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A method for characterizing model fidelity in laser powder bed fusion additive manufacturing

Published

Author(s)

Ibrahim Assouroko, Paul Witherell, Felipe LOPEZ

Abstract

As Additive Manufacturing (AM) matures as a technology, predictive modeling methods have become increasingly sought after as a means for process planning, monitoring and control. For many, predictive modeling offers the potential to complement, and in some cases perhaps ultimately supplant, tedious part qualification processes. Models are tailored for specific applications, focusing on specific predictions of interest, which are obtained with different degrees of fidelity. Limited knowledge of model fidelity hinders the user's ability to make informed decisions on the selection, use, and reuse of predictive models. A detailed study of the assumptions and approximation adopted in the development of models could be used to identify their predictive capabilities and estimate the level of fidelity to be expected from them. This paper conceptualizes the modeling process and proposes a method to characterize AM models and ease the identification and communication of their capabilities. An ontology is leveraged to provide structure to the identified modeling concepts. This ontological framework is designed to allow the share of knowledge about indicators of model fidelity, through semantic query and knowledge browsing.
Proceedings Title
Proceedings of the ASME 2016 International Mechanical Engineering Congress & Exposition
ASME IMECE 2016
Conference Dates
November 11-17, 2016
Conference Location
Phoenix, AZ, US

Keywords

additive manufacturing, model fidelity, model characterization, ontology

Citation

Assouroko, I. , Witherell, P. and LOPEZ, F. (2016), A method for characterizing model fidelity in laser powder bed fusion additive manufacturing, Proceedings of the ASME 2016 International Mechanical Engineering Congress & Exposition ASME IMECE 2016, Phoenix, AZ, US, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=921525 (Accessed June 21, 2024)

Issues

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

Created November 30, 2016, Updated April 6, 2022