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Analysis of the effecting parameters on laser cutting process by using response surface methodology (RSM) method

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Języki publikacji
EN
Abstrakty
EN
Purpose: The main aim of this research is to analyse the effects of the laser cutting parameters on the kerf width of stainless steel (2505), and develop a model of laser cutting that can predict the relation between the characteristics of the resultant kerf width and the process input parameters. Design/methodology/approach: To achieve the minimum kerf width; the optimal setting of the effecting parameters like; power supply, cutting speed, and gas pressure for the response surface methodology, and factorial design-based optimal parametric analysis has been carried out for this purpose. A mathematical model for analysis of the kerf width was developed using the (Minitab 16) on the basis of experimental results. Findings: It’s found that the interaction between power value, cutting speed, and pressure has a significant effect on the response value. Also, it’s found that, when the power and cutting speed are set at optimal values i.e. 1250 watt and 5 mm/min, the minimum kerf width will be 0.389 mm. The mathematical model has been established based on regression analysis by factorial design and response surface model. Research limitations/implications: The cutting quality in this process widely depend on the technical specifications of a laser machine. Consequently, machine operation parameters are considered the main limitations factor in this process. Practical implications: In this current work, the 4 mm thickness stainless steel (2505) has been used in the experimental investigation to measure the Influence of laser cutting machine parameters. In addition, optimal laser cutting parameter values that minimize the width of kerf width were identified. The optimization problem was formulated and solved by the second-order model method. The laser cutting experiment was planned and conducted according to the (RSM) central composite design approach (uncode). Originality/value: The validation with experimental results shows that the factorial analysis gives an average error 5%, and the (R-sq) is equal to 69.02%. It’s concluded that the model that has the (R-sq) value greater than 41% is considered as a fit model and can be used for the next machining process.
Rocznik
Strony
59--66
Opis fizyczny
Bibliogr. 20 poz., rys., tab.
Twórcy
autor
  • Institute Technical of Karbala, Al-Furat Al Awsat Technical University, Iraq
Bibliografia
  • 1. S.F. Lombardo, I. Deretzis, A. Sciuto, A. La Magna, Continuum modeling and TCAD simulations of laser-related phenomena in CMOS applications, in: F. Cristiano, A. La Magna (eds.), Woodhead Publishing Series in Electronic and Optical Materials, Laser Annealing Processes in Semiconductor Technology, Woodhead Publishing, 2021, 251-291. DOI: https://doi.org/10.1016/B978-0-12-820255-5.00002-7
  • 2. A.N. Samant, N.B. Dahotre, Laser machining of structural ceramics: a review, Journal of the European Ceramic Society 29/6 (2009) 969-993. DOI: https://doi.org/10.1016/j.jeurceramsoc.2008.11.010
  • 3. R.H. Myers, D.C. Montgomery, C.M. Anderson-Cook, Response Surface Methodology: Process and product optimization using Designed Experiments, Third Edition, Wiley, 2009.
  • 4. A. Gok, C. Gologlu, H.I. Demirci, M. Kurt, Determination of Surface Qualities on Inclined Surface Machining with Acoustic Sound Pressure, Strojniški Vestnik - Journal of Mechanical Engineering 58/10 (2012) 587-597. DOI: https://doi.org/10.5545/sv-jme.2012.352
  • 5. M. Hashemzadeh, R. Pourshaban, The Effect of Power, Maximum Cutting Speed and Specific Point Energy on the Material Removal Rate and Cutting Volume Efficiency in CO2 Laser Cutting of Polyamide Sheets, Journal of Modern Processes in Manufacturing and Production 9/3 (2020) 23-39.
