Arcuate contact workpiece anti-ablation uniformity sample abnormal image detection method and system

By using a multiphysics coupling simulation model and advanced image processing technology, combined with reverse engineering and particle swarm optimization algorithms, the ablation resistance uniformity of static arc contact workpieces is identified and improved, achieving efficient and accurate anomaly detection and process optimization.

CN119832269BActive Publication Date: 2026-06-02HENAN XINFENG NEW MATERIALS CO LTD
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
HENAN XINFENG NEW MATERIALS CO LTD
Filing Date
2024-12-23
Publication Date
2026-06-02

AI Technical Summary

Technical Problem

Existing technologies are insufficient to effectively identify and improve the uniformity of ablation resistance of static arc contact workpieces. Traditional methods lack advanced feature extraction capabilities, resulting in insufficient detection capabilities.

Method used

Theoretical reference images are generated by multiphysics coupling simulation models, and image registration and anomaly detection are performed by combining convolutional neural networks and autoencoders. Anomaly patterns are reconstructed using reverse engineering and particle swarm optimization algorithms, and causal reasoning frameworks are applied for cause diagnosis. Detailed reports are generated to adjust design and manufacturing parameters.

Benefits of technology

It improves the accuracy and efficiency of anomaly detection, reduces false alarms, provides clear improvement suggestions, optimizes production processes, and improves product quality.

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Abstract

The application provides a static arc contact workpiece anti-ablation uniformity sample abnormal image detection method and system, wherein the physical behavior of the static arc contact workpiece under different arc conditions is modeled, a multi-physical field coupling simulation model and a theoretical reference image are generated; an actual surface image is obtained, based on the theoretical reference image, a plurality of quantitative indexes and a convolutional neural network are used to extract high-level features, comparative analysis is performed, and a matching degree evaluation result is generated; the evaluation result is subjected to abnormality detection, an abnormal area with uneven anti-ablation performance is identified, an abnormality identification result is generated; according to the abnormality identification result, the reverse engineering principle is combined with a particle swarm optimization algorithm to reconstruct an abnormal arc behavior mode, the input parameters are adjusted through the simulation model until the abnormality is reproduced, preliminary cause diagnosis information is obtained, and a detailed cause diagnosis report is generated. The application improves the detection and improvement capability of the static arc contact workpiece anti-ablation uniformity