Color consistency is crucial for both photo and commercial printing applications. Dot gain tables are currently updated sporadically, and between updates colors can shift due to process drift in the press. The goal of this investigation is to dynamically control the dot gain table and developer voltage to ensure more consistent color control while minimizing waste and calibration measurements.In this article we approach the elements of this calibration process as a series of machine-learning problems and investigate the efficacy of replacing physical calibration measurements with model-based predictions. The current state of the machine, expressed as sensor measurements, are used to model both the developer voltage, and the subsequent dot gain look up table. We also consider models that make a prediction based on a restricted set of calibration measurements, not necessarily including the full machine state vector. Our initial investigation using a preliminary dataset shows that machine learning methods are suitable for predicting the dot gain table.