Zitieren Sie bitte immer diesen URN: urn:nbn:de:kobv:b43-547039
Quality Prediction in Directed Energy Deposition Using Artificial Neural Networks Based on Process Signals
- The Directed Energy Deposition process is used in a wide range of applications including the repair, coating or modification of existing structures and the additive manufacturing of individual parts. As the process is frequently applied in the aerospace industry, the requirements for quality assurance are extremely high. Therefore, more and more sensor systems are being implemented for process monitoring. To evaluate the generated data, suitable methods must be developed. A solution, in this context, was the application of artificial neural networks (ANNs). This article demonstrates how measurement data can be used as input data for ANNs. The measurement data were generated using a pyrometer, an emission spectrometer, a camera (Charge-Coupled Device) and a laser scanner. First, a concept for the extraction of relevant features from dynamic measurement data series was presented. The developed method was then applied to generate a data set for the quality prediction of variousThe Directed Energy Deposition process is used in a wide range of applications including the repair, coating or modification of existing structures and the additive manufacturing of individual parts. As the process is frequently applied in the aerospace industry, the requirements for quality assurance are extremely high. Therefore, more and more sensor systems are being implemented for process monitoring. To evaluate the generated data, suitable methods must be developed. A solution, in this context, was the application of artificial neural networks (ANNs). This article demonstrates how measurement data can be used as input data for ANNs. The measurement data were generated using a pyrometer, an emission spectrometer, a camera (Charge-Coupled Device) and a laser scanner. First, a concept for the extraction of relevant features from dynamic measurement data series was presented. The developed method was then applied to generate a data set for the quality prediction of various geometries, including weld beads, coatings and cubes. The results were compared to ANNs trained with process parameters such as laser power, scan speed and powder mass flow. It was shown that the use of measurement data provides additional value. Neural networks trained with measurement data achieve significantly higher prediction accuracy, especially for more complex geometries.…
Autor*innen: | A. Marko, S. Bähring, J. Raute, M. Biegler, Michael RethmeierORCiD |
---|---|
Dokumenttyp: | Zeitschriftenartikel |
Veröffentlichungsform: | Verlagsliteratur |
Sprache: | Englisch |
Titel des übergeordneten Werkes (Englisch): | Applied Sciences |
Jahr der Erstveröffentlichung: | 2022 |
Organisationseinheit der BAM: | 9 Komponentensicherheit |
9 Komponentensicherheit / 9.3 Schweißtechnische Fertigungsverfahren | |
Veröffentlichende Institution: | Bundesanstalt für Materialforschung und -prüfung (BAM) |
Verlag: | MDPI |
Jahrgang/Band: | 12 |
Ausgabe/Heft: | 8 |
Aufsatznummer: | 3955 |
Erste Seite: | 1 |
Letzte Seite: | 13 |
DDC-Klassifikation: | Technik, Medizin, angewandte Wissenschaften / Ingenieurwissenschaften / Ingenieurwissenschaften und zugeordnete Tätigkeiten |
Freie Schlagwörter: | Artificial neural network; DED; Data preparation; Process monitoring; Quality assurance |
Themenfelder/Aktivitätsfelder der BAM: | Material |
Material / Additive Fertigung | |
DOI: | 10.3390/app12083955 |
URN: | urn:nbn:de:kobv:b43-547039 |
Verfügbarkeit des Dokuments: | Datei für die Öffentlichkeit verfügbar ("Open Access") |
Lizenz (Deutsch): | Creative Commons - CC BY - Namensnennung 4.0 International |
Datum der Freischaltung: | 27.04.2022 |
Referierte Publikation: | Ja |
Datum der Eintragung als referierte Publikation: | 08.06.2022 |
Schriftenreihen ohne Nummerierung: | Wissenschaftliche Artikel der BAM |