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Parametric optimization of laser deposited high entropy alloys using response surface methodology (RSM)

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Abstract

In this study, a full-factorial design of experiments was systematically structured to optimize the laser processing parameters of additively manufactured high entropy alloys by determining the impact of these parameters on the mechanical properties of the amalgams for aerospace applications. Analysis of variance (ANOVA) was employed to determine the conditions that allow the optimization process and to verify the model significance or otherwise. Laser additive manufacturing was used to fabricate the high-entropy alloys on a steel base-plate. To improve the quality of the components built and its mechanical properties, the laser process parameters must be optimized. Thus, the design of experiments was employed as a cost-effective tool for optimization with laser power and scan speed as parametric factors using a response surface methodology (RSM). These parameters were varied simultaneously over a few sets of experimental runs to determine the optimum process parameter for improving the output response. The output response was the micro-hardness properties of the HEAs after the laser deposited components were subjected to a Vickers hardness test using a Matsuzawa Seiki MMT-X series Vickers micro-hardness testing machine. The results showed that the optimum parameters were at a laser power of 1500 and 1600 W with a scan speed of 10 mm/s to give microhardness responses of 450 and 715 HV for the Ti-based and Cu-based high-entropy alloy, respectively. The model revealed that there was a strong correlation between the predicted microhardness response and the actual experimental data.

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References

  1. Jien-Wei Y (2006) Recent progress in high entropy alloys. Ann Chim Sci Mater 31:633–648

    Google Scholar 

  2. Aristeidakis IS, Maria-Ioanna TT (2016). Physical Metallurgy. High Entropy Alloys. High Entropy Alloys University of Thessaly Press, Volos Greece

  3. Zhang EA (2018) Science and technology in high-entropy alloys. Sci China Mater. https://doi.org/10.1007/s40843-017-9195-8

  4. Yeh (2013) Alloy design strategies and future trends in high-entropy alloys. Jom 65:1759–1771

    Google Scholar 

  5. Senkov O, Wilks G, Miracle D, Chuang C, Liaw P (2010) Refractory high-entropy alloys. Intermetallics 18:1758–1765

    Google Scholar 

  6. Zhang Y, Zuo TT, Tang Z, Gao MC, Dahmen KA, Liaw PK et al (2014) Microstructures and properties of high-entropy alloys. Prog Mater Sci 61:1–93

    Google Scholar 

  7. Lu Y, Dong Y, Guo S, Jiang L, Kang H, Wang T et al (2014) A promising new class of high-temperature alloys: eutectic high-entropy alloys. Sci Rep 4:6200

    Google Scholar 

  8. Murty B, Satyanarayana et al. (2019) High-entropy alloys. 2nd Edition. Elsevier, Amsterdam, pp 70–150

  9. Miracle DB, Senkov ON (2017) A critical review of high entropy alloys and related concepts. Acta Mater 122:448–511

    Google Scholar 

  10. Tsai M-H (2013) Physical properties of high entropy alloys. Entropy 15:5338–5345

    MATH  Google Scholar 

  11. Li Z, Zhao S, Ritchie RO, Meyers MA (2019) Mechanical properties of high-entropy alloys with emphasis on face-centered cubic alloys. Prog Mater Sci 102:296–345

    Google Scholar 

  12. Giannakopoulos A, Larsson P-L, Vestergaard R (1994) Analysis of Vickers indentation. Int J Solids Struct 31:2679–2708

    MATH  Google Scholar 

  13. Ostojic P, McPherson R (1987) A review of indentation fracture theory: its development, principles and limitations. Int J Fract 33:297–312

    Google Scholar 

  14. Quinn GD, Bradt RC (2007) On the Vickers indentation fracture toughness test. J Am Ceram Soc 90:673–680

    Google Scholar 

  15. Pelleg J (2012) Mechanical properties of materials. Springer Science & Business Media, Berlin, pp 36–50

  16. Bashir MJ, Aziz HA, Yusoff MS, Adlan MN (2010) Application of response surface methodology (RSM) for optimization of ammoniacal nitrogen removal from semi-aerobic landfill leachate using ion exchange resin. Desalination 254:154–161

    Google Scholar 

  17. Witek-Krowiak A, Chojnacka K, Podstawczyk D, Dawiec A, Pokomeda K (2014) Application of response surface methodology and artificial neural network methods in modelling and optimization of biosorption process. Bioresour Technol 160:150–160

    Google Scholar 

  18. Hu W, Enying L, Yao LG (2008) Optimization of drawbead design in sheet metal forming based on intelligent sampling by using response surface methodology. J Mater Process Technol 206:45–55

    Google Scholar 

  19. Bahloul R, Ben-Elechi S, Potiron A (2006) Optimisation of springback predicted by experimental and numerical approach by using response surface methodology. J Mater Process Technol 173:101–110

    Google Scholar 

  20. Qian L, Ping Y, Yunbai L (2013) Response surface modeling and optimization of a new impact-toughened mould material used in the shaping of sanitary ware. Mater Des 50:191–197

