Skip to main content
SpringerLink
Log in

A new multiobjective optimization with elliptical constraints approach for nonlinear models implemented in a stainless steel cladding process

  • ORIGINAL ARTICLE
  • Published:
The International Journal of Advanced Manufacturing Technology Aims and scope Submit manuscript

Abstract

This paper proposes a new multiobjective optimization with elliptical constraints approach for nonlinear models implemented in a cladding process of ABNT 1020 carbon steel plate using austenitic ABNT 316L stainless steel cored wire. Stainless steel stands out among the cladding materials as it allows obtaining surfaces with determined desirable characteristics from lower cost materials. This kind of process may be controlled by a relatively small number of input variables, i.e., the wire feed rate (WF), voltage (V), welding speed (WS), and the distance from the contact tip to the workpiece (N). Besides that, many outputs can be evaluated and simultaneously optimized. In the present paper, dilution (D), yield (Y), convexity index (CI), and penetration index (PI) were investigated. In order to consider the problem’s multivariate nature, techniques such as factor analysis and Bonferroni’s multivariate intervals were applied combined with elliptical constraints. The response variables were mathematically modeled using Poisson regression, and the obtained results were satisfactory since accurate models were achieved. The normal boundary intersection (NBI) method produced a set of viable configurations for the input variables that allows the experimenter to encounter the best system setup regarding the importance level of each response. Feasible and non-dominated solutions were found which means that the best possible scenario considering all the constraints was reached.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

Data availability

All the data used in this research were extracted from [22].

References

  1. Pessoa ARP (2010) Seleção de parâmetros através do método Taguchi para soldagem de revestimento com ligas de níquel pelo processo MIG/MAG. Soldag. insp. (Impr.), São Paulo. 15(4): 317–324

  2. Murugan N, Parmar RS (1997) Stainless steel cladding deposited by automatic gas metal arc welding. Weld J 76:391s–403s

    Google Scholar 

  3. Gupta D, Sharma A (2011) Development and microstructural characterization of microwave cladding on austenitic stainless steel. Surf Claddings Technol, Elsevier 205(21–22):5147–5155

    Article  Google Scholar 

  4. Phillips AL (1965b) Welding handbook: special welding processes and cutting, vol 3. American Welding Society, London, p 4

    Google Scholar 

  5. Marques PV, Modenesi PJ, Bracarense AQ (2005) Soldagem: fundamentos e tecnologia. Belo Horizonte, UFMG, p 362

    Google Scholar 

  6. Murugan N, Parmar RS (1994) Effects of MIG process parameters on the geometry of the bead in the automatic surfacing of stainless steel. J Mater Process Technol 41(4):381–398

    Article  Google Scholar 

  7. Zhang P, Liu Z (2017) On sustainable manufacturing of Cr-Ni alloy claddings by laser cladding and high-efficiency turning process chain and consequent corrosion resistance. J Clean Prod, Elsevier 161:676–687

    Article  Google Scholar 

  8. Flandinet L, Tedjar F, Ghetta V, Fouletier J (2012) Metals recovering from waste printed circuit boards (WPCBs) using molten salts. J Hazard Mater 213–214:485–490

    Article  Google Scholar 

  9. Peng S, Li T, Li M, Guo Y, Shi J, Tan GZ, Zhang H (2019) An integrated decision model of restoring technologies selection for engine remanufacturing practice. J Clean Prod, Elsevier 206:598–610

    Article  Google Scholar 

  10. Palani PK, Murugan N (2006) Development of mathematical models for prediction of weld bead geometry in cladding by flux cored arc welding. Int J Adv Manuf Technol 30(7–8):669–676

    Article  Google Scholar 

  11. Palani PK, Murugan N (2007) Optimization of weld bead geometry for stainless steel claddings deposited by FCAW. J Mater Process Technol 190(1–3):291–299

    Article  Google Scholar 

  12. Kannan T, Murugan N (2006) Effect of flux cored arc welding process parameters on duplex stainless steel clad quality. J Mater Process Technol 176(1–3):230–239

    Article  Google Scholar 

  13. Dupont JN, Marder AR (1996) Dilution in single pass arc welds. Metall Mater Trans B 27(3):481–489

    Article  Google Scholar 

  14. Banovic SW, Dupont JN, Marder AR (2002) Dilution and microsegregation in dissimilar metal welds between super austenitic stainless steel and nickel base alloys. Sci Technol Weld Join 7(6):374–383. https://doi.org/10.1179/136217102225006804

    Article  Google Scholar 

  15. Gittos MF, Gooch TG (1996) Effect of iron dilution on corrosion resistance of Ni-Cr-Mo alloy cladding. Br Corros J UK 31(4):309–314. https://doi.org/10.1179/bcj.1996.31.4.309

    Article  Google Scholar 

  16. Shahi AS, Pandey S (2006) Prediction of dilution in GMA and UGMA stainless steel single layer cladding using response surface methodology. Sci Technol Weld Join 11(6):634–640

