Robust Parameter Design and Optimization for Quality Engineering
İhsan Yanıkoğlu (),
Erinç Albey () and
Serkan Okçuoğlu
Additional contact information
İhsan Yanıkoğlu: Özyegin University
Erinç Albey: Özyegin University
Serkan Okçuoğlu: VESTEL R&D Department
SN Operations Research Forum, 2022, vol. 3, issue 1, 1-36
Abstract:
Abstract This paper proposes a methodology to determine the optimal settings of key decision variables that affect the resilience of an engineering design against uncertainty. Uncertainty in quality engineering is often caused by environmental factors, and scarcity of data due to limitations in the experimentation phase amplifies the level of ambiguity. The proposed robust parameter design and optimization approach utilizes the Taguchi method to find critical variables to be used in the optimization, and it utilizes robust optimization to immunize the obtained solution against uncertainty. To demonstrate our approach, we focus on design optimization of an injection molding product, a refrigerator door cap, made from thermoplastic raw material and its key quality characteristic, warpage. The near-optimal designs found by the robust parameter design and optimization approach are implemented in a real-life manufacturing environment. The numerical experiments show that the new designs significantly improve the warpage quality characteristic and the total production cycle time compared to the current design used in the manufacturing company.
Keywords: Robust optimization; Robust parameter design; Injection molding (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations:
Downloads: (external link)
http://link.springer.com/10.1007/s43069-022-00121-3 Abstract (text/html)
Access to the full text of the articles in this series is restricted.
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:spr:snopef:v:3:y:2022:i:1:d:10.1007_s43069-022-00121-3
Ordering information: This journal article can be ordered from
https://www.springer.com/journal/43069
DOI: 10.1007/s43069-022-00121-3
Access Statistics for this article
SN Operations Research Forum is currently edited by Marco Lübbecke
More articles in SN Operations Research Forum from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().