    Google Scholar 

  21. Azaouzi M, Lebaal N (2012) Tool path optimization for single point incremental sheet forming using response surface method. Simul Model Pract Theory 24:49–58

    Google Scholar 

  22. Nemati-Chari R, Dehghani K, Kami A, Banabic D (2015) Application of response surface methodology for study of effective strain in equal channel angular pressing of AA6061 alloy. Proceed Roman Acad Ser A-Mathemat Phys Tech Sci Inf Sci 16:184–192

    Google Scholar 

  23. Makadia AJ, Nanavati J (2013) Optimisation of machining parameters for turning operations based on response surface methodology. Measurement 46:1521–1529

    Google Scholar 

  24. Ujah C, Popoola A, Popoola O, Aigbodion V (2019) Optimisation of spark plasma sintering parameters of Al-CNTs-Nb nano-composite using Taguchi Design of Experiment. Int J Adv Manuf Technol 100:1563–1573

    Google Scholar 

  25. Khajelakzay M, Bakhshi SR (2017) Optimization of spark plasma sintering parameters of Si3N4-SiC composite using response surface methodology (RSM). Ceram Int 43:6815–6821

    Google Scholar 

  26. Wang X, Song X, Jiang M, Li P, Hu Y, Wang K et al (2012) Modeling and optimization of laser transmission joining process between PET and 316L stainless steel using response surface methodology. Opt Laser Technol 44:656–663

    Google Scholar 

  27. Adalarasan R, Santhanakumar M, Rajmohan M (2015) Optimization of laser cutting parameters for Al6061/SiCp/Al2O3 composite using grey based response surface methodology (GRSM). Measurement 73:596–606

    Google Scholar 

  28. Dhupal D, Doloi B, Bhattacharyya B (2007) Optimization of process parameters of Nd: YAG laser microgrooving of Al2TiO5 ceramic material by response surface methodology and artificial neural network algorithm. Proc Inst Mech Eng B J Eng Manuf 221:1341–1350

    Google Scholar 

  29. Dada M, Popoola P, Mathe N, Pityana S, Adeosun S, Aramide O, Lengopeng T Process Optimization of High Entropy Alloys by Laser Additve Manufactruing. Authorea. https://doi.org/10.22541/au.158049508.81554322

  30. Rao RV, Kalyankar V (2014) Optimization of modern machining processes using advanced optimization techniques: a review. Int J Adv Manuf Technol 73:1159–1188

    Google Scholar 

  31. Ogunbiyi O, Jamiru T, Sadiku R, Adesina O, Olajide JL, Beneke L (2019) Optimization of spark plasma sintering parameters of inconel 738LC alloy using response surface methodology (RSM). Int J Lightweight Mater Manuf 3(2):177–188

  32. Paul L, Hiremath SS (2013) Response surface modelling of micro holes in electrochemical discharge machining process. Procedia Eng 64:1395–1404

    Google Scholar 

  33. Sahu NK, Andhare A (2018) Design of Experiments Applied to Industrial Process. In: Statistical Approaches With Emphasis on Design of Experiments Applied to Chemical Processes, p 5

    Google Scholar 

  34. Montgomery DC (2017) Design and analysis of experiments. Wiley

  35. Kharissova OV, Dias HR, Kharisov BI, Pérez BO, Pérez VMJ (2013) The greener synthesis of nanoparticles. Trends Biotechnol 31:240–248

    Google Scholar 

  36. Olorundaisi E, Jamiru T, Adegbola T, Ogunbiyi O (2019) Modeling and optimization of operating parameters using RSM for mechanical behaviour of dual phase steels. Mater Res Express 6:105628

    Google Scholar 

  37. Saldaña-Robles A, Guerra-Sánchez R, Maldonado-Rubio MI, Peralta-Hernandez JM (2014) Optimization of the operating parameters using RSM for the Fenton oxidation process and adsorption on vegetal carbon of MO solutions. J Ind Eng Chem 20:848–857

    Google Scholar 

  38. Yang WP, Tarng Y (1998) Design optimization of cutting parameters for turning operations based on the Taguchi method. J Mater Process Technol 84:122–129

    Google Scholar 

  39. Myers RH, Montgomery DC, Anderson-Cook CM (2016) Response surface methodology: process and product optimization using designed experiments. Wiley Series in Probability and Statistics, 4th ediition. Wiley John, Wiley & Sons, Hoboken

  40. Hill WJ, Hunter WG (1966) A review of response surface methodology: a literature survey. Technometrics 8:571–590

    MathSciNet  Google Scholar 

  41. Anderson VL, McLean RA (1974) Design of experiments: a realistic approach. Marcel Dekker. Inc., New York, p 418

  42. Fatemi S, Ashany JZ, Aghchai AJ, Abolghasemi A (2017) Experimental investigation of process parameters on layer thickness and density in direct metal laser sintering: a response surface methodology approach. Virtual Phys Prototyp 12:133–140

    Google Scholar 

  43. Beugelsdijk S, Kostova T, Roth K (2017) An overview of Hofstede-inspired country-level culture research in international business since 2006. J Int Bus Stud 48:30–47

