*Increasingly, a wide range of in-situ sensors are being instrumented on additive manufacturing (AM) machines. Researchers and manufacturers use these sensors to collect a variety of data to monitor process performance and part quality.  The amount and speed of the collected data are unprecedented; consequently, several data-related issues are now a major stumbling block. Broadly speaking, those issues include registration, curation, organization, storage, management, and analysis.  This paper justifies the need for meta-data by providing a gap analysis of the current registration approaches. It then identifies a set of meta-data for sensor data collected from several types of in-situ sensors that are used to monitor laser powder bed fusion (L-PBF) processes. The paper also includes an example data model that captures the properties and relationships among those meta-data. The data elements in that model provide industrial users with the capabilities and formats to capture, exchange, and share L-PBF process data.Future work include the development of ex-situ data modeling for X-Ray Computed Tomography 3D models, coordinate measuring metrological models, and surface roughness models. Both in-situ and ex-situ data model are needed for create methods for sensor data fusion, which includes geometric data alignment and data registration. Furthermore, user-based scenarios can thus be developed for using data analytics in process monitoring and control.*


Meta-Data for In-Situ Monitoring of Laser Powder Bed Fusion Processes

Division / Sponsor: 
Manufacturing Engineering Division (MED)

International Manufacturing Science and Engineering Conference - Paper Proceedings: https://asmedigitalcollection.asme.org/MSEC/MSEC2020/volume/84256

JSME 2020 Conference on Leading Edge Manufacturing/Materials and Processing - Paper Proceedings: https://asmedigitalcollection.asme.org/lemp/LEMP2020/volume/83624

MSEC2020 - Virtual Conference, LEMP2020 - Virtual Conference

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