Improved multi-objective decision-making in manufacturing processes through uncertainty quantification and robust pareto front modelling
Abstract Manufacturing processes often exhibit complex relationships between input parameters and output responses, posing challenges for optimization and decision-making. Surrogate models are commonly employed to approximate these relationships, enabling efficient exploration of the design space. H...
Saved in:
| Main Authors: | Arne De Temmerman, Mathias Verbeke |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-04-01
|
| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-025-97508-z |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Integrated Index for assessing operational uncertainty in manufacturing for decision-making
by: Matolwandile M. Mtotywa, et al.
Published: (2025-07-01) -
Robustimizer: A graphical user interface application for efficient uncertainty quantification, robust optimization, and reliability-based optimization of processes and designs
by: Omid Nejadseyfi
Published: (2025-05-01) -
Improvements of particle filter optimization algorithm for robust optimization under different types of uncertainties
by: Éva Kenyeres, et al.
Published: (2025-01-01) -
Bi-level Hybrid Uncertainty Quantification in Fatigue Analysis: S-N Curve Approach
by: Raphael Basilio Pires Nonato
Published: (2020-09-01) -
Robust confinement state classification with uncertainty quantification through ensembled data-driven methods
by: Yoeri Poels, et al.
Published: (2025-01-01)