Advertisement

Arabian Journal for Science and Engineering

, Volume 44, Issue 2, pp 919–934 | Cite as

Process Optimization of Slurry Spray Technique Through Multi-attribute Utility Function

  • Rajeev VermaEmail author
  • Suman Kant
  • Narendra Mohan Suri
Research Article - Mechanical Engineering
  • 23 Downloads

Abstract

Slurry spray technique (SST) could be bethought as an allied thermal spray coating method; wherein, the coating ingredients are subjected to thermal energy for sintering in order to mature the coating structure. It is also relatively new deposition technique and needs more research endeavours for its exploration and effective utilization. This can be done by process optimization for dependent variables of concern in respect to the appropriately chosen input process parameters. Amongst the existing and previously applied multi-attribute optimization approaches, utility-based Taguchi approach has been practiced in this work due to its commendatory properties like monotonicity and concavity. The utility-based elicitation methodology has been demonstrated for simultaneous optimization of two dependent variables of interest, i.e. adhesion strength and coating thickness of mullite–nickel coatings deposited by SST. The utility values based on the preference structure of these dependent variables have been analysed for process accretion by using Taguchi’s theory. Confirmation experiments assure the conceivability of the approach over the range of coating conditions exerted in the SST experimentation. The characterization of thus produced coatings illustrated formidable microstructure free from any major imperfections, besides conforming to the utility-based Taguchi results for the unified attribute function comprising the two response parameters under study.

