A method for constructing an electrofluid printing mode classification model

By constructing a classification model for current fluid printing patterns, utilizing a neural network framework and multi-layer encoder units, and combining a pre-training-transfer learning method, the problem of insufficient recognition accuracy in current fluid printing pattern monitoring in existing technologies is solved, achieving efficient pattern recognition and process parameter adjustment.

CN119169346BActive Publication Date: 2025-10-17HUAZHONG UNIV OF SCI & TECH
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Patent Information

Application Number
CN202411174593.3
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2024-08-26
Publication Date
2025-10-17
Estimated Expiration
2044-08-26

AI Technical Summary

Technical Problem

Existing methods for monitoring electrofluid printing patterns rely on manually extracting image features, which results in insufficient recognition accuracy and is easily affected by dynamic changes in the Taylor cone, leading to misjudgments.

Method used

A current fluid printing pattern classification model is constructed, including an image feature extraction module, an adjacent frame motion feature calculation module, a spatiotemporal flow feature extraction module, and a classification module. A neural network framework is used for pattern recognition, combined with a multilayer encoder and a multilayer perceptron output unit. The model is trained using a pre-training-transfer learning method and uses video data for pattern classification.

Benefits of technology

It improves the accuracy of pattern recognition, simplifies the calculation process, reduces computational consumption, and enables more efficient monitoring of current fluid printing, which can guide the efficient adjustment of process parameters.

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Abstract

The application belongs to the field of electrohydrodynamic printing, and particularly relates to a method for constructing an electrohydrodynamic printing mode classification model, which comprises: each training sample comprises an electrohydrodynamic Taylor cone and jet video containing f frames of images and corresponding electrohydrodynamic printing modes; a neural network framework: an image feature extraction module is used for preliminary extraction of image features; an adjacent frame motion feature calculation module is used for calculation of feature level motion information between adjacent frames; a time flow feature extraction unit is connected with the image feature extraction module and used for further extraction of motion features; a space flow feature extraction unit is connected with the adjacent frame motion feature calculation module and used for further extraction of image features; and a classification module is connected with the time flow feature extraction unit and the space flow feature extraction unit for classification. The application takes video data as input and combines a time-space double flow attention mechanism, effectively making up for the problem of insufficient recognition accuracy of an image-based classification model.
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