A glue-filling process defect early warning method and system

By acquiring glue dispensing process parameters and product structure information, and combining them with glue temperature sequence for flow stability analysis, the problem of low accuracy in existing glue dispensing process defect early warning methods has been solved, enabling precise early warning and clear intervention for glue dispensing process defects.

CN122174052APending Publication Date: 2026-06-09SHENZHEN YUSHENG OPTOELECTRONICS CO LTD
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
SHENZHEN YUSHENG OPTOELECTRONICS CO LTD
Filing Date
2026-03-26
Publication Date
2026-06-09

AI Technical Summary

Technical Problem

Existing methods for early warning of defects in the glue-filling process cannot accurately distinguish the types of defects that are about to occur, resulting in low accuracy of early warning and unclear intervention direction.

Method used

By acquiring glue dispensing process parameters, product structure information, and glue temperature sequence, glue flow stability analysis is performed. Combined with the probability sequence of air bubble encapsulation and insufficient filling risk, a comparative analysis is conducted to generate accurate defect early warning signals.

Benefits of technology

It enables precise early warning of defects in the glue-pouring process, provides clear intervention directions and valuable time windows, and significantly improves the accuracy and pertinence of early warning.

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Abstract

This invention relates to the field of glue-filling production technology and discloses a method and system for early warning of defects in the glue-filling process. This invention extracts structural constraint features from product structural data, thereby quantifying abstract product drawings or model information into quantitative engineering parameters that can be analyzed in conjunction with glue-filling process parameters. Simultaneously, it constructs and calculates in real time values ​​for flow instability tendency, venting difficulty, process disturbance intensity, and simulates an evolution curve representing the instantaneous net propulsion capability of the filling process, among other multi-dimensional risk indicators. This allows for the keen detection of complex risks that are about to occur within traditional safety thresholds but due to the synergistic deterioration of multiple process and material parameters in unfavorable ranges. It solves the problems of low accuracy and unclear intervention direction of traditional methods, achieving a qualitative leap from post-event alarms to pre-event warnings and defect attribution, providing a clear direction and valuable time window for process early warning intervention.
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