Improving ceramic additive manufacturing via machine learning-enabled closed-loop control

Advanced ceramic products are widely used in aerospace, automotive, electronic, laboratory equipment, and other industries. To achieve the geometric complexity and desirable properties that are difficult to obtain by conventional manufacturing methods, ceramic additive manufacturing (AM) methods have been studied intensively in recent years. However, the adaptive control with feedback is not currently implemented in any commercially available ceramic three-dimensional printer. Robocasting is one of the most widely utilized constant-volumetric-flow AM processes for creating various ceramic materials at a low cost. This study employed robocasting as a model to implement an adaptive control with a feedback loop in the ceramic AM process. In this research, processing load that was proportional to the processing pressure, width of the print, and length of extrusion were selected to be representative of process signal, quality signal, and control parameter, respectively. First, a database of the load and extrusion length was established. An artificial neural network model was created using that established database. The data-driven, closed-loop control was integrated into the robocasting process. Finally, the improvement was validated by comparing the quality of the prints produced by both the closed-loop process with the adaptive control and the open-loop process without the adaptive control.