A data feedback-based flexible circuit board production quality monitoring method and system

By acquiring multi-source data and using neural networks to identify abnormal patterns, combined with feedback-based process adjustments, the problems of low efficiency and insufficient accuracy in traditional flexible circuit board testing have been solved, achieving efficient and accurate quality monitoring and improved production stability.

CN120704266BActive Publication Date: 2026-02-17EN DA DIAN LU SHEN ZHEN YOU XIAN GONG SI
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Patent Information

Application Number
CN202510861993.X
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2025-06-25
Publication Date
2026-02-17
Estimated Expiration
2045-06-25

AI Technical Summary

Technical Problem

Traditional flexible circuit board inspection methods rely on manual visual inspection and mechanical measurement, which are inefficient and easily affected by human factors, making it difficult to meet the high precision and high efficiency requirements of modern production.

Method used

The method employs multi-source data acquisition, joint detection of abnormal fluctuations, and feedback-based process adjustment. Physical parameters are collected in real time using a high-precision microscope and copper thickness measuring instrument. Anomalies are detected using univariate and multivariate control charts, and abnormal patterns are identified by combining a BP neural network. Equipment parameters are adjusted in real time, and thresholds are dynamically adjusted to optimize monitoring sensitivity.

Benefits of technology

It enables efficient and accurate detection in the flexible circuit board production process, timely detection and correction of abnormalities, and improves product quality and production stability.

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Abstract

The application discloses a flexible circuit board production quality monitoring method and system based on data feedback, collects physical parameters such as line width, line spacing, hole diameter, roundness and copper thickness of developing etching, drilling and copper plating processes, and workshop temperature and humidity and dust concentration data, generates a single variable control chart for single process physical parameters, generates a multivariate T 2 Control chart for multiple coupling parameter processes, calculates process stability index and comprehensive fluctuation index, inputs abnormal data into a BP neural network, identifies abnormal mode types and outputs characteristic parameters, generates parameter correction instructions according to the abnormal mode and the characteristic parameters, adjusts equipment parameters in real time, recalculates the process capability index based on the adjusted data, triggers secondary feedback if the process capability index does not meet the standard, and dynamically adjusts the control chart threshold value according to the standard deviation and mean value of the process capability index of continuous batches. The application comprehensively covers key production parameters, discovers abnormalities in time, accurately and comprehensively detects, adjusts in real time to ensure stability and controllability, and improves product quality.
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