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Toward prediction and insight of porosity formation in laser welding: A physics-informed deep learning framework
- The laser welding process is an important manufacturing technology for metallic materials. However, its application is often hindered by the occurrence of porosity defects. By far, an accurate prediction of the porosity defects and an insight into its formation mechanism are still challenging due to the highly nonlinear physics involved. In this paper, we propose a physics-informed deep learning (PIDL) framework by utilizing mechanistic modeling and experimental data to predict the porosity level during laser beam welding of aluminum alloys. With a proper selection of the physical variables (features) concerning the solidification, liquid metal flow, keyhole stability, and weld pool geometry, the PIDL model shows great superiority in predicting the porosity ratio, with a reduction of mean square error by 41 %, in comparison with the conventional DL model trained with welding parameters. Furthermore, the selected variables are fused into dimensionless features with explicit physicalThe laser welding process is an important manufacturing technology for metallic materials. However, its application is often hindered by the occurrence of porosity defects. By far, an accurate prediction of the porosity defects and an insight into its formation mechanism are still challenging due to the highly nonlinear physics involved. In this paper, we propose a physics-informed deep learning (PIDL) framework by utilizing mechanistic modeling and experimental data to predict the porosity level during laser beam welding of aluminum alloys. With a proper selection of the physical variables (features) concerning the solidification, liquid metal flow, keyhole stability, and weld pool geometry, the PIDL model shows great superiority in predicting the porosity ratio, with a reduction of mean square error by 41 %, in comparison with the conventional DL model trained with welding parameters. Furthermore, the selected variables are fused into dimensionless features with explicit physical meanings to improve the interpretability and extendibility of the PIDL model. Based on a well-trained PIDL model, the hierarchical importance of the physical variables/procedures on the porosity formation is for the first time revealed with the help of the Shapley Additive Explanations analysis. The keyhole ratio is identified as the most influential factor in the porosity formation, followed by the downward flow-driven drag force, which offers a valuable guideline for process optimization and porosity minimization.…


| Autor*innen: | Xiangmeng MengORCiD, Marcel BachmannORCiD, Fan Yang, Michael RethmeierORCiD |
|---|---|
| Dokumenttyp: | Zeitschriftenartikel |
| Veröffentlichungsform: | Verlagsliteratur |
| Sprache: | Englisch |
| Titel des übergeordneten Werkes (Englisch): | Acta Materialia |
| Jahr der Erstveröffentlichung: | 2025 |
| Organisationseinheit der BAM: | 9 Komponentensicherheit |
| 9 Komponentensicherheit / 9.3 Schweißtechnische Fertigungsverfahren | |
| Veröffentlichende Institution: | Bundesanstalt für Materialforschung und -prüfung (BAM) |
| Verlag: | Elsevier B.V. |
| Jahrgang/Band: | 286 |
| Aufsatznummer: | 120740 |
| Erste Seite: | 1 |
| Letzte Seite: | 13 |
| DDC-Klassifikation: | Technik, Medizin, angewandte Wissenschaften / Ingenieurwissenschaften / Ingenieurwissenschaften und zugeordnete Tätigkeiten |
| Freie Schlagwörter: | Feature fusion; Hierarchical importance; Laser beam welding; Physics-informed deep learning; Porosity prediction |
| Themenfelder/Aktivitätsfelder der BAM: | Material |
| Material / Degradationsmechanismen | |
| DOI: | 10.1016/j.actamat.2025.120740 |
| URN: | urn:nbn:de:kobv:b43-624708 |
| Verfügbarkeit des Dokuments: | Datei für die Öffentlichkeit verfügbar ("Open Access") |
| Lizenz (Deutsch): | Creative Commons - CC BY-NC-ND - Namensnennung - Nicht kommerziell - Keine Bearbeitungen 4.0 International |
| Datum der Freischaltung: | 30.01.2025 |
| Referierte Publikation: | Ja |
| Datum der Eintragung als referierte Publikation: | 05.05.2025 |
| Paper des Monats: | Ja |
| Schriftenreihen ohne Nummerierung: | Wissenschaftliche Artikel der BAM |