Keywords

Slurry spray technique Taguchi Utility Adhesion strength Coating thickness 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Walia, R.S.; Shan, H.S.; Kumar, P.: Parametric optimization of centrifugal force assisted abrasive flow machining (CFAAFM) by the Taguchi method. J. Mater. Process. Manuf. 21(4), 37–382 (2005).  https://doi.org/10.1080/10426910500411645 Google Scholar
  2. 2.
    Dubey, A.K.: Multi-response optimization of electro-chemical honing using utility-based Taguchi approach. Int. J. Adv. Manuf. Technol. 41, 749–759 (2008).  https://doi.org/10.1007/s00170-008-1525-2 CrossRefGoogle Scholar
  3. 3.
    Dhanapal, P.; Mohamed Nazirudeen, S.S.; Chandrasekar, A.: Multi-response optimization of carbidic austempered ductile iron production parameters using Taguchi method. J. Inst. Eng. India Ser. D 93(1), 23–29 (2012).  https://doi.org/10.1007/s40033-012-0003-z CrossRefGoogle Scholar
  4. 4.
    Teimouri, R.; Baseri, H.; Moharami, R.: Multi-responses optimization of ultrasonic machining process. J. Intell. Manuf. 26(4), 745–753 (2013).  https://doi.org/10.1007/s10845-013-0831-1 CrossRefGoogle Scholar
  5. 5.
    Deng, J.: Control problems of grey systems. Syst. Control Lett. 1(5), 288–294 (1982)MathSciNetCrossRefzbMATHGoogle Scholar
  6. 6.
    Kuo, Y.; Yang, T.; Huang, G.W.: The use of gray relational analysis in solving multi attribute decision making problems. Eng. Optim. 40(6), 517–528 (2008).  https://doi.org/10.1080/03052150701857645 CrossRefGoogle Scholar
  7. 7.
    Gadharia, P.; Sahooa, P.: Influence of process parameters on multiple roughness characteristics of Ni–P–\(\text{ TiO }_{2}\) composite coatings. Procedia Eng. 94, 439–448 (2014).  https://doi.org/10.1016/j.proeng.2014.12.268 CrossRefGoogle Scholar
  8. 8.
    Roy, S.; Sahoo, P.: Parametric optimization of corrosion and wear of electroless Ni-P-Cu coating using grey relational coefficient coupled with weighted principal component analysis. Int. J. Mech. Mater. Eng. 9(1), 1–15 (2014).  https://doi.org/10.1186/s40712-014-0010-y CrossRefGoogle Scholar
  9. 9.
    Panja, B.; Das, S.K.; Sahoo, P.: Tribological behaviour of electroless Ni-P coatings in brine solution and optimization of coating parameters using Taguchi based grey relation analysis. J. Inst. Eng. India Ser. C 96(3), 299–309 (2015).  https://doi.org/10.1007/s40032-015-0174-0 CrossRefGoogle Scholar
  10. 10.
    Jailani, H.S.; et al.: Multi-response optimisation of sintering parameters of Al–Si alloy/fly ash composite using Taguchi method and grey relational analysis. Int. J. Adv. Manuf. Technol. 45, 362–369 (2009).  https://doi.org/10.1007/s00170-009-1973-3 CrossRefGoogle Scholar
  11. 11.
    Pearson, K.: On lines and planes of closest fit to systems of points in space. Philos. Mag. 62, 559–572 (1901)CrossRefzbMATHGoogle Scholar
  12. 12.
    Hotelling, H.: Analysis of a complex of statistical variables into principal components. J. Educ. Psychol. 24, 417–441 (1993)CrossRefGoogle Scholar
  13. 13.
    Rajesh, S.; Devaraj, D.; Sudhakara Pandian, R.; Rajakarunakaran, S.: Multi-response optimization of machining parameters on red mud-based aluminum metal matrix composites in turning process. Int. J. Adv. Manuf. Technol. 67, 811–821 (2012).  https://doi.org/10.1007/s00170-012-4525-1 CrossRefGoogle Scholar
  14. 14.
    Nikrooz, B.; Zandrahimi, M.: Optimization of process variables and corrosion properties of a multi-layer silica sol gel coating on AZ91D using the Box–Behnken design. J. Sol–Gel Sci. Technol. 59, 640–649 (2011).  https://doi.org/10.1007/s10971-011-2539-z CrossRefGoogle Scholar
  15. 15.
    Tillmann, W.; Vogli, E.; Baumann, I.; Kopp, G.; Weihs, C.: Desirability-based multi-criteria optimization of HVOF spray experiments to manufacture fine structured wear-resistant \(\text{75Cr }_{3}\text{ C }_{2}\)-25(NiCr20) coatings. J. Thermal Spray Technol. 19(1–2), 392–408 (2010).  https://doi.org/10.1007/s11666-009-9383-5 CrossRefGoogle Scholar
  16. 16.
    Raveendran, P.; Marimuthu, P.: Multi-response optimization of turning parameters for machining glass fibre-reinforced plastic composite rod. Adv. Mech. Eng. 7(12), 1–10 (2015).  https://doi.org/10.1177/1687814015620109 CrossRefGoogle Scholar
  17. 17.
    Meral, G.; Sarıkaya, M.; Dilipak, H.; Şeker, U.: Multi-response optimization of cutting parameters for hole quality in drilling of AISI 1050 steel. Arab. J. Sci. Eng. 40(12), 3709–3722 (2015)CrossRefGoogle Scholar
  18. 18.
    Bhattacharya, A.; Singla, S.: Dissimilar GTAW between AISI 304 and AISI 4340 steel: Multi-response optimization by analytic hierarchy process. Proc. IMechE Part E: J. Process. Mech. Eng. (2016).  https://doi.org/10.1177/0954408916641458
  19. 19.
    Sharma, V.; Chatopathadhyaya, S.; Hloch, S.: Multi response optimization of process parameters based on Taguchi—Fuzzy model for coal cutting by water jet technology. Int. J. Adv. Manuf. Technol. 56, 1019–1025 (2011).  https://doi.org/10.1007/s00170-011-3258-x CrossRefGoogle Scholar
  20. 20.
