Volume 20, Issue 3 (March 2020)                   Modares Mechanical Engineering 2020, 20(3): 709-719 | Back to browse issues page

XML Persian Abstract Print


Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:

Hassani H, Khodaygan S. Reliability-Based Robust Design Optimization of Mechanical Systems in the Presence of Uncertain Parameters Based on Bayesian Inference. Modares Mechanical Engineering 2020; 20 (3) :709-719
URL: http://mme.modares.ac.ir/article-15-28170-en.html
1- Applied Mechanics Division Department, Mechanical Engineering School, Sharif University of Technology, Tehran, Iran
2- Applied Mechanics Division Department, Mechanical Engineering School, Sharif University of Technology, Tehran, Iran , khodaygan@sharif.edu
Abstract:   (4122 Views)
This competitive commercial space forces designers and manufactures to produce and supply products with high quality and low prices at a desirable level of reliability. On the other hand, during the design and production process, engineers are always faced with uncertainty. In recent years, to encounter these uncertainties and guarantee the quality and reliability of a system subsequently, reliability-based robust design optimization (RBRDO) algorithms have been developed based on robust design optimization (RDO) and reliability-based optimization (RBDO). In practical engineering, uncertainties of some design parameters or variables are epistemic and only a few samples are available for designer. Generally, some of the RBRDO methods ignore the information in the design process. This approach can lead to an enormous error. Other RBRDO methods ignore this valuable information in the design process. This study, a comprehensive RBRDO framework is developed by combining Bayesian reliability analysis and dimensionality reduction method (DRM) using NSGA2-II multi-objective optimization algorithm. For verification of the proposed algorithm, an engineering example is selected and the effects of epistemic uncertainty on objectives are studied. Moreover, the results of the proposed approach are compared with other existing approaches at a specific case of available data about epistemic uncertainty.
Full-Text [PDF 1239 kb]   (1502 Downloads)    
Article Type: Original Research | Subject: Design and manufacture by computer
Received: 2018/12/14 | Accepted: 2019/07/14 | Published: 2020/03/1

References
1. 1- Yadav OP, Bhamare SS, Rathore A. Reliability-based robust design optimization: A multi-objective framework using hybrid quality loss function. Quality and Reliability Engineering International. 2010;26(1):27-41. [Link] [DOI:10.1002/qre.1027]
2. Arora JS. Introduction to optimum design. 2nd Edition. Amsterdam: Elsevier; 2004. [Link] [DOI:10.1016/B978-012064155-0/50004-5]
3. Haldar A, Mahadevan S. Probability, reliability, and statistical methods in engineering design. 1st Volume. New York: Wiley; 2000. [Link]
4. Beyer HG, Sendhoff B. Robust optimization-a comprehensive survey. Computer Methods in Applied Mechanics and Engineering. 2007;196(33-34):3190-3218. [Link] [DOI:10.1016/j.cma.2007.03.003]
5. Park GJ, Lee TH, Lee KH, Hwang KH. Robust design: An overview. AIAA Journal. 2006;44(1):181-191. [Link] [DOI:10.2514/1.13639]
6. Du X, Chen W. Towards a better understanding of modeling feasibility robustness in engineering design. Journal of Mechanical Design. 2000;122(4):385-394. [Link] [DOI:10.1115/1.1290247]
7. Aoues Y, Chateauneuf A. Benchmark study of numerical methods for reliability-based design optimization. Structural and multidisciplinary optimization. 2010;41(2):277-294. [Link] [DOI:10.1007/s00158-009-0412-2]
8. Valdebenito MA, Schuëller GI. A survey on approaches for reliability-based optimization. Structural and Multidisciplinary Optimization. 2010;42(5):645-663. [Link] [DOI:10.1007/s00158-010-0518-6]
9. Yadav OP, Bhamare SS, Rathore A. Reliability‐based robust design optimization: A multi-objective framework using hybrid quality loss function. Quality and Reliability Engineering International. 2010;26(1):27-41. [Link] [DOI:10.1002/qre.1027]
10. Forouzandeh Shahraki A, Noorossana R. Reliability-based robust design optimization: a general methodology using genetic algorithm. Computers & Industrial Engineering. 2014;74:199-207. [Link] [DOI:10.1016/j.cie.2014.05.013]
11. Lobato FS, Da Silva MA, Cavalini Jr AA, Steffen Jr V. Reliability-based robust multi-objective optimization applied to engineering system design. Engineering Optimization. 2019;52(1):1-21. [Link] [DOI:10.1080/0305215X.2019.1577413]
12. Lu H, Zhu Z, Zhang Y. A hybrid approach for reliability-based robust design optimization of structural systems with dependent failure modes. Engineering Optimization. 2019. [Link] [DOI:10.1080/0305215X.2019.1586893]
13. Box GE, Tiao GC. Bayesian inference in statistical analysis. Hoboken: John Wiley & Sons; 2011. [Link]
14. Srivastava R, Deb K. An evolutionary based Bayesian design optimization approach under incomplete information. Engineering Optimization. 2013;45(2):141-165. [Link] [DOI:10.1080/0305215X.2012.661730]
15. Gunawan S, Papalambros PY. A bayesian approach to reliability-based optimization with incomplete information. Journal of Mechanical Design. 2006;128(4):909-918. [Link] [DOI:10.1115/1.2204969]
16. Bastidas-Arteaga E, Soubra AH. Reliability analysis methods. Unknown Location: ALERT Doctoral School; 2014. [Link]
17. Du X. First order and second reliability methods. Rolla: University of Missouri - Rolla; 2005. pp. 1-33. [Link]
18. Zhang Y, Der Kiureghian A. Two improved algorithms for reliability analysis. In: Rackwitz R, Augusti G, Borri A, editors. Reliability and optimization of structural systems. Boston: Springer; 1995. pp. 297-304. [Link] [DOI:10.1007/978-0-387-34866-7_32]

Add your comments about this article : Your username or Email:
CAPTCHA

Send email to the article author


Rights and permissions
Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.