Details
| Original language | English |
|---|---|
| Title of host publication | Towards Sustainable Customization |
| Subtitle of host publication | Bridging Smart Products and Manufacturing Systems - Proceedings of the 8th Changeable, Agile, Reconfigurable and Virtual Production Conference CARV 2021 and 10th World Mass Customization and Personalization Conference MCPC 2021 |
| Editors | Ann-Louise Andersen, Rasmus Andersen, Thomas Ditlev Brunoe, Maria Stoettrup Schioenning Larsen, Kjeld Nielsen, Alessia Napoleone, Stefan Kjeldgaard |
| Publisher | Springer Science and Business Media Deutschland GmbH |
| Pages | 653-660 |
| Number of pages | 8 |
| ISBN (electronic) | 978-3-030-90700-6 |
| ISBN (print) | 978-3-030-90699-3 |
| Publication status | Published - 2022 |
| Event | 8th Changeable, Agile, Reconfigurable and Virtual Production Conference, CARV 2021 and 10th World Mass Customization and Personalization Conference, MCPC 2021 - Aalborg, Denmark Duration: 1 Nov 2021 → 2 Nov 2021 |
Publication series
| Name | Lecture Notes in Mechanical Engineering |
|---|---|
| ISSN (Print) | 2195-4356 |
| ISSN (electronic) | 2195-4364 |
Abstract
During product development, many decisions have to be made which affect the entire product life cycle. Especially in early phases in product development these decisions are subject to uncertainties when involving customer specific requirements. To support the designer in developing robust products that are insensitive to uncertainty, this paper compares two opposing approaches for modeling and assessing uncertainties: an explicit method in form of a Bayesian decision network with utility functions and an implicit method in form of a robust numerical optimization with sampling of a model-based product representation. Hence, a quantitative and qualitative comparison is provided regarding the modeling expense and the usability in early design stages. Regarding the integration into the product development process we propose a combination of the two approaches as complementary tools.
Keywords
- Bayesian Decision Network, Comparative study, Decision-making under uncertainty, Robust numerical optimization, Robust product design
ASJC Scopus subject areas
- Engineering(all)
- Automotive Engineering
- Engineering(all)
- Aerospace Engineering
- Engineering(all)
- Mechanical Engineering
- Chemical Engineering(all)
- Fluid Flow and Transfer Processes
Cite this
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Towards Sustainable Customization: Bridging Smart Products and Manufacturing Systems - Proceedings of the 8th Changeable, Agile, Reconfigurable and Virtual Production Conference CARV 2021 and 10th World Mass Customization and Personalization Conference MCPC 2021. ed. / Ann-Louise Andersen; Rasmus Andersen; Thomas Ditlev Brunoe; Maria Stoettrup Schioenning Larsen; Kjeld Nielsen; Alessia Napoleone; Stefan Kjeldgaard. Springer Science and Business Media Deutschland GmbH, 2022. p. 653-660 (Lecture Notes in Mechanical Engineering).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Implicit and Explicit Modeling of Uncertainty in Early Design Stages of Product Design
T2 - 8th Changeable, Agile, Reconfigurable and Virtual Production Conference, CARV 2021 and 10th World Mass Customization and Personalization Conference, MCPC 2021
AU - Plappert, Stefan
AU - Wolniak, Philipp
AU - Gembarski, Paul Christoph
AU - Lachmayer, Roland
PY - 2022
Y1 - 2022
N2 - During product development, many decisions have to be made which affect the entire product life cycle. Especially in early phases in product development these decisions are subject to uncertainties when involving customer specific requirements. To support the designer in developing robust products that are insensitive to uncertainty, this paper compares two opposing approaches for modeling and assessing uncertainties: an explicit method in form of a Bayesian decision network with utility functions and an implicit method in form of a robust numerical optimization with sampling of a model-based product representation. Hence, a quantitative and qualitative comparison is provided regarding the modeling expense and the usability in early design stages. Regarding the integration into the product development process we propose a combination of the two approaches as complementary tools.
AB - During product development, many decisions have to be made which affect the entire product life cycle. Especially in early phases in product development these decisions are subject to uncertainties when involving customer specific requirements. To support the designer in developing robust products that are insensitive to uncertainty, this paper compares two opposing approaches for modeling and assessing uncertainties: an explicit method in form of a Bayesian decision network with utility functions and an implicit method in form of a robust numerical optimization with sampling of a model-based product representation. Hence, a quantitative and qualitative comparison is provided regarding the modeling expense and the usability in early design stages. Regarding the integration into the product development process we propose a combination of the two approaches as complementary tools.
KW - Bayesian Decision Network
KW - Comparative study
KW - Decision-making under uncertainty
KW - Robust numerical optimization
KW - Robust product design
UR - http://www.scopus.com/inward/record.url?scp=85119455813&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-90700-6_74
DO - 10.1007/978-3-030-90700-6_74
M3 - Conference contribution
AN - SCOPUS:85119455813
SN - 978-3-030-90699-3
T3 - Lecture Notes in Mechanical Engineering
SP - 653
EP - 660
BT - Towards Sustainable Customization
A2 - Andersen, Ann-Louise
A2 - Andersen, Rasmus
A2 - Brunoe, Thomas Ditlev
A2 - Larsen, Maria Stoettrup Schioenning
A2 - Nielsen, Kjeld
A2 - Napoleone, Alessia
A2 - Kjeldgaard, Stefan
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 1 November 2021 through 2 November 2021
ER -