Skip to main content

Advertisement

Log in

Modeling and optimization of laser shock hole-clinching using response surface methodology and genetic algorithm

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

Abstract

Laser shock hole-clinching is one of mechanical joining processes in which metal foils are joined together based on plastic deformation caused by laser-induced shock wave. In the process, fracture often occurs due to the unreasonable arrangement of laser process parameters, suggesting that it is urgent to seek a feasible way to improve the joint forming quality. However, it consumes a great deal of time and effort to obtain the optimal solution of process parameters from so many different combinations through experiments and numerical simulations. In this study, the mathematical models between laser process parameters and joint forming quality were established through response surface methodology (RSM). A finite element analysis model of laser shock hole-clinching process for pure copper and pre-pierced stainless steel foils was developed, and then it was used to perform the calculation scheme arranged by design of experiments. Analysis of variance (ANOVA) was implemented to evaluate the statistical significance of RSM models and the influence of process parameters on objectives. Multi-objective optimization was carried out to achieve the optimal combination of laser process parameters by using genetic algorithm (GA). It is revealed that the RSM-GA-integrated approach is an effective way to realize the modeling and optimization of laser process parameters for laser shock hole-clinching. The pulsed laser energy (E), number of laser pulses (N), and laser spot diameter (D) are statistically significant, and both the interlock value and the maximum thinning rate are sensitive to these parameters based on ANOVA. The fitting precision analysis indicates that the established RSM models can be used to navigate the design space of variables and predict the actual data with high accuracy. Moreover, it is found that the influence order of laser process parameters on the interlock value from strong to weak is N, E, and D, while this order changes to D, N, and E for the maximum thinning rate. According to the Pareto noninferior solutions and the corresponding values of satisfaction function, the optimal combination is E = 164 mJ, N = 25, and D = 2.1 mm in the given design space. The GA optimization result has been experimentally confirmed.

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

Access this article

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

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14

Similar content being viewed by others

Availability of data and material

Not applicable.

Code availability

Not applicable.

References

  1. Righi G, Ruestes CJ, Stan CV, Ali SJ, Rudd RE, Kawasaki M, Park HS, Meyers MA (2021) Towards the ultimate strength of iron: spalling through laser shock. Acta Mater 215:117072. https://doi.org/10.1016/j.actamat.2021.117072

    Article  Google Scholar 

  2. Groche P, Wohletz S, Brenneis M, Pabst C, Resch F (2014) Joining by forming—a review on joint mechanisms, applications and future trends. J Mater Process Technol 214:1972–1994. https://doi.org/10.1016/j.jmatprotec.2013.12.022

    Article  Google Scholar 

  3. Wang XY, Ji Z, Liu R, Zheng C (2018) Making interlock by laser shock forming. Opt Laser Technol 107:331–336. https://doi.org/10.1016/j.optlastec.2018.06.011

    Article  Google Scholar 

  4. Veenaas S, Wielage H, Vollertsen F (2014) Joining by laser shock forming: realization and acting pressures. Prod Eng Res Devel 8:283–290. https://doi.org/10.1007/s11740-013-0521-z

    Article  Google Scholar 

  5. Wang X, Li XD, Li C, Shen ZB, Ma YJ, Liu HX (2018) Laser shock micro clinching of Al/Cu. J Mater Process Technol 258:200–210. https://doi.org/10.1016/j.jmatprotec.2018.04.005

    Article  Google Scholar 

  6. Li XD, Wang X, Shen ZB, Ma YJ, Liu HX (2019) An experimental study on micro-shear clinching of metal foils by laser shock. Materials 12:1422. https://doi.org/10.3390/ma12091422

    Article  Google Scholar 

  7. Wang J, Wang YQ, Wang S, Lu GX, Zheng C, Ji Z (2021) Experimental and numerical investigation on incremental laser shock clinching for joining three sheets of copper/aluminum/stainless steel. Opt Laser Technol 141:107141. https://doi.org/10.1016/j.optlastec.2021.107141

    Article  Google Scholar 

  8. Veenaas S, Vollertsen F (2015) Forming behavior during joining by laser induced shock waves. Key Eng Mater 651–653:1451–1456. https://doi.org/10.4028/www.scientific.net/KEM.651-653.1451