    Article  Google Scholar 

  17. Balasubramanian V, Lakshminarayanan AK, Varahamoorthy R, Babu S (2009) Application of response surface methodology to prediction of dilution in plasma transferred arc hardfacing of stainless steel on carbon steel. Int J Iron Steel Res 16(1):44–53

    Article  Google Scholar 

  18. Shahi AS, Pandey S (2008) Modelling of the effects of welding conditions on dilution of stainless steel claddings produced by gas metal arc welding procedures. J Mater Process Technol 196(1–3):339–344

    Article  Google Scholar 

  19. Black JT, Hunter SL (2003) Lean manufacturing and cell design. Society of Manufacturing Engineers, Chicago

    Google Scholar 

  20. Piercy N, Rich N (2009) Lean transformation in the pure service environment: the case of the call service centre. Int J Oper Prod Manag 29(1):54–76

    Article  Google Scholar 

  21. de Almeida FA, Santos ACO, de Paiva AP, Gomes GF, Gomes JHF (2020) Multivariate Taguchi loss function optimization based on principal components analysis and normal boundary intersection. Eng Comput, Springer. https://doi.org/10.1007/s00366-020-01122-8

  22. Gomes JHF, Paiva AP, Costa SC, Balestrassi PP, Paiva EJ (2013) Weighted multivariate mean square error for processes optimization: a case study on flux-cored arc welding for stainless steel claddings. Eur J Oper Res, Elsevier 226:522–535

    Article  Google Scholar 

  23. de Freitas GJH et al (2011). Otimização de múltiplos objetivos na soldagem de revestimento de chapas de aço carbono ABNT 1020 utilizando arame tubular inoxidável austenítico. Soldag. insp. (Impr.), São Paulo 16(3):232–342

  24. Jeffus L (2004) Welding: principles and applications, 5th edn. Delmar Learning, Australia, p 904

    Google Scholar 

  25. Rodrigues LO, Paiva AP, Costa SC (2008) Otimização do processo de soldagem com eletrodo tubular através da análise da geometria do cordão de solda. Soldagem & Inspeção 13(2):118–127

    Google Scholar 

  26. Paiva EJ, Rodrigues LO, Costa SC, Paiva AP, Balestrassi PP (2010) Otimização do processo de soldagem FCAW usando o Erro Quadrático Médio Multivariado. Soldag insp 15(1):31–40

    Article  Google Scholar 

  27. Bryman A (1989) Research methods and organization studies (contemporary social research), 1st edn. Routledge, London

    Google Scholar 

  28. Valdenebro, Meseguer JL, Portolés A, Conesa E (2018) Electrical parameters optimisation on welding geometry in the 6063-T alloy using the Taguchi methods. Int J Adv Manuf Technol 98:2449–2460

    Article  Google Scholar 

  29. Braguine TB, DE Alcântara DS, Castro CAC, DOS Santos GHR (2018) Influence of welding parameters on aluminum tubes AA6063 by the CDFW process | [Influência dos parâmetros de soldagem em tubos de alumínio AA6063 pelo processo CDFW]. Soldag insp 23(1):3–16

    Article  Google Scholar 

  30. Valdenebro, Meseguer JL, Portolés E Oñoro J (2016) Numerical study of TTP curves upon welding of 6063-T5 aluminium alloy and optimization of welding process parameters by Taguchi’s method. Indian J Eng Mater Sci 23:341–348

    Google Scholar 

  31. Costa DMD, Brito TG, Paiva AP, Leme RC, Balestrassi PP (2016) A normal boundary intersection with multivariate mean square error approach for dry end milling process optimization of the AISI 1045 steel. J Clean Prod 135:1658–1672

  32. Montgomery DC (2013) Design and analysis of experiments, 8th. edn [S.l.]. John Wiley & Sons, Inc, Hoboken

  33. Singh D, Rao PV (2007) A surface roughness prediction model for hard turning process. Int J Adv Manuf Technol, Springer 32:1115–1124

    Article  Google Scholar 

  34. Myers RH, Montgomery D (2002) Response surface methodology: process and product optimization using designed experiments, 2nd edn. [S.l.]. John Wiley & Sons, Inc, Hoboken

  35. Torres AF, Rocha FB, Almeida FA, Gomes JHF, Paiva AP, Balestrassi PP (2020) Multivariate stochastic optimization approach applied in a flux-cored arc welding process. IEEE Access 8:61267–61276

    Article  Google Scholar 

  36. Lu Y, Xu Z (2017) Recycling non-leaching gold from gold-plated memory cards: parameters optimization, experimental verification, and mechanism analysis. J Clean Prod, Elsevier 162:1518–1526

    Article  Google Scholar 

  37. Nasiri R, Arsalani N (2018) Synthesis and application of 3D graphene nanocomposite for the removal of cationic dyes from aqueous solutions: response surface methodology design. J Clean Prod, Elsevier 190:63–71

