Skip to main content
Log in

A new ensemble modeling approach for reliability-based design optimization of flexure-based bridge-type amplification mechanisms

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

Abstract

Reliability-based design optimization (RBDO) for the design process of the flexure-based bridge-type amplification mechanisms (FBTAMs) relies on an accurate surrogate model. At present, ensemble modeling approaches have been widely used. However, existing ensemble modeling approaches for the RBDO have not considered the model form selection in the process modeling, which leads to an inaccurate quality estimation. Aiming at addressing the drawback of existing ensemble modeling approaches for the RBDO, a new ensemble modeling approach is proposed. The stepwise model selection strategy is adopted where redundant model(s) will be eliminated before constructing an ensemble model. The proposed ensemble modeling approach is applied to a typical FBTAM to illustrate its effectiveness. Results revealed that the proposed ensemble modeling approach has a higher accuracy compared with existing ensemble modeling approaches, and thus reached a better RBDO solution.

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

Similar content being viewed by others

References

  1. Howell LL, Magleby SP, Olsen BM (2013) Handbook of compliant mechanisms. Wiley, New York

    Book  Google Scholar 

  2. Xu QS (2018) Micromachines for biological micromanipulation. Springer, Cham

    Book  Google Scholar 

  3. Hu C, Youn BD, Wang PF (2018) Engineering design under uncertainty and health prognostics. Springer, Piscataway

    MATH  Google Scholar 

  4. Abebe M, Park JW, Kang BS (2017) Reliability-based robust process optimization of multi-point dieless forming for product defect reduction. Int J Adv Manuf Technol 89(1-4):1223–1234

    Article  Google Scholar 

  5. Ibrahim MH, Kharmanda G, Charki A (2015) Reliability-based design optimization for fatigue damage analysis. Int J Adv Manuf Technol 76:1021–1030

    Article  Google Scholar 

  6. Fan XN, Wang PF, Hao FF (2019) Reliability-based design optimization of crane bridges using Kriging-based surrogate models. Struct Multidiscip Optim 59(3):993–1005

    Article  Google Scholar 

  7. Li X, Gong CL, Gu LX, Jing Z, Fang H, Gao RC (2019) A reliability-based optimization method using sequential surrogate model and Monte Carlo simulation. Struct Multidiscip Optim 59(2):439–460

    Article  MathSciNet  Google Scholar 

  8. Fang JG, Gao YK, Sun GY, Li Q (2013) Multiobjective reliability-based optimization for design of a vehicledoor. Finite Elements in Analysis & Design 67(5):13–21

    Article  Google Scholar 

  9. Xiao NC, Zuo MJ, Zhou C (2018) Zhou CN 2018 A new adaptive sequential sampling method to construct surrogate models for efficient reliability analysis. Reliab Eng Syst Saf 169:330–338

    Article  Google Scholar 

  10. Fang YD, Zhan ZF, Yang JQ, Liu X (2017) A mixed-kernel-based support vector regression model for automotive body design optimization under uncertainty. ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems 3(4):1–9

    Google Scholar 

  11. Zhu P, Zhang Y, Chen GL (2011) Metamodeling development for reliability-based design optimization of automotive body structure. Comput Ind 62(7):729–741

    Article  Google Scholar 

  12. Yin HF, Wen GL, Fang HB, Qing QX, Kong XZ, Xiao JR, Liu ZB (2014) Multiobjective crashworthiness optimization design of functionally graded foam-filled tapered tube based on dynamic ensemble metamodel. Mater Des 55:747–757

    Article  Google Scholar 

  13. Gu XG, Lu JW, Wang HZ (2015) Reliability-based design optimization for vehicle occupant protection system based on ensemble of metamodels. Struct Multidiscip Optim 51(2):533–546

    Article  Google Scholar 

  14. Shi RH, Liu L, Long T, Liu J (2016) An efficient ensemble of radial basis functions method based on quadratic programming. Eng Optim 48(7):1202–1225

    Article  MathSciNet  Google Scholar 

  15. Zhou XJ, Ma YZ, Tu YL, Feng Y (2013) Ensemble of surrogates for dual response surface modeling in robust parameter design. Qual Reliab Eng Int 29(2):173–197

    Article  Google Scholar 

  16. Li XK, Du JG, Chen ZZ, Ming YW, Cao Y, He WB, Ma J (2018) Reliability-based NC milling parameters optimization using ensemble metamodel. Int J Adv Manuf Technol 97:3359–3369

    Article  Google Scholar 

  17. Rajagopal R, Castillo ED (2005) Model-robust process optimization using bayesian model averaging. Technometrics 47(2):152–163