    Article  Google Scholar 

  9. Zheng C, Pan CD, Tian ZR, Zhao XH, Zhao GQ, Ji Z, Song LB (2020) Laser shock induced incremental forming of pure copper foil and its deformation behavior. Opt Laser Technol 121:105785. https://doi.org/10.1016/j.optlastec.2019.105785

    Article  Google Scholar 

  10. Wang XY, Ji Z, Wang JF, You SX, Zheng C, Liu R (2018) An experimental and numerical study on laser shock clinching for joining copper foil and perforated stainless steel sheet. J Mater Process Technol 258:155–164. https://doi.org/10.1016/j.jmatprotec.2018.03.025

    Article  Google Scholar 

  11. You SX, Wang XY, Ji Z, Zheng C, Zhang GF, Liu R (2019) Making line undercut structure by incremental laser shock forming. Int J Precis Eng Manuf 20:1289–1296. https://doi.org/10.1007/s12541-019-00141-w

    Article  Google Scholar 

  12. Lambiase F, Di Ilio A (2013) Optimization of the clinching tools by means of integrated FE modeling and artificial intelligence techniques. Procedia CIRP 12:163–168. https://doi.org/10.1016/j.procir.2013.09.029

    Article  Google Scholar 

  13. Shen XJ, Shukla P, Subramaniyan AK, Zammit A, Swanson P, Lawrence J, Fitzpatrick ME (2020) Residual stresses induced by laser shock peening in orthopaedic Ti-6Al-7Nb alloy. Opt Laser Technol 131:106446. https://doi.org/10.1016/j.optlastec.2020.106446

    Article  Google Scholar 

  14. Wang C, Li KF, Hu XY, Yang HT, Zhou YJ (2021) Numerical study on laser shock peening of TC4 titanium alloy based on the plate and blade model. Opt Laser Technol 142:107163. https://doi.org/10.1016/j.optlastec.2021.107163

    Article  Google Scholar 

  15. Wu JJ, Xu ZH, Qiao HC, Zhao JB, Huang Z (2021) Mechanical properties prediction of superalloy FGH4095 treated by laser shock processing based on machine learning. Mater Lett 297:129970. https://doi.org/10.1016/j.matlet.2021.129970

    Article  Google Scholar 

  16. Davidson MJ, Balasubramanian K, Tagore GRN (2008) Surface roughness prediction of flow-formed AA6061 alloy by design of experiments. J Mater Process Technol 202:41–46. https://doi.org/10.1016/j.jmatprotec.2007.08.065

    Article  Google Scholar 

  17. Bagudanch I, Vives-Mestres M, Sabater M, Garcia-Romeu ML (2017) Polymer incremental sheet forming process: temperature analysis using response surface methodology. Mater Manuf Processes 32:44–53. https://doi.org/10.1080/10426914.2016.1176191

    Article  Google Scholar 

  18. Mostafanezhad H, Menghari HG, Esmaeili S, Shirkharkolaee EM (2018) Optimization of two-point incremental forming process of AA1050 through response surface methodology. Measurement 127:21–28. https://doi.org/10.1016/j.measurement.2018.04.042

    Article  Google Scholar 

  19. Chen C, Zhao SD, Han XL, Cui MC, Fan SQ (2016) Optimization of a reshaping rivet to reduce the protrusion height and increase the strength of clinched joints. J Mater Process Technol 234:1–9. https://doi.org/10.1016/j.jmatprotec.2016.03.006

    Article  Google Scholar 

  20. Eshtayeh M, Hrairi M (2016) Multi objective optimization of clinching joints quality using Grey-based Taguchi method. Int J Adv Manuf Technol 87:233–249. https://doi.org/10.1007/s00170-016-8471-1

    Article  Google Scholar 

  21. Roux E, Bouchard PO (2013) Kriging metamodel global optimization of clinching joining processes accounting for ductile damage. J Mater Process Technol 213:1038–1047. https://doi.org/10.1016/j.jmatprotec.2013.01.018

    Article  Google Scholar 

  22. Oudjene M, Ben-Ayed L, Delamézière A, Batoz JL (2009) Shape optimization of clinching tools using the response surface methodology with moving least-square approximation. J Mater Process Technol 209:289–296. https://doi.org/10.1016/j.jmatprotec.2008.02.030