    Google Scholar 

  44. Lundstedt T, Seifert E, Abramo L, Thelin B, Nyström Å, Pettersen J et al (1998) Experimental design and optimization. Chemom Intell Lab Syst 42:3–40

    Google Scholar 

  45. Bezerra MA, Santelli RE, Oliveira EP, Villar LS, Escaleira LA (2008) Response surface methodology (RSM) as a tool for optimization in analytical chemistry. Talanta 76:965–977

    Google Scholar 

  46. Shuai C, Yang Y, Wu P, Lin X, Liu Y, Zhou Y et al (2017) Laser rapid solidification improves corrosion behavior of mg-Zn-Zr alloy. J Alloys Compd 691:961–969

    Google Scholar 

  47. Dinda G, Dasgupta A, Mazumder J (2012) Evolution of microstructure in laser deposited Al–11.28% Si alloy. Surf Coat Technol 206:2152–2160

    Google Scholar 

  48. Yandell B (1997) Practical data analysis for designed experiments. Vol 39. Taylor & Francis CRC Press, Boca Raton, p 35

  49. Whitcomb PJ, Anderson MJ (2004) RSM simplified: optimizing processes using response surface methods for design of experiments. Taylor & Francis CRC Press, Boca Raton, p 215

  50. Mason RL, Gunst RF, Hess JL (2003) Statistical design and analysis of experiments: with applications to engineering and science, vol 474. John Wiley & Sons, Hoboken, pp 107–309

  51. Sundararaman K, Padmanaban K, Sabareeswaran M (2016) Optimization of machining fixture layout using integrated response surface methodology and evolutionary techniques. Proc Inst Mech Eng C J Mech Eng Sci 230:2245–2259

    Google Scholar 

  52. Datta S, Mahapatra S (2010) Modeling, simulation and parametric optimization of wire EDM process using response surface methodology coupled with grey-Taguchi technique. Int J Eng Sci Technol 2:162–183

    Google Scholar 

  53. Fatoba O, Adesina O, Farotade G, Adediran A (2017) Modelling and optimization of laser alloyed AISI 422 stainless steel using Taguchi approach and response surface model (RSM). Curr J Appl Sci Technol:1–16

  54. Danmaliki GI, Saleh TA, Shamsuddeen AA (2017) Response surface methodology optimization of adsorptive desulfurization on nickel/activated carbon. Chem Eng J 313:993–1003

    Google Scholar 

  55. Fedorov VV (1972) Theory of optimal experiments. 1st edn. Elsevier Science Academic Press, London, pp 213–223

  56. Candioti LV, De Zan MM, Cámara MS, Goicoechea HC (2014) Experimental design and multiple response optimization. Using the desirability function in analytical methods development. Talanta 124:123–138

    Google Scholar 

  57. R. O. Kuehl and R. Kuehl, "Design of experiments: statistical principles of research design and analysis," 2000

    MATH  Google Scholar 

  58. Wu CJ, Hamada MS (2011) Experiments: planning, analysis, and optimization, vol 552, 2nd edn. John Wiley & Sons Hoboken

  59. Brinksmeier E, TÖnshoff HK, Czenkusch C, Heinzel C (1998) Modelling and optimization of grinding processes. J Intell Manuf 9:303–314

    Google Scholar 

  60. Dean A, Voss D, Draguljić D (1999) Design and analysis of experiments, vol 1. Springer, New York

  61. Bailey RA (2008) Design of comparative experiments, vol 25 of Cambridge Series in Statistical and Probabilistic Mathematics: Cambridge University Press, Cambridge, pp 259–271

  62. Pan LK, Wang CC, Hsiao YC, Ho KC (2005) Optimization of Nd: YAG laser welding onto magnesium alloy via Taguchi analysis. Opt Laser Technol 37:33–42

    Google Scholar 

  63. Nocedal J, Wright S (2006) Numerical optimization. Springer series in Operations Research and Financial Engineering, 2nd edn. Springer Science & Business Media Berlin

  64. Wright S, Nocedal J (1999) Numerical optimization. Springer Science+ business Media Berlin, pp 67–68

  65. Baş D, Boyacı IH (2007) Modeling and optimization I: usability of response surface methodology. J Food Eng 78:836–845

    Google Scholar 

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Acknowledgments

The authors will like to appreciate the National Laser Centre at the Council for Scientific and Industrial Research (CSIR) and the Tshwane University of Technology, Pretoria, South Africa, for their technical support.

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Authors and Affiliations

  1. Chemical, Metallurgical and Materials Engineering, Tshwane University of Technology, Pretoria, South Africa

    Modupeola Dada & Patricia Popoola

  2. Council for Scientific and Industrial Research, Pretoria, South Africa

    Ntombizodwa Mathe & Sisa Pityana

  3. Metallurgical and Materials Engineering, University of Lagos, Akoka, Nigeria

    Samson Adeosun

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  1. Modupeola Dada
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Dada, M., Popoola, P., Mathe, N. et al. Parametric optimization of laser deposited high entropy alloys using response surface methodology (RSM). Int J Adv Manuf Technol 109, 2719–2732 (2020). https://doi.org/10.1007/s00170-020-05781-1

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