    Article  MathSciNet  Google Scholar 

  18. Ng SH (2010) A Bayesian model-averaging approach for multiple-response optimization. J Qual Technol 42(1):52–68

    Article  Google Scholar 

  19. Wan W, Birch JB (2011) A semiparametric technique for the multi-response optimization problem. Qual Reliab Eng Int 27(1):47–59

    Article  Google Scholar 

  20. Bhosekar A, Ierapetritou M (2018) Advances in surrogate based modeling, feasibility analysis, and optimization: a review. Comput Chem Eng 108:250–267

    Article  Google Scholar 

  21. Wang DL, Zhang WY, Bakhai (2010) A comparison of Bayesian model averaging and stepwise methods for model selection in logistic regression. Stat Med 23(22):3451–3467

    Article  Google Scholar 

  22. Draper NR, Smith H (1998) Applied regression analysis. Wiley, Hoboken

    Book  Google Scholar 

  23. Viana FAC, Haftka RT, Steffen V (2009) Multiple surrogates: how cross-validation errors can help us to obtain the best predictor. Struct Multidiscip Optim 39(4):439–457

    Article  Google Scholar 

  24. Goel T, Haftka RT, Shyy W, Queipo NV (2007) Ensemble of surrogates. Struct Multidiscip Optim 33(3):199–216

    Article  Google Scholar 

  25. Ouyang LH, Zhou DQ, Ma YZ, Tu YL (2018) Ensemble modeling based on 0–1 programming in micro-manufacturing process. Comput Ind Eng 123:242–253

    Article  Google Scholar 

  26. Aruniit A, Kers J, Goljandin D, Saarna M, Tall K, Majak J, Herranen H (2011) Particulate filled composite plastic materials from recycled glass fibre reinforced plastics. Mater Sci (Medžiagotyra) 17(3):276–281

    Google Scholar 

  27. Aruniit A, Ker J, Majak J, Krumme A, Tall K (2012) Influence of hollow glass microspheres on the mechanical and physical properties and cost of particle reinforced polymer composites. Proc Estonian Acad Sci 61(3):160–165

    Article  Google Scholar 

  28. Lee DH, Jeong IJ, Kim KJ (2018) A desirability function method for optimizing mean and variability of multiple responses using a posterior preference articulation approach. Qual Reliab Eng Int 34(3):360–376

    Article  Google Scholar 

  29. He Z, Zhu PF, Park SH (2012) A robust desirability function method for multi-response surface optimization considering model uncertainty. Eur J Oper Res 221(1):241–247

    Article  MathSciNet  Google Scholar 

  30. Goethals PL, Cho BR (2011) Reverse programming the optimal process mean problem to identify a factor space profile. Eur J Oper Res 215(1):204–217

    Article  Google Scholar 

  31. Derringer GC (1994) A balancing act: optimizing a product’s properties. Qual Prog 27(6):51–58

    Google Scholar 

  32. Youn BD, Choi KK, Yang RJ, Gu L (2004) Reliability-based design optimization for crashworthiness of vehicle side impact. Struct Multidiscip Optim 26(3-4):272–283

    Article  Google Scholar 

  33. Hu JF, Xu GY, Hao YZ (2014) Optimization design of a compound bridge-type micro-platform based on dynamic characteristics. Trans Chin Soc Agric Mach 45(1):306–312

    Google Scholar 

  34. Polit S, Dong JY (2009) Design of high-bandwidth high-precision flexure-based nanopositioning modules. J Manuf Syst 28(2-3):71–77

    Article  Google Scholar 

  35. Varoquaux G (2017) Cross-validation failure: small sample sizes lead to large error bars. Neuroimage 180(2018):68–77

    Google Scholar 

Download references

Funding

This research is supported by the National Natural Science Foundation of China (71811540414, 71702072, 71573115); the National Social Science Fund of China (19BJY094); the Natural Science Foundation for Jiangsu Institutions grant numbers (BK20170810); the Ministry of education of Humanities and Social Science Planning Fund (18YJA630008); the Fundamental Research Funds for the Central Universities (56XBB19001); the Alberta Innovative-Technologies Future, Canada; and the China Scholarship Council, China (201506840098, 201806830105).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Yuejian Chen.

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

Wan, L., Chen, H., Ouyang, L. et al. A new ensemble modeling approach for reliability-based design optimization of flexure-based bridge-type amplification mechanisms. Int J Adv Manuf Technol 106, 47–63 (2020). https://doi.org/10.1007/s00170-019-04506-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-019-04506-3

Keywords

Navigation