    Article  Google Scholar 

  23. Wang MH, Xiao GQ, Li Z, Wang JQ (2018) Shape optimization methodology of clinching tools based on Bezier curve. Int J Adv Manuf Technol 94:2267–2280. https://doi.org/10.1007/s00170-017-0987-5

    Article  Google Scholar 

  24. Schwarz C, Kropp T, Kraus C, Drossel W (2020) Optimization of thick sheet clinching tools using principal component analysis. Int J Adv Manuf Technol 106:471–479. https://doi.org/10.1007/s00170-019-04512-5

    Article  Google Scholar 

  25. Fabbro R, Fournier J, Ballard P, Devaux D, Virmont J (1990) Physical study of laser-produced plasma in confined geometry. J Appl Phys 68:775–784. https://doi.org/10.1063/1.346783

    Article  Google Scholar 

  26. Zhang WW, Yao YL (2002) Microscale laser shock processing of metallic components. J Manuf Sci Eng 124:369–378. https://doi.org/10.1115/1.1445149

    Article  Google Scholar 

  27. Wielage H, Vollertsen F (2011) Classification of laser shock forming within the field of high speed forming processes. J Mater Process Technol 211:953–957. https://doi.org/10.1016/j.jmatprotec.2010.07.012

    Article  Google Scholar 

  28. Johnson GR, Cook WH (1983) A constitutive model and data for metals subjected to large strains, high strain rates and high temperatures, 7th International Symposium on Ballistics. Hague, Netherlands, pp 541–547

    Google Scholar 

  29. Zheng C, Sun S, Ji Z, Wang W, Liu J (2010) Numerical simulation and experimentation of micro scale laser bulge forming. Int J Mach Tools Manuf 50:1048–1056. https://doi.org/10.1016/j.ijmachtools.2010.08.012

    Article  Google Scholar 

  30. Myers RH, Montgomery DC, Anderson-Cook CM (2016) Response surface methodology: process and product optimization using designed experiments, 4th edn. Wiley, New Jersey

    MATH  Google Scholar 

  31. Fenske H, Vollertsen F (2019) Laser shock punching: principle and influencing factors. Prod Eng 13:399–407. https://doi.org/10.1007/s11740-019-00886-3

    Article  Google Scholar 

  32. Kumar SP, Elangovan S (2020) Optimization in single point incremental forming of Inconel 718 through response surface methodology. Trans Can Soc Mech Eng 44:148–160. https://doi.org/10.1139/tcsme-2019-0003

    Article  Google Scholar 

  33. Zhou YJ, Lan FC, Huang XH, Chen JQ (2011) Multi-objective optimization of geometry of clinching tools for steel-aluminum blank sheets. Mater Sci Technol 19:86–93. https://doi.org/10.11951/j.issn.1005-0299.20110616

    Article  Google Scholar 

  34. Kramer O (2017) Genetic algorithm essentials. Springer, Cham

    Book  Google Scholar 

  35. Zhou G, Ma ZD, Li GY, Cheng AG, Duan LB, Zhao WZ (2016) Design optimization of a novel NPR crash box based on multi-objective genetic algorithm. Struct Multidisc Optim 54:673–684. https://doi.org/10.1007/s00158-016-1452-z

    Article  Google Scholar 

Download references

Funding

This work is supported by the National Natural Science Foundation of China (No. 52075299), Natural Science Foundation of Shandong Province (No. ZR2020ME149), and the Fundamental Research Funds of Shandong University (2018JC042).

Author information

Authors and Affiliations

Authors

Contributions

Haoyu Yuan: Writing–original draft preparation, investigation. Changdong Pan: Methodology, formal analysis. Libin Song: Resources, writing–review. Guoqun Zhao: Supervision. Chao Zheng: Conceptualization, writing–review and editing.

Corresponding author

Correspondence to Chao Zheng.

Ethics declarations

Conflict of interest

The authors declare no competing interests.

Additional information

Publisher's note

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

Rights and permissions

Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yuan, H., Pan, C., Song, L. et al. Modeling and optimization of laser shock hole-clinching using response surface methodology and genetic algorithm. Int J Adv Manuf Technol 122, 2391–2406 (2022). https://doi.org/10.1007/s00170-022-10056-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-022-10056-y

Keywords

Navigation