A method for characterizing model fidelity in laser powder bed fusion additive manufacturing
Ibrahim Assouroko, Paul Witherell, Felipe LOPEZ
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 of the ASME 2016 International Mechanical Engineering Congress & Exposition
ASME IMECE 2016
, Witherell, P.
and LOPEZ, F.
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 February 27, 2024)