    Atashpaz-Gargari, E.; Lucas, C.: Imperialist competitive algorithm: an algorithm for optimization inspired by imperialistic competition. In: IEEE Congress on Evolutionary Computation, pp. 4661–4667 (2007)Google Scholar
  21. 21.
    Kaveh, A.; Talatahari, S.: Optimum design of skeletal structures using imperialist competitive algorithm. Comput. Struct. 88(21), 1220–1229 (2010)CrossRefzbMATHGoogle Scholar
  22. 22.
    Verma, R.; Kant, S.; Suri, N.M.: Adhesion strength optimization of slurry sprayed mullite-based coating using Taguchi method. Proc. IMechE Part E: J Process Mech. Eng. 230(2), 87–96 (2016).  https://doi.org/10.1177/0954408915595948 Google Scholar
  23. 23.
    Verma, R.; Suri, N.M.; Kant, S.: Effect of parameters on adhesion strength for slurry spray coating technique. J. Mater. Manuf. Process 32(4), 416–424 (2016).  https://doi.org/10.1080/10426914.2016 CrossRefGoogle Scholar
  24. 24.
    Verma, R.; Suri, N.M.; Kant, S.: Parametric appraisal of slurry sprayed mullite coatings for coating thickness. J. Thermal Spray Technol. 25(7), 1289–1301 (2016).  https://doi.org/10.1007/s11666-016-0437-1 CrossRefGoogle Scholar
  25. 25.
    Thakur, A.G.; Nandedkar, V.M.: Optimization of the resistance spot welding process of galvanized steel sheet using the Taguchi method. Arab. J. Sci. Eng. 39(2), 1171–1176 (2014).  https://doi.org/10.1007/s13369-013-0634-x CrossRefGoogle Scholar
  26. 26.
    Goyal, T.; Walia, R.S.; Sidhu, T.S.: Multi-response optimization of low-pressure cold-sprayed coatings through Taguchi method and utility concept. Int. J. Adv. Manuf. Technol. 64(5–8), 903–914 (2013).  https://doi.org/10.1007/s00170-012-4049-8 CrossRefGoogle Scholar
  27. 27.
    Kapoor, J.: Multi-response optimization of WEDM using utility-based Taguchi approach. In: Proceedings of the International Conference on Research and Innovations in Mechanical Engineering, pp. 233–242. Springer India (2014)Google Scholar
  28. 28.
    Ruder, A.; Buchkremer, H.P.; Jansen, H.; Mallener, W.; Stöver, D.: Wet powder spraying—a process for the production of coatings. Surf. Coat. Technol. 53(1), 71–74 (1992)CrossRefGoogle Scholar
  29. 29.
    Nguyen, P.; Harding S.; Ho. S. Y.: Experimental studies on slurry based thermal barrier coatings. In: 5th Australasian Congress on Applied Mechanics ACAM, vol 1, pp. 545–550. Engineers Australia (2007)Google Scholar
  30. 30.
    Aixiang, Z.; Weihao, X.; Caifang, W.; Qionghua, Z.: Structure and properties of \(\text{ BaFe }_{12}\text{ O }_{19}\) coated fly ash cenospheres by sol-gel process. J. Wuhan Univ. Technol. Mater. Sci. Ed. 21(3), 129–131 (2006)CrossRefGoogle Scholar
  31. 31.
    US Patent US 7,056,583 B2. James Stewart. Flyash coating, Washington, DC: US Patent and Trademark Office (2006)Google Scholar
  32. 32.
    Chavez-Valdez, A.; Arizmendi-Morquecho, A.; Vargas, G.; Almanza, J.M.; Alvarez-Quintana, J.: Ultra-low thermal conductivity thermal barrier coatings from recycled fly-ash cenospheres. Acta Materialia 59(6), 2556–2562 (2011)CrossRefGoogle Scholar
  33. 33.
    Behera, A.; Mishra, S.C.: Prediction and analysis of deposition efficiency of plasma spray coating using artificial intelligence method. Open J. Compos. Mater. 2, 54–60 (2012)CrossRefGoogle Scholar
  34. 34.
    Sambyal, P.; Ruhi, G.; Bhandari, H.; Dhawan, S.K.: Advanced anti corrosive properties of poly (aniline-co-o-toluidine)/flyash composite coatings. Surf. Coat. Technol. 272, 129–140 (2015)CrossRefGoogle Scholar
  35. 35.
    Mishra, S.C.; Rout, K.C.; Padmanabhan, P.V.A.; Mills, B.: Plasma spray coating of fly ash pre-mixed with aluminium powder deposited on metal substrates. J. Mater. Process. Technol. 102(1), 9–13 (2000)CrossRefGoogle Scholar
  36. 36.
  37. 37.
    Derek, W.B.: Analysis for Optimal Decisions, p. 92. Wiiley, New York (1982)Google Scholar
  38. 38.
    Dong, Y.; Hampshire, S.; Zhou, J.E.; Lin, B.; Ji, Z.; Zhang, X.; Meng, G.: Recycling of fly ash for preparing porous mullite membrane supports with titania addition. J. Hazard. Mater. 180(1), 173–180 (2010)CrossRefGoogle Scholar
  39. 39.
    Zois, D.; Lekatou, A.; Vardavoulias, M.: A microstructure and mechanical property investigation on thermally sprayed nanostructured ceramic coatings before and after a sintering treatment. Surf. Coat. Technol. 204, 15–27 (2009)CrossRefGoogle Scholar
  40. 40.
    Dahl, P.; Kaus, I.; Zhao, Z.; Johnsson, M.; Nygren, M.; Wiik, K.; Grande, T.; Einarsrud, M.A.: Densification and properties of zirconia prepared by three different sintering techniques. Ceram. Int. 33(8), 1603–1610 (2007)CrossRefGoogle Scholar

Copyright information

© King Fahd University of Petroleum & Minerals 2018

Authors and Affiliations

  • Rajeev Verma
    • 1
    Email author
  • Suman Kant
    • 2
  • Narendra Mohan Suri
    • 2
  1. 1.Industrial and Production Engineering DepartmentDr BR Ambedkar National Institute of TechnologyJalandharIndia
  2. 2.Production and Industrial Engineering DepartmentPEC University of TechnologyChandigarhIndia

Personalised recommendations