A Method for Characterizing Model Fidelity in Laser Powder Bed Fusion Additive Manufacturing

As Additive Manufacturing (AM) matures as a technology, modeling methods have become increasingly sought after as a means for improving process planning, monitoring and control. For many, 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. Such predictions 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 models. A detailed study of the assumptions and approximations adopted in the development of models could be used to identify their predictive capabilities. This could then be used to estimate the level of fidelity to be expected from the models. This paper conceptualizes the modeling process and proposes a method to characterize AM models and ease the identification and communication of their capabilities, as determined by assumptions and approximations. An ontology is leveraged to provide structure to the identified characteristics. The resulting ontological framework enables the sharing of knowledge about indicators of model fidelity, through semantic query and knowledge browsing capabilities.