    Article  Google Scholar 

  38. Almeida FA, Gomes GF, Paula VR, Corrêa JE, Paiva AP, Gomes JHF, Turrioni JB et al (2018) A weighted mean square error approach to the robust optimization of the surface roughness in an AISI 12L14 free-machining steel-turning process. J Mech Eng, Strojniški vestnik 64(3):147–156

    Google Scholar 

  39. Myers RH, Montgomery DC, Anderson-Cook CM (2009) Response surface methodology: process and product optimization using designed experiments, 3rd edn. Wiley, New York

    MATH  Google Scholar 

  40. Gaudêncio JHD, Almeida FA, Turrioni JB, Quinino RC, Balestrassi PP, Paiva AP (2019) A multiobjective optimization model for machining quality in the AISI 12L14 steel turning process using fuzzy multivariate mean square error. Precis Eng, Elsevier 56:303–320

    Article  Google Scholar 

  41. Belinato G, Almeida FA, Paiva AP, Gomes JHF, Balestrassi PP, Rosa PARC (2019) A multivariate normal boundary intersection PCA-based approach to reduce dimensionality in optimization problems for LBM process. Eng Comput, Springer 35:1533–1544

    Article  Google Scholar 

  42. Das I, Dennis JE (1998) Normal-boundary intersection: a new method for generating the Pareto surface in nonlinear multicriteria optimization problems. SIAM J Optim 8:631–657

    Article  MathSciNet  Google Scholar 

  43. Oujebbour F, Habbal A, Ellaia R (2013) Optimization of concurrent criteria in the stamping process. In: Proceedings of 2013 International Conference on Industrial Engineering and Systems Management (IESM), Rabat, pp 1–10

    Google Scholar 

  44. Moura D, Barcelos V, Samanamud GRL, França AB, Lofrano R, Loures CCA, Naves LLR, Amaral MS, Naves FL (2018) Normal boundary intersection applied as multivariate and multiobjective optimization in the treatment of amoxicillin synthetic solution. Environ Monit Assess 190(3):140

    Article  Google Scholar 

  45. Naves FL, de Paula TI, Balestrassi PP, Braga WLM, Sawhney RS, de Paiva AP (2017) Multivariate normal boundary intersection based on rotated factor scores: a multiobjective optimization method for methyl orange treatment. J Clean Prod Elsevier 143:413–439

    Article  Google Scholar 

  46. Myers RH, Montgomery DC, Vining GG, Robinson TJ (2010) Generalized linear models with applications in engineering and the sciences

  47. Johnson RA, Wichern DW (2007) Applied multivariate statistical analysis, 6th edn. Prentice Hall, Upper Saddle River

  48. Hair JF, Black WC, Babin BJ, Anderson RE, Tatham RL (2006) Multivariate data analysis 6th Edition. Pearson Prentice Hall, Upper Saddle River

    Google Scholar 

  49. King G How not to lie with statistics [Online] Available in < https://gking.harvard.edu/files/mist.pdf>: Access in: [August 23, 2020]

  50. Paiva AP, Paiva EJ, Ferreira JR, Balestrassi PP, Costa SC (2009) A multivariate mean square error optimization of AISI 52100 hardened steel turning. Int J Adv Manuf Technol 43:631–643

    Article  Google Scholar 

Download references

Acknowledgments

The authors would like to thank the Brazilian agencies of CAPES, CNPq, and FAPEMIG for supporting this research.

Author information

Authors and Affiliations

  1. Institute of Industrial Engineering and Management, Federal University of Itajubá, Itajubá, MG, Brazil

    Eduardo Rivelino Luz, Estevão Luiz Romão, Simone Carneiro Streitenberger, José Henrique Freitas Gomes, Anderson Paulo de Paiva & Pedro Paulo Balestrassi

Authors
  1. Eduardo Rivelino Luz
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Estevão Luiz Romão
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Simone Carneiro Streitenberger
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. José Henrique Freitas Gomes
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Anderson Paulo de Paiva
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Pedro Paulo Balestrassi
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

JHG, APP, and PPB: resources; JHG, APP, ERL, SCS, ELR, and PPB: investigation; ERL, ELR, and SCS: data curation; SCS, ELR, and ERL: writing—original draft preparation; ELR, SCS, and ERL: writing—review and editing; JHG, APP, ERL, SCS, ELR, and PPB: visualization; APP, PPB, and JHG: supervision. All of the authors have read and agreed to publish this version of the manuscript.

Corresponding author

Correspondence to Eduardo Rivelino Luz.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

Not applicable.

Consent to participate

Not applicable.

Consent for publication

Not applicable.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Luz, E.R., Romão, E.L., Streitenberger, S.C. et al. A new multiobjective optimization with elliptical constraints approach for nonlinear models implemented in a stainless steel cladding process. Int J Adv Manuf Technol 113, 1469–1484 (2021). https://doi.org/10.1007/s00170-020-06581-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-020-06581-3

Keywords

Search

Navigation