Details
| Originalsprache | Englisch |
|---|---|
| Seiten (von - bis) | 181-186 |
| Seitenumfang | 6 |
| Fachzeitschrift | Procedia Manufacturing |
| Jahrgang | 52 |
| Publikationsstatus | Veröffentlicht - 24 Dez. 2020 |
| Veranstaltung | 5th International Conference on System-Integrated Intelligence - Bremen, Deutschland Dauer: 11 Nov. 2020 → 13 Nov. 2020 Konferenznummer: 5 |
Abstract
Enhancing structural components by implementing sensors offers great potential regarding condition monitoring for lifetime analysis, predictive maintenance and automatic adaptation to environmental conditions. This article describes an approach to determining the operational forces applied to the front suspension arm of a car using strain gauges. Since suspension arms are components with free-form surfaces, an analytical calculation of applied forces by means of measured strains is not feasible. Hence, artificial neural networks are applied to approximate the functional relationship. The results reveal how artificial neural networks can be applied to identify load conditions on structural components and, therefore, deliver essential data for condition monitoring.
ASJC Scopus Sachgebiete
- Ingenieurwesen (insg.)
- Wirtschaftsingenieurwesen und Fertigungstechnik
- Informatik (insg.)
- Artificial intelligence
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in: Procedia Manufacturing, Jahrgang 52, 24.12.2020, S. 181-186.
Publikation: Beitrag in Fachzeitschrift › Konferenzaufsatz in Fachzeitschrift › Forschung › Peer-Review
}
TY - JOUR
T1 - Identification of dynamic loads on structural component with artificial neural networks
AU - Altun, Osman
AU - Zhang, Danyang
AU - Siqueira, Renan
AU - Wolniak, Philipp
AU - Mozgova, Iryna
AU - Lachmayer, Roland
N1 - Conference code: 5
PY - 2020/12/24
Y1 - 2020/12/24
N2 - Enhancing structural components by implementing sensors offers great potential regarding condition monitoring for lifetime analysis, predictive maintenance and automatic adaptation to environmental conditions. This article describes an approach to determining the operational forces applied to the front suspension arm of a car using strain gauges. Since suspension arms are components with free-form surfaces, an analytical calculation of applied forces by means of measured strains is not feasible. Hence, artificial neural networks are applied to approximate the functional relationship. The results reveal how artificial neural networks can be applied to identify load conditions on structural components and, therefore, deliver essential data for condition monitoring.
AB - Enhancing structural components by implementing sensors offers great potential regarding condition monitoring for lifetime analysis, predictive maintenance and automatic adaptation to environmental conditions. This article describes an approach to determining the operational forces applied to the front suspension arm of a car using strain gauges. Since suspension arms are components with free-form surfaces, an analytical calculation of applied forces by means of measured strains is not feasible. Hence, artificial neural networks are applied to approximate the functional relationship. The results reveal how artificial neural networks can be applied to identify load conditions on structural components and, therefore, deliver essential data for condition monitoring.
KW - Artificial neural networks
KW - Condition monitoring
KW - Load Identification
KW - Sensor integration
KW - Smart components
UR - http://www.scopus.com/inward/record.url?scp=85100805803&partnerID=8YFLogxK
U2 - 10.1016/j.promfg.2020.11.032
DO - 10.1016/j.promfg.2020.11.032
M3 - Conference article
AN - SCOPUS:85100805803
VL - 52
SP - 181
EP - 186
JO - Procedia Manufacturing
JF - Procedia Manufacturing
SN - 2351-9789
T2 - 5th International Conference on System-Integrated Intelligence
Y2 - 11 November 2020 through 13 November 2020
ER -