Heterogeneous sensor data fusion for multiscale, shape agnostic flaw detection in laser powder bed fusion additive manufacturing

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dc.contributor.authorBevans, Benjaminen
dc.contributor.authorBarrett, Christopheren
dc.contributor.authorSpears, Thomasen
dc.contributor.authorGaikwad, Aniruddhaen
dc.contributor.authorRiensche, Alexen
dc.contributor.authorSmoqi, Ziyaden
dc.contributor.authorHalliday, Harold (Scott)en
dc.contributor.authorRao, Prahaladaen
dc.date.accessioned2023-06-20T19:37:00Zen
dc.date.available2023-06-20T19:37:00Zen
dc.date.issued2023en
dc.description.abstractWe developed and applied a novel approach for shape agnostic detection of multiscale flaws in laser powder bed fusion (LPBF) additive manufacturing using heterogenous in-situ sensor data. Flaws in LPBF range from porosity at the micro-scale (< 100 mu m), layer related inconsistencies at the meso-scale (100 mu m to 1 mm) and geometry-related flaws at the macroscale (> 1 mm). Existing data-driven models are primarily focused on detecting a specific type of LPBF flaw using signals from one type of sensor. Such approaches, which are trained on data from simple cuboid and cylindrical-shaped coupons, have met limited success when used for detecting multiscale flaws in complex LPBF parts. The objective of this work is to develop a heterogenous sensor data fusion approach capable of detecting multiscale flaws across different LPBF part geometries and build conditions. Accordingly, data from an infrared camera, spatter imaging camera, and optical powder bed imaging camera were acquired across separate builds with differing part geometries and orientations (Inconel 718). Spectral graph-based process signatures were extracted from this heterogeneous thermo-optical sensor data and used as inputs to simple machine learning models. The approach detected porosity, layer-level distortion, and geometry-related flaws with statistical fidelity exceeding 93% (F-score).en
dc.description.notesPrahalada Rao acknowledges funding from the Department of Energy (DOE), Office of Science, under Grant number DE-SC0021136, and the National Science Foundation (NSF) [Grant numbers CMMI 1752069/CMMI 2309483,CMMI-1719388, CMMI-1920245, CMMI-1739696, PFI-TT 2044710, ECCS 2020246] for funding his research programme. H. Scot Halliday acknowledges funding from the NSF for Award #1840138 funding the NTU Center for Advanced Manufacturing.en
dc.description.sponsorshipDepartment of Energy (DOE), Office of Science [DE-SC0021136]; National Science Foundation (NSF) [CMMI 1752069, CMMI 2309483, CMMI-1719388, CMMI-1920245, CMMI-1739696, PFI-TT 2044710, ECCS 2020246]; NSF [1840138]en
dc.description.versionPublished versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.doihttps://doi.org/10.1080/17452759.2023.2196266en
dc.identifier.eissn1745-2767en
dc.identifier.issn1745-2759en
dc.identifier.issue1en
dc.identifier.othere2196266en
dc.identifier.urihttp://hdl.handle.net/10919/115460en
dc.identifier.volume18en
dc.language.isoenen
dc.publisherTaylor & Francisen
dc.rightsCreative Commons Attribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.subjectAdditive manufacturingen
dc.subjectsensor data fusionen
dc.subjectthermal imagingen
dc.subjectspatter monitoringen
dc.subjectshape agnostic monitoringen
dc.subjectporosityen
dc.titleHeterogeneous sensor data fusion for multiscale, shape agnostic flaw detection in laser powder bed fusion additive manufacturingen
dc.title.serialVirtual and Physical Prototypingen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten

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