Gally, Tristan ; Groche, Peter ; Hoppe, Florian ; Kuttich, Anja ; Matei, Alexander ; Pfetsch, Marc E. ; Rakowitsch, Martin ; Ulbrich, Stefan (2024)
Identification of model uncertainty via optimal design of experiments applied to a mechanical press.
In: Optimization and Engineering : International Multidisciplinary Journal to Promote Optimization Theory & Applications in Engineering Sciences, 2022, 23 (1)
doi: 10.26083/tuprints-00023488
Artikel, Zweitveröffentlichung, Verlagsversion
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Kurzbeschreibung (Abstract)
In engineering applications almost all processes are described with the help of models. Especially forming machines heavily rely on mathematical models for control and condition monitoring. Inaccuracies during the modeling, manufacturing and assembly of these machines induce model uncertainty which impairs the controller’s performance. In this paper we propose an approach to identify model uncertainty using parameter identification, optimal design of experiments and hypothesis testing. The experimental setup is characterized by optimal sensor positions such that specific model parameters can be determined with minimal variance. This allows for the computation of confidence regions in which the real parameters or the parameter estimates from different test sets have to lie. We claim that inconsistencies in the estimated parameter values, considering their approximated confidence ellipsoids as well, cannot be explained by data uncertainty but are indicators of model uncertainty. The proposed method is demonstrated using a component of the 3D Servo Press, a multi-technology forming machine that combines spindles with eccentric servo drives.
| Typ des Eintrags: | Artikel |
|---|---|
| Erschienen: | 2024 |
| Autor(en): | Gally, Tristan ; Groche, Peter ; Hoppe, Florian ; Kuttich, Anja ; Matei, Alexander ; Pfetsch, Marc E. ; Rakowitsch, Martin ; Ulbrich, Stefan |
| Art des Eintrags: | Zweitveröffentlichung |
| Titel: | Identification of model uncertainty via optimal design of experiments applied to a mechanical press |
| Sprache: | Englisch |
| Publikationsjahr: | 30 April 2024 |
| Ort: | Darmstadt |
| Publikationsdatum der Erstveröffentlichung: | 2022 |
| Ort der Erstveröffentlichung: | Dordrecht |
| Verlag: | Springer Science |
| Titel der Zeitschrift, Zeitung oder Schriftenreihe: | Optimization and Engineering : International Multidisciplinary Journal to Promote Optimization Theory & Applications in Engineering Sciences |
| Jahrgang/Volume einer Zeitschrift: | 23 |
| (Heft-)Nummer: | 1 |
| DOI: | 10.26083/tuprints-00023488 |
| URL / URN: | https://tuprints.ulb.tu-darmstadt.de/23488 |
| Zugehörige Links: | |
| Herkunft: | Zweitveröffentlichung DeepGreen |
| Kurzbeschreibung (Abstract): | In engineering applications almost all processes are described with the help of models. Especially forming machines heavily rely on mathematical models for control and condition monitoring. Inaccuracies during the modeling, manufacturing and assembly of these machines induce model uncertainty which impairs the controller’s performance. In this paper we propose an approach to identify model uncertainty using parameter identification, optimal design of experiments and hypothesis testing. The experimental setup is characterized by optimal sensor positions such that specific model parameters can be determined with minimal variance. This allows for the computation of confidence regions in which the real parameters or the parameter estimates from different test sets have to lie. We claim that inconsistencies in the estimated parameter values, considering their approximated confidence ellipsoids as well, cannot be explained by data uncertainty but are indicators of model uncertainty. The proposed method is demonstrated using a component of the 3D Servo Press, a multi-technology forming machine that combines spindles with eccentric servo drives. |
| Freie Schlagworte: | Model uncertainty, Model inadequacy, Optimal design of experiments, Parameter identification, Sensor placement, Forming machines |
| Status: | Verlagsversion |
| URN: | urn:nbn:de:tuda-tuprints-234889 |
| Sachgruppe der Dewey Dezimalklassifikatin (DDC): | 500 Naturwissenschaften und Mathematik > 510 Mathematik 600 Technik, Medizin, angewandte Wissenschaften > 620 Ingenieurwissenschaften und Maschinenbau |
| Fachbereich(e)/-gebiet(e): | 16 Fachbereich Maschinenbau 16 Fachbereich Maschinenbau > Institut für Produktionstechnik und Umformmaschinen (PtU) 04 Fachbereich Mathematik 04 Fachbereich Mathematik > Optimierung |
| Hinterlegungsdatum: | 30 Apr 2024 12:54 |
| Letzte Änderung: | 02 Mai 2024 10:17 |
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| Suche nach Titel in: | TUfind oder in Google |
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