steel 304 (SS 304) sheet. SS304 is a substantial manufacturing material machined by laser machining. The central composite design (CCD) of the response surface methodology (RSM) was utilized for the design of the experimental plan. Laser power, cutting speed, gas pressure and pulse frequency were used as process parameters, whereas kerf width and kerf taper were measured as performance characteristics. The factor effects on kerf width and kerf taper were analyzed using a surface plot. A good degree of fit was shown by the developed regression model. The analysis of variance results reveals that cutting speed, laser power and gas pressure had contributions of 41.49, 31.71 and 16.71% on kerf width, and 31.23, 26.03 and 19.81% on kerf taper. The optimal machining conditions of kerf width found at laser power of 1800 W, cutting speed of 1000 mm/min, gas pressure of 1.30 bar and a pulse frequency of 1500 Hz and of kerf taper at laser power of 1800 W, cutting speed of 802 mm/min, gas pressure of 1.30 bar and a pulse frequency of 1500 Hz. The higher cutting speed and gas pressure and lower laser power and pulse frequency result in the smallest kerf width, while an increase in cutting speed up to mid-level, higher gas pressure and lower laser power and pulse frequency result in the lower kerf taper. A mathematical model was developed to govern the relationship between the process parameters and the kerf width and kerf taper. The kerf width and kerf taper during the laser machining procedure are optimized efficiently through this method. It is clearly revealed that the performance characteristics of the laser machining process can be optimized efficiently by this method.
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Research context

OpenAlex topics: Laser Material Processing Techniques · Advanced Machining and Optimization Techniques · Advanced machining processes and optimization

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References (19)

Show source-provided references
1. H.A. Eltawahni, M. Hagino, K.Y. Benyounis, T. Inoue, and A.G. Olabi, Effect of CO2 Laser Cutting Process Parameters on Edge Quality and Operating Cost of AISI316L, Opt. Laser Technol., 2012, 44, p 1068–1082.10.1016/j.optlastec.2011.10.008 ↗
2. R. Surebana, V.N. Kulkarni, and V.N. Gaitonde, Modern Optimization Techniques for Advanced Machining Processes–A Review, Mater. Today: Proc., 2019, 18, p 3034–3042.10.1016/j.matpr.2019.07.175 ↗
3. E. Haddadi and M. Moradi, Ayub Karimzad Ghavidel, Ali Karimzad Ghavidel, Saleh Meiabadi, Experimental and Parametric Evaluation of Cut Quality Characteristics in CO2 Laser Cutting of Polystyrene, Optik, 2021, 184, p 103–114.10.1016/j.ijleo.2019.03.040 ↗
4. M. Moradi and H. Abdollahi, Statistical Modelling and Optimization of the Laser Percussion Microdrilling of Thin Sheet stainless Steel, Lasers Eng., 2018, 40(4–6), p 375–393.
5. D.J. Kotadiya and D.H. Pandya, Parametric Analysis of Laser Machining with Response Surface Method on SS-304, Procedia Technol., 2016, 23, p 376–382.10.1016/j.protcy.2016.03.040 ↗
6. M. Sarıkaya and V. Yılmaz, Optimization and Predictive Modeling Using S/N, RSM, RA and ANNs for Micro-Electrical Discharge Drilling of AISI 304 Stainless Steel Springer, Neural Comput. Appl., 2018, 30, p 1503–1517.10.1007/s00521-016-2775-9 ↗
7. D.F. Pessoa, P. Herwig, A. Wetzig, and M. Zimmermann, Influence of Surface Condition due to Laser Beam Cutting on the Fatigue Behavior of Metastable Austenitic Stainless Steel AISI 304 Elsevier, Eng. Fract. Mech., 2017, 185, p 227–240.10.1016/j.engfracmech.2017.05.040 ↗
8. M. Madic and M. Radovanovic, Application of RCGA-ANN Approach for Modeling Kw and Surface Roughness in CO2 Laser Cutting of Mild Steel, J. Braz. Soc. Mech. Sci. Eng., 2013, 35, p 103–110.10.1007/s40430-013-0008-z ↗
9. Avanish Kumar Dubey, Vinod Yadava, Multi-Objective Optimization of Nd:YAG Laser Cutting of Nickel-Based Superalloy Sheet Using Orthogonal Array with Principal Component Analysis, Elsevier, Opt. Lasers Eng., 2008, 46, p 124–132.10.1016/j.optlaseng.2007.08.011 ↗
10. Arun Kumar Pandey, Avanish Kumar Dubey, “Multiple Quality Optimization in Laser Cutting of Difficult-to-Laser-cut Material Using Grey-Fuzzy Methodology,” Int. J. Adv. Manuf. Technol., 2013, 65, p 421–431.10.1007/s00170-012-4181-5 ↗
11. D. Kondayya and A. Gopala Krishna, An Integrated Evolutionary Approach for Modelling and Optimization of Laser Beam Cutting Process Springer, Int. J. Adv. Manuf. Technol., 2013, 65, p 259–274.10.1007/s00170-012-4165-5 ↗
12. K.K. Mandal, A.S. Kuar, and S. Mitra, Experimental Investigation on Laser Micro-Machining of Al 7075 Al, Opt. Laser Technol., 2018, 107, p 260–267.10.1016/j.optlastec.2018.05.017 ↗
13. Bekir Sami Yilbas, Mian Mobeen Shaukat, Farhan Ashraf, “Laser Cutting of Various Materials: Kw size Analysis and Life Cycle Assessment of Cutting Process”, Elsevier, Opt. Laser Technol., 2017, 93, p 67–73.10.1016/j.optlastec.2017.02.014 ↗
14. T. Kongcharoen and M. Suchatawat, Effects of the Laser Process Parameters on Kerf Quality, Int. J. Mech. Eng. Robot. Res., 2018, 7(2), p 164–168.10.18178/ijmerr.7.2.164-168 ↗
15. A. Ghosal and A. Manna, Response surface Method Based Optimization of Ytterbium Fiber Laser Parameter During Machining of Al/Al2O3-MMC Elsevier, Opt. Laser Technol., 2013, 46, p 67–76.10.1016/j.optlastec.2012.04.030 ↗
16. S.C. Kulkarni, M.S. Patil, A.V. Kulkarni, R.M. Kulkarni, and R.R. Malagi, Effect of Manufacturing Method of Cu-Al Electrode on Performance During Electric Discharge Machining of Siliconised Silicon Carbide (SiSiC), Mater. Today: Proc., 2020, 27, p 120–129.10.1016/j.matpr.2019.09.064 ↗
17. A.M. Sifullah, A. Khaled, Y. Nukman, M.A. Hassan, and A. Hossain, Laser Cutting of Square Blanks in Stainless Steel-304 Sheets: HAZ and Thermal Stress Analysis, Sains Malays., 2017, 46(5), p 755–762.10.17576/jsm-2017-4605-10 ↗
18. A.D. Tura, H.B. Mamo, and D.G. Desisa, Multi-Objective Optimization and Analysis for Laser Beam Cutting of Stainless Steel (SS304) Using Hybrid Statistical Tools GA-RSM, IOP Conf. Ser. Mater. Sci. Eng., 2021, 1201(1), p 012030.10.1088/1757-899X/1201/1/012030 ↗
19. M. Moradi, O. Mehrabi, T. Azdast, and K.Y. Benyounis, Enhancement of Low Power CO2 laser Cutting Process for Injection Molded Polycarbonate, Opt. Laser Technol., 2017, 96, p 208–218.10.1016/j.optlastec.2017.05.022 ↗