This invention discloses a method and
system for
processing multi-
layer thickness images in UV printing. The method includes acquiring the target thickness intention, outputting a three-dimensional joint instruction
tensor of droplet volume, droplet temperature, and UV
light intensity via a
generative adversarial network (GAN), and then performing multi-
physics collaborative printing based on this
tensor. At the pixel level, the method independently controls the droplet temperature of each
nozzle and the UV
light intensity of each pixel, achieving differentiated modulation such as high stacking, tiling, and edge barrier walls. An
error map is obtained by measuring the actual thickness in situ and calculating the expected thickness using a differentiable physical proxy model. This
error map is then processed through a three-layer
closed loop to achieve real-time correction, GAN update, and fine-tuning of the tactile mapping model. This invention significantly improves thickness control accuracy, substrate adaptability, and printing efficiency, and can be applied to various scenarios such as 3D relief, micro-optical components, functionally graded materials, and 4D self-folding.