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Deep Learning Based 3D Volume Correlation for Additive Manufacturing Using High-Resolution Industrial X-Ray Computed Tomography

  • Quality control in Additive Manufacturing (AM) is vital for industrial applications in areas such as the automotive, medical, and aerospace sectors. Geometric inaccuracies caused by shrinkage and deformations can compromise the life and performance of additively manufactured components. Such deviations can be quantified using Digital Volume Correlation (DVC), which compares the Computer-Aided Design (CAD) model with the X-ray Computed Tomography (XCT) geometry of the components produced. However, accurate registration between the two modalities is challenging due to the absence of a ground truth or reference deformation field. In addition, the extremely large data size of high-resolution XCT volumes makes computation difficult. In this work, we present a deep learning-based approach for estimating voxel-wise deformations between CAD and XCT volumes. Our method uses a dynamic patch-based processing strategy to handle high-resolution volumes. In addition to the Dice score, we introduce aQuality control in Additive Manufacturing (AM) is vital for industrial applications in areas such as the automotive, medical, and aerospace sectors. Geometric inaccuracies caused by shrinkage and deformations can compromise the life and performance of additively manufactured components. Such deviations can be quantified using Digital Volume Correlation (DVC), which compares the Computer-Aided Design (CAD) model with the X-ray Computed Tomography (XCT) geometry of the components produced. However, accurate registration between the two modalities is challenging due to the absence of a ground truth or reference deformation field. In addition, the extremely large data size of high-resolution XCT volumes makes computation difficult. In this work, we present a deep learning-based approach for estimating voxel-wise deformations between CAD and XCT volumes. Our method uses a dynamic patch-based processing strategy to handle high-resolution volumes. In addition to the Dice score, we introduce a Binary Difference Map (BDM) that quantifies voxel-wise mismatches between binarized CAD and XCT volumes to evaluate the accuracy of the registration. Our approach shows a 9.2% improvement in the Dice score and a 9.9% improvement in the voxel match rate compared to classic DVC methods, while reducing the interaction time from days to minutes. This work sets the foundation for deep learning-based DVC methods to generate compensation meshes that can then be used in closed-loop correlations during the AM production process. Such a system would be of great interest to industry, as it would make the manufacturing process more reliable and efficient, saving time and material.zeige mehrzeige weniger

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Metadaten
Autor*innen:Keerthana ChandORCiD, Tobias FritschORCiD, Bardia HejaziORCiD, Konstantin PokaORCiD, Giovanni BrunoORCiD
Dokumenttyp:Buchkapitel
Veröffentlichungsform:Verlagsliteratur
Sprache:Englisch
Titel des übergeordneten Werkes (Englisch):Frontiers in Artificial Intelligence and Applications
Jahr der Erstveröffentlichung:2025
Organisationseinheit der BAM:8 Zerstörungsfreie Prüfung
8 Zerstörungsfreie Prüfung / 8.5 Röntgenbildgebung
Veröffentlichende Institution:Bundesanstalt für Materialforschung und -prüfung (BAM)
Verlag:IOS Press
Jahrgang/Band:413
Erste Seite:5368
Letzte Seite:5375
DDC-Klassifikation:Technik, Medizin, angewandte Wissenschaften / Ingenieurwissenschaften / Ingenieurwissenschaften und zugeordnete Tätigkeiten
Freie Schlagwörter:Deep learning
Themenfelder/Aktivitätsfelder der BAM:Material
Material / Additive Fertigung
DOI:10.3233/FAIA251475
URN:urn:nbn:de:kobv:b43-646293
ISSN:0922-6389
Verfügbarkeit des Dokuments:Datei für die Öffentlichkeit verfügbar ("Open Access")
Lizenz (Deutsch):License LogoCreative Commons - CC BY-NC - Namensnennung - Nicht kommerziell 4.0 International
Datum der Freischaltung:10.11.2025
Referierte Publikation:Nein
Schriftenreihen ohne Nummerierung:Wissenschaftliche Artikel der BAM
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