Abstract
A novel approach for improving the accuracy and efficiency of laser-induced forward transfer (LIFT), through the application of deep learning techniques is presented. By training a neural network on a dataset of images of donor and receiver substrates, the appearance of copper droplets deposited onto the receiver was predicted directly from images of the donor. The results of droplet image prediction using LIFT gave an average RMSE of 9.63 compared with the experimental images, with the SSIM ranging from 0.75 to 0.83, reflecting reliable structural similarity across predictions. These findings underscore the model's predictive potential while identifying opportunities for refinement in minimising error. This approach has the potential to transform parameter optimisation for LIFT, as it enables the visualization of the deposited material without the time-consuming requirement of removing the donor from the setup to allow inspection of the receiver. This work therefore represents an important step forward in the development of LIFT as an additive manufacturing technology to create complex 3D structures on the microscale.