Journalartikel - Wissenschaftlicher Artikel | 2026 | Peer reviewed

Multi-Objective Optimization in Injection Molding Simulation: A Preference-Driven Approach with an Adaptive Experimental Design to Investigate the Optimal Solution Region

Autor*innen

Baum, Markus ; Anders, Denis ; Reinicke, Tamara

Zeitschrift

Applied Sciences

DOI

10.3390/app16126148

URL

https://www.mdpi.com/2076-3417/16/12/6148

Weitere Details

Sprache

englisch

Darstellungsform

Text

Band

Ausgabe

Artikelnummer

6148

Schlüsselwörter

NSGA-II , adaptive training data extension , injection molding simulation , optimization , preferred solution identification , response surface methodology

Zitierstile

Baum, M., Anders, D., & Reinicke, T. (2026). Multi-Objective Optimization in Injection Molding Simulation: A Preference-Driven Approach with an Adaptive Experimental Design to Investigate the Optimal Solution Region. Applied Sciences, 16(12), 6148. doi:10.3390/app16126148

In die Zwischenablage kopieren

Baum, M., Anders, D. and Reinicke, T., 2026. Multi-Objective Optimization in Injection Molding Simulation: A Preference-Driven Approach with an Adaptive Experimental Design to Investigate the Optimal Solution Region. Applied Sciences, [online] 16(12), p.6148. Available at: <https://www.mdpi.com/2076-3417/16/12/6148>.

In die Zwischenablage kopieren

Baum, Markus, Anders, Denis & Reinicke, Tamara 2026. Multi-Objective Optimization in Injection Molding Simulation: A Preference-Driven Approach with an Adaptive Experimental Design to Investigate the Optimal Solution Region. Applied Sciences 16, 12, 6148. https://www.mdpi.com/2076-3417/16/12/6148.

American Psychological Association 6th edition (DOI)

Anglia Ruskin University - Harvard

Gesellschaft für Bildung und Forschung in Europa - Harvard