Intelligent modeling and monitoring of micro-droplet profiles in 3D printing

The inkjet 3D printing has been one of the most studied and applied additive manufacturing (AM) processes in electronic industry. In this AM process, the forming quality is greatly influenced by the micro-droplet deposition and substrate temperature. While most studies focus on the formation mechanism of droplets, there are few studies on the quantitative evaluation of the droplet surface profile and its qualitative correlation with temperature changes. In this study, the characteristics of droplet profile in three-dimensional inkjet printing were studied from two aspects, the modeling of droplet shape and the estimation of droplet temperature. For this purpose, different types of radial basis function networks (RBFN) are applied. The validity of the regularized RBFN model is developed and verified by experiments. The results show that the droplet shape can be accurately modeled and the drying temperature can be accurately estimated given the model.