Heterogeneous sensor data fusion for multiscale, shape agnostic flaw detection in laser powder bed fusion additive manufacturing
We 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 µm), layer related inconsistencies at the meso-sc...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2023-12-01
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| Series: | Virtual and Physical Prototyping |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/17452759.2023.2196266 |
| _version_ | 1797678654190256128 |
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| author | Benjamin Bevans Christopher Barrett Thomas Spears Aniruddha Gaikwad Alex Riensche Ziyad Smoqi Harold (Scott) Halliday Prahalada Rao |
| author_facet | Benjamin Bevans Christopher Barrett Thomas Spears Aniruddha Gaikwad Alex Riensche Ziyad Smoqi Harold (Scott) Halliday Prahalada Rao |
| author_sort | Benjamin Bevans |
| collection | DOAJ |
| description | We 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 µm), layer related inconsistencies at the meso-scale (100 µm 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). |
| first_indexed | 2024-03-11T23:03:04Z |
| format | Article |
| id | doaj.art-e058754e617045928447cca28cce2a26 |
| institution | Directory Open Access Journal |
| issn | 1745-2759 1745-2767 |
| language | English |
| last_indexed | 2024-03-11T23:03:04Z |
| publishDate | 2023-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Virtual and Physical Prototyping |
| spelling | doaj.art-e058754e617045928447cca28cce2a262023-09-21T14:38:04ZengTaylor & Francis GroupVirtual and Physical Prototyping1745-27591745-27672023-12-0118110.1080/17452759.2023.21962662196266Heterogeneous sensor data fusion for multiscale, shape agnostic flaw detection in laser powder bed fusion additive manufacturingBenjamin Bevans0Christopher Barrett1Thomas Spears2Aniruddha Gaikwad3Alex Riensche4Ziyad Smoqi5Harold (Scott) Halliday6Prahalada Rao7Virginia Polytechnical InstituteOpen Additive, LLCOpen Additive, LLCUniversity of Nebraska-LincolnVirginia Polytechnical InstituteUniversity of Nebraska-LincolnNavajo Technical UniversityVirginia Polytechnical InstituteWe 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 µm), layer related inconsistencies at the meso-scale (100 µm 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).http://dx.doi.org/10.1080/17452759.2023.2196266additive manufacturingsensor data fusionthermal imagingspatter monitoringshape agnostic monitoringporosity |
| spellingShingle | Benjamin Bevans Christopher Barrett Thomas Spears Aniruddha Gaikwad Alex Riensche Ziyad Smoqi Harold (Scott) Halliday Prahalada Rao Heterogeneous sensor data fusion for multiscale, shape agnostic flaw detection in laser powder bed fusion additive manufacturing Virtual and Physical Prototyping additive manufacturing sensor data fusion thermal imaging spatter monitoring shape agnostic monitoring porosity |
| title | Heterogeneous sensor data fusion for multiscale, shape agnostic flaw detection in laser powder bed fusion additive manufacturing |
| title_full | Heterogeneous sensor data fusion for multiscale, shape agnostic flaw detection in laser powder bed fusion additive manufacturing |
| title_fullStr | Heterogeneous sensor data fusion for multiscale, shape agnostic flaw detection in laser powder bed fusion additive manufacturing |
| title_full_unstemmed | Heterogeneous sensor data fusion for multiscale, shape agnostic flaw detection in laser powder bed fusion additive manufacturing |
| title_short | Heterogeneous sensor data fusion for multiscale, shape agnostic flaw detection in laser powder bed fusion additive manufacturing |
| title_sort | heterogeneous sensor data fusion for multiscale shape agnostic flaw detection in laser powder bed fusion additive manufacturing |
| topic | additive manufacturing sensor data fusion thermal imaging spatter monitoring shape agnostic monitoring porosity |
| url | http://dx.doi.org/10.1080/17452759.2023.2196266 |
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