Intelligent control system for droplet volume in inkjet printing based on stochastic state transition soft actor–critic DRL algorithm

Inkjet printing is a low-cost, high efficiency technology for organic light emitting diode (OLED) manufacturing, and it is essential to accurately control the volume of droplets for high-resolution OLED manufacturing. A major challenge in volume control is to address the problem of droplet volume deviation caused by uncertain factors in the production process. In contrast, existing studies focus on the design of fixed waveforms to eject stable droplets, which cannot be used for volume control when the droplet volume deviates due to perturbations. In this paper, a soft actor–critic deep reinforcement learning (DRL) algorithm based on stochastic state transition is proposed for intelligent control of the droplet volume, which can adjust waveform parameters in time to control the volume when the observed droplet volume changes during the production. Firstly, based on the stochastic state transition method, the offline strategy samples are created from the discrete state dataset collected by the visual observation system, and then the control strategy is learned from the offline strategy samples via the soft actor–critic DRL algorithm. Then, according to the droplet volume deviation calculated by the visual observation system, the waveform parameters are adjusted by the control strategy to ensure the droplet volume accuracy. Finally, droplet volume control experiments were performed on an OLED inkjet printing equipment developed by our group. The experimental results show that the proposed method can control the droplet volume within ±5% deviation of the target volume with disturbances in the industrial manufacturing environment.