Role of multi-response principal component analysis in reliability-based robust design optimization: an application to commercial vehicle design

Juhee Lim, Yong Sok Jang, Hong Suk Chang, Jong Chan Park, Jongsoo Lee

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)

Abstract

The Taguchi method is a widely used conventional approach for robust design that combines experimental design with quality loss functions. However, this method can be only used in a single-response problem. In this study, we propose the use of principal component analysis (PCA) to consider multi-response problems in the Taguchi method and to investigate the influence factor of a cab suspension system. We compute the normalized quality loss for each response and perform PCA to calculate the multi-response performance index. In this study, control factors with three level combinations and noise factors with random sampling from each normal distribution are considered. Additionally, we applied multi-objective reliability based robust design optimization (RBRDO) to accommodate design uncertainties and its data scattering based on rational probabilistic approaches. This is used to develop the reliability assessment and reliability based design optimization and corresponds to an integrated method that accounts for the design robustness in the objective function and reliability in the constraints.

Original languageEnglish
Pages (from-to)785-796
Number of pages12
JournalStructural and Multidisciplinary Optimization
Volume58
Issue number2
DOIs
Publication statusPublished - 2018 Aug 1

Bibliographical note

Funding Information:
Acknowledgements This research is supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF), funded by the Ministry of Science, ICT & Future Planning (2017R1A2B4009606). This work is supported by the Korea Institute of Energy Technology Evaluation and Planning (KETEP) and the Ministry of Trade, Industry & Energy (MOTIE) of Republic of Korea (20163030024420). This research is supported by NGV & Hyundai Motor Group (2016-11-0906).

Publisher Copyright:
© 2018, Springer-Verlag GmbH Germany, part of Springer Nature.

All Science Journal Classification (ASJC) codes

  • Software
  • Control and Systems Engineering
  • Computer Science Applications
  • Computer Graphics and Computer-Aided Design
  • Control and Optimization

Fingerprint

Dive into the research topics of 'Role of multi-response principal component analysis in reliability-based robust design optimization: an application to commercial vehicle design'. Together they form a unique fingerprint.

Cite this