Efficient Creation of Jettability Diagrams Using Active Machine Learning

The ability to jet a wide variety of materials consistently from print heads remains a key technical challenge for inkjet-based additive manufacturing processes. Drop watching is the most direct method for testing new inks and print head designs but such experiments are also resource consuming. In this work, a data-efficient machine learning technique called active learning is used to construct detailed jettability diagrams that identify complex regions corresponding to "no jetting," "jetting," and "desired jetting," rather than only individually sampled points. Crucially, our active learning method has resolved challenges with model selection that previously limited the accuracy of active learning in practical settings with very small experimental budgets. In addition, the key "desired jetting" zone may be quite small which is a challenge for initializing active learning. We leverage the physical intuition that the "desired jetting" zone tends to exist between the "jetting" and "no jetting" zone, to improve the performance of this highly imbalanced classification problem by performing two binary classifications in sequence. The first binary classification aims to map out the "jetting" zone versus the "no jetting" zone, while the second binary classification targets identifying the "desired jetting" zone with primary drops only. Our experiments use a stroboscopic drop watcher to visualize the jetting behavior of two fluids from a piezoelectric print head with different jetting waveforms. The results obtained from active learning were compared to a grid search method, which involves running more than 200 experiments for each fluid. The active learning method significantly reduces the number of experiments by 80% while achieving a test accuracy of more than 95% in the "jetting" zone prediction for the test fluids. The ability to construct these jettability diagrams will further accelerate new ink and print head developments.