Closed-Loop Control of Droplet Quality Based on Curriculum Deep Deterministic Policy Gradient Algorithm

Piezoelectric inkjet printing has important applications in high-precision manufacturing, but precise control of droplet quality faces challenges such as the nonlinear coupling between drive waveform parameters and droplet characteristics, as well as environmental disturbances. To address these issues, this paper proposes a closed-loop droplet control method based on a curriculum deep deterministic policy gradient (DDPG) algorithm, with the standard deviation of droplet volume and velocity as the main optimization objectives. To tackle the complex mapping relationships and boost training efficiency, we first built a high-fidelity simulation environment using a four-layer deep residual network trained on real-world industrial ink droplet data. Following that, we designed a dynamic weight-adaptive composite reward mechanism and integrated a curriculum learning strategy with the Deep Deterministic Policy Gradient (DDPG) algorithm. Relying on our self-developed droplet observation platform, we achieve closed-loop control of the standard deviation of droplet volume and velocity. Experimental results show that this method can precisely control the droplet volume within ±0.1 pl of the target value, while keeping the velocity standard deviation below 40 mm/s. The adjustment process is stable without severe oscillations, and the method demonstrates strong robustness against environmental disturbances such as ink supply pressure. This provides an efficient and practical solution for the automated control of droplet quality in piezoelectric inkjet printing.