Artificial neural network-based casting layer stress prediction method and system, and storage medium

Through the casting layer stress prediction method based on artificial neural network, the problem of inability to achieve low-cost online prediction during the manufacturing process of electroforming mold cores was solved, and efficient and low-cost production of electroforming mold cores was achieved, thereby improving the manufacturing quality and efficiency of polymer microdevices.

CN120611758BActive Publication Date: 2025-10-10CENT SOUTH UNIV
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Patent Information

Application Number
CN202511102730.7
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2025-08-07
Publication Date
2025-10-10
Estimated Expiration
2045-08-07

AI Technical Summary

Technical Problem

Existing technologies fail to effectively combine artificial neural networks to transform the casting layer stress detection process into a digital model, resulting in the inability to achieve low-cost online prediction and real-time monitoring of electroplated mold cores during the manufacturing process, which in turn affects the mass production and industrial application of polymer microdevices.

Method used

A casting layer stress prediction method based on artificial neural network is adopted. Through electroforming substrate pretreatment, in-situ stress detection platform configuration, electroforming experiment, data acquisition and processing, an artificial neural network model is established to realize the nonlinear relationship fitting and prediction of casting layer stress.

Benefits of technology

It reduces the cost of casting layer stress detection, provides real-time feedback during the electroforming core production process, improves processing efficiency and method versatility, reduces the workload of process engineers, and ensures high-quality production of electroforming cores.

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Abstract

The application discloses a kind of based on artificial neural network's casting layer stress prediction method, system and storage medium, comprising: with silicon chip as electroforming base, one side of electroforming base is polished, and the other side is provided with conductive layer;Configuration electrocasting groove, collimating light source and wavefront sensor, adjust the relative position of collimating light source, wavefront sensor and electrocasting groove, make the light beam of collimating light source be reflected into wavefront sensor through the polished surface of silicon chip;With direct current or pulse current, electrocasting is carried out in electrocasting groove;Through data acquisition module, electrocasting process data and laser wavefront curvature radius are collected;According to the stress value calculated according to laser wavefront curvature radius, and stress value data and electrocasting process data are converted into neural network adaptation format;Select network type, activation function and hyperparameter, train artificial neural network model to fit the nonlinear relationship between electrocasting parameter and stress value;Stress prediction: input electrocasting parameter to artificial neural network model, output predicted casting layer stress value.
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