A method of generating an ROI profile for a fuel injector using machine learning and a constrained/limited training data set is disclosed. The method includes receiving a first plurality of measurement sets for a fuel injector when operating at a first target set point. Preferably, at least two measurement sets of the first plurality of measurement sets are selected to generate a first averaged ROI profile for the first target condition. The at least two selected measurement sets are then used to train a machine learning model that can output a predicted ROI profile for a fuel injector based on a desired pressure value and/or desired mass flow rate value. Training of the machine learning model preferably includes a predetermined number of iterations that induces overfitting within the model/neural network.
Khanh Cung; Zachary Williams; Ahmed Moiz; Daniel Bitsis, Jr.