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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.zeige mehrzeige weniger

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Metadaten
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):License LogoCreative 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
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