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Aiming at the problems of low cleaning efficiency, damaged nozzle, and high safety hazard in the traditional cleaning method of the fused deposition modeling (FDM) equipment nozzle, an environmentally friendly nozzle cleaning system was designed by using the chemical solvent and ultrasonic cleaning technology. The modular design of the nozzle clamping module can improve the nozzle loading efficiency and cleaning efficiency. The cleaning liquid circulation device makes the cleaning process automated under the coordination of the control module, which effectively reduces the safety hazards of the cleaning process. A thermal drying device is mainly used to speed up the drying rate of the nozzle. An exhaust gas purification device is used to absorb the volatile chemicals in the cleaning process and reduce pollution in the working environment.

In order to solve the problem of the single feature scale of the generated image in the SISR field and the lack of texture information, a parallel generation confrontation network structure based on the attention mechanism and multi-scale is proposed on the basis of SRGAN, which adopts a dual generator and discriminator combined with attention module model. Train the network to learn multi-scale features, and integrate high-frequency information of different scales in the residual network. The experimental results on Set5, Set14, and BSD100 benchmark data sets prove that the algorithm has a good effect in restoring image detail information.

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