Intelligent control system for 3D inkjet printing

Additive manufacturing is typically an open-loop process. Consequently, it results in poor material matching at the junctions in multimaterial printing, affecting the performance of functional components. This study investigated the surface accumulation characteristics of parts in three-dimensional (3D) inkjet printing. Moreover, an intelligent additive manufacturing system for array nozzles was developed to improve the material accumulation accuracy and printing efficiency. First, a height prediction model was established to predict the printing height information, and a P-type closed-loop iterative learning control compensation algorithm with initial value correction was formulated based on the layer thickness experimental data. This algorithm reduced the root-mean-squared error (RMSE) of the sample's surface by 69.3%. Second, a set of random offset nozzle compensation algorithms was developed and combined with the laser vision scanning method to obtain the nozzle clogging information and compensate for sample defects by adjusting the parameters of the random algorithm. Finally, a microstrip antenna was fabricated. The surface roughness of the sample in this study was lower than that of the open-loop printed sample, resulting in a good antenna radio frequency (RF) layer connection. In addition, the S11 parameters were closer to the simulation results. These results validate the significance of this research in electronic printing.

Automated summary

This study improves the material accumulation accuracy and printing efficiency of 3D inkjet printing through the development of height prediction models and compensation algorithms. By applying these methods, a sample with lower surface roughness was successfully fabricated, resulting in improved antenna performance.