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BP neural network-based pneumatic micro-droplet jetting state prediction method

A BP neural network and droplet ejection technology, applied in biological neural network models, neural architectures, character and pattern recognition, etc., can solve the problems of time-consuming simulation, increase system complexity, and limit the design space of other components, and achieve strong performance. The effect of generalization

Active Publication Date: 2019-06-11
BEIJING UNIV OF TECH
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

Its effectiveness still needs to be verified experimentally, and the simulation is time-consuming
The optical measurement method based on scattering can realize the high-speed measurement of the geometric parameters of the droplet, but the system requires high environmental conditions (such as dark environment), and the practicability is poor
The method based on machine vision and image processing is the most direct and effective method to study the state of droplet ejection, but the acquisition equipment is expensive, and the real-time processing of a large amount of image information increases the complexity of the system
The presence of the imaging system largely limits the design space for other components such as moving parts

Method used

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  • BP neural network-based pneumatic micro-droplet jetting state prediction method
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  • BP neural network-based pneumatic micro-droplet jetting state prediction method

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Embodiment 1

[0060] In this embodiment, the number of ejected droplets is mainly predicted for the built pneumatic droplet ejecting device. The device includes a droplet ejection system and a droplet monitoring system.

[0061] Step 1, recording of waveform and droplet ejection state:

[0062] Through the pneumatic droplet ejection device, record various air pressure oscillation signals P(t) and the droplet ejection state under the corresponding waveform:

[0063] (1) Open the industrial camera on the control software of the upper computer, open the serial communication interface to communicate with the control circuit of the lower computer;

[0064] (2) Set the injection frequency of the device on the control software of the host computer to 20Hz, the conduction time of the high-speed solenoid valve Δt to 1000-1500μs, and the delayed photo time of the industrial camera to 5000μs; adjust the input air pressure P at the front end of the device 0 is 0.3MPa.

[0065] (3) In the waveform re...

Embodiment 2

[0096] In this embodiment, mainly for the built-up pneumatic droplet spraying device, when spraying a single droplet, the droplet position H d Make predictions. The device includes a droplet ejection system and a droplet monitoring system.

[0097] Step 1, waveform and injection state records:

[0098] Through the pneumatic droplet ejection device, record various air pressure oscillation signals P(t i ) and the droplet position parameter H under the corresponding waveform d :

[0099] (1) Open the industrial CCD camera on the control software of the host computer, and open the serial communication interface to communicate with the control circuit of the lower computer;

[0100](2) Set the injection frequency of the device on the control software of the host computer to 20Hz, the conduction time of the high-speed solenoid valve Δt to 1000-1500μs, and the delayed photo time of the industrial camera to 5000μs; adjust the input air pressure P at the front end of the device 0 ...

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Abstract

The invention discloses a BP neural network-based pneumatic micro-droplet jetting state prediction method, and belongs to the field of micro-droplet jetting. According to the method, firstly, a BP neural network-based micro-droplet state prediction model is established, the prediction model takes an air pressure oscillation signal P(t) as an input, P(t) is collected by an intracavity high-speed pressure sensor, and a micro-droplet state is taken as an output. It is verified that the established model can accurately predict the micro-droplet jetting state. Common jetting state parameters comprise the number Nd of micro-droplets and the distance Hd of the micro-droplets relative to a nozzle at a certain time delay (with the rising edge of a high-speed electromagnetic valve driving signal asreference time). In an application example, the prediction accuracy of the number of micro-droplets is higher than 99%. Compared with a statistical average position of the micro-droplets obtained based on machine vision and image processing, the prediction precision of Hd through a prediction model of P(t) and BP neural networks can be improved by more than three times.

Description

technical field [0001] The invention belongs to the field of droplet spraying, and in particular relates to a pneumatic droplet spraying system and a method for predicting the state of droplet spraying based on BP neural network. The method can effectively predict the droplet ejection state by establishing a prediction model, and can be used for the prediction, real-time monitoring and control of the droplet state of the pneumatic droplet ejection device. Background technique [0002] Droplet ejection technology is widely used in many fields, such as inkjet printing, printed electronics, 3D printing, etc. In addition, droplet ejection technology is often used in the field of biomedicine. For precious or scarce samples, micro-sample dispensing not only reduces the sample volume to reduce costs, but also helps to increase the speed of biochemical reactions. Compared with other spraying methods, pneumatic droplet spraying is easy to operate and suitable for samples with vario...

Claims

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Application Information

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IPC IPC(8): G06F17/50G06K9/32G06N3/04
Inventor 王志海王飞包伟捷王一玮
Owner BEIJING UNIV OF TECH
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