Ejection state prediction for a pneumatic micro-droplet generator by BP neural networks

Abstract

Micro-droplet generation is related to liquid dispensing technology that has potential applications in many fields. Specifically, pneumatic micro-droplet generation is controlled by a solenoid valve being briefly turned on, so that high pressure gas enters the liquid reservoir, forming a gas pressure pulse waveform P(t), forcing the liquid out through a tiny nozzle to form a micro-droplet. For each ejection, P(t) is acquired by a high speed pressure sensor, and the ejection state is obtained by machine vision methods. A prediction model based on BP neural network is established, with P(t) as input and the droplet ejection state as output. Experiments show that the BP neural network can predict the number of droplets with an accuracy higher than 99%. It is also shown that the BP neural network can improve the prediction accuracy for the position of droplets relative to the nozzle, at a given moment. Under typical working conditions, P(t) is not consistent. As a result, the ejection state is not consistent either. These prediction models may be used for real time monitoring and control of the pneumatic micro-droplet generator.

Keywords

• micro-droplet
• pneumatic
• bp neural network
• ejection state
• machine vision