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IN-SITU OBSERVATION SELECTION FOR QUALITY MANAGEMENT IN METAL ADDITIVE MANUFACTURING

Published

Author(s)

Paul Witherell, Yan Lu

Abstract

Metal additive manufacturing provides a larger design space with accompanying manufacturability than traditional manufacturing. Recently, much research has focused on simulating the process with regards to part geometry, porosity, and microstructure properties. Despite continued advances, this process has many process variables that are not well understood with respect to their effect on the process outcomes. With the continued application of in-situ sensors, such as CMOS cameras and infrared cameras, numerous real-time data can be captured and analyzed for monitoring the build quality. However, currently, real-time data predominantly focuses on the build failure and process anomalies by capturing the printing defects (cracks/ peel-off). A large amount of data such as melt pool geometries and temperature gradients are just beginning to be explored in relation to their connection towards final part quality. Towards investigating these connections, in this paper we propose sensor models that include numerous sensor capabilities and associate them with the corresponding real-time physical phenomena. These sensor models lay the foundation for a comprehensive knowledge framework for quality monitoring and management in a metal additive manufacturing system by bridging the physical phenomena and process outcomes. With the combination of our previously developed process ontology model [1-3] that describes the relationship between process variables and process outcomes, the real-time physical phenomena can be discovered for envisioning the deviation in the targeted build quality. For example, statistically significant sensor data that deviates from targeted process qualities can be detected. Finally, the observation and correctly monitored real-time data can help govern the desired process qualities Case studies are provided that scope the physical phenomena and sensors for verifying the effectiveness and efficiency of the proposed qualification and certification models.
Proceedings Title
2021 ASME IDETC CIE
Conference Dates
August 17-20, 2021
Conference Location
Virtual, MD, US
Conference Title
International Design Engineering Technical Conferences & Computers and Information in Engineering Conference

Keywords

Additive Manufacturing, Ontology, Advanced Manufacturing

Citation

Witherell, P. and Lu, Y. (2021), IN-SITU OBSERVATION SELECTION FOR QUALITY MANAGEMENT IN METAL ADDITIVE MANUFACTURING, 2021 ASME IDETC CIE, Virtual, MD, US, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=932400 (Accessed May 29, 2024)

Issues

If you have any questions about this publication or are having problems accessing it, please contact reflib@nist.gov.

Created October 1, 2021, Updated January 3, 2023