Optimization of Elliptical Double-Beta Spray Gun Model Under the Control of Fan Air Pressure

The air spray gun model for painting robots is a mathematical model that describes the performance and behavior of the air spray gun in a spray-painting system and simulates the spraying process. Currently, film uniformity and spraying efficiency are key factors in evaluating the spray performance of this model. To further enhance the accuracy and controllability of spray gun modeling, this study used the elliptical double-beta spray pattern model to investigate the key parameters influencing its performance. Fan air pressure was selected as the optimization variable. A fixed-point spraying experimental platform was established where spraying experiments were conducted under six different pressures, and coating thickness data were collected. The optimal fitting function was obtained using data processing software. Experimental verification showed that the amplitude error was within 3 mm and the film thickness error was within 4 µm. The results indicate that fan air pressure can accurately predict film thickness, significantly improving paint utilization, with a high engineering application value. This provides new theoretical support for precise control in the spraying process and optimization of automated spraying systems.