  • 6. R.D. Shelke, U.H. Chavan, Optimization of Sheet Metal Cutting Parameters of Laser Beam Machine, International Journal of Engineering Sciences and Research Technology 7/4 (2018) 474-484. DOI: https://doi.org/10.5281/zenodo.1218697
  • 7. E. Gvozdev, I.V. Golyshev, I.V. Minayev, A.N. Sergeyev, N.N. Sergeyev, I.V. Tikhonova, D.M. Khonelidze, A.G. Kolmakov, Multiparametric Optimi¬zation of Laser Cutting of Steel Sheets, Inorganic Materials: Applied Research 6/4 (2015) 305-310. DOI: https://doi.org/10.1134/S2075113315040115
  • 8. T. Mushtaq, Y. Wang, M. Rehman, A.M. Khan, M. Mia, State-Of-The-Art and Trends in CO2 Laser Cutting of Polymeric Materials - A Review, Materials 13/17 (2020) 3839. DOI: https://doi.org/10.3390/ma13173839
  • 9. J. Vora, R. Chaudhari, C. Patel, D.Y. Pimenov, V.K. Patel, K. Giasin, S. Sharma, Experimental Investigations and Pareto Optimization of Fiber Laser Cutting Process of Ti6Al4V, Metals 11/9 (2021) 1461. DOI: https://doi.org/10.3390/met11091461
  • 10. I. Kubovský, L. Krišt’ák, J. Suja, M. Gajtanska, R. Igaz, I. Ružiak, R. Réh, Optimization of Parameters for the Cutting of Wood-Based Materials by a CO2 Laser, Applied Sciences 10/22 (2020) 8113. DOI: https://doi.org/10.3390/app10228113
  • 11. A. Riveiro, F. Quintero, F. Lusquiños, R. Comesana, J. Pou, Study of melt flow dynamics and influence on quality for CO2 laser fusion cutting, Journal of Physics D: Applied Physics 44/13 (2011) 135501. DOI: https://doi.org/10.1088/0022-3727/44/13/135501
  • 12. M. Madic, M. Radovanovic, Analysis of the heat affected zone in CO2 laser cutting of stainless steel, Thermal Science 16/suppl. 2 (2012) 363-373. DOI: https://doi.org/10.2298/TSCI120424175M
  • 13. S.R. Rajpurohit, D.M. Patel, Striation Mechanism in Laser Cutting: The Review, International Journal of Engineering Research and Applications 2/2 (2006) 457-461.
  • 14. H.A. Eltawahni, A.G. Olabi, K.Y. Benyounis, Investigating the CO2 laser cutting parameters of MDF wood composite material, Optics and Laser Technology 43/3 (2011) 648-659. DOI: https://doi.org/10.1016/j.optlastec.2010.09.006
  • 15. B. Bulut, O. Tazegul, M. Baydogan, E.S. Kayali, The comparison of the sintering methods for diamond cutting tools, Journal of Achievements in Materials and Manufacturing Engineering 76/1 (2016) 30-35. DOI: https://doi.org/10.5604/17348412.1228631
  • 16. Y.S. Liu, J.Y. Wu, Optimization of cell growth and carotenoid production of Xanthophyllomyces dendrorhous through statistical experiment design, Biochemical Engineering Journal 36/2 (2007) 182-189. DOI: https://doi.org/10.1016/j.bej.2007.02.014
  • 17. H.M. Magid, Experimental study of mild steel cutting process by using the plasma arc method, Journal of Achievements in Materials and Manufacturing Engineering 108/2 (2021) 75-85. DOI: https://doi.org/10.5604/01.3001.0015.5066
  • 18. C. Sivri, Development of electrospun nanofibers having novel morphologies via corona plasma treatment, Journal of Achievements in Materials and Manufacturing Engineering 76/1 (2016) 36-40. DOI: https://doi.org/10.5604/17348412.1228632
  • 19. D.C. Montgomery, G.C. Runger, N.F. Hubele, Engineering Statistics, Fifth edition, John Wiley & Sons, Inc, 2010.
  • 20. H. Kim, A. Piqué, Laser Processing of Energy Storage Materials, in: M. Pomeroy (ed.), Encyclopedia of Materials: Technical Ceramics and Glasses, Vol. 3, Elsevier, 2021, 59-73. DOI: https://doi.org/10.1016/B978-0-12-803581-8.12086-7.
Uwagi
PL
Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-83500e83-ce03-44de-b587-fc13183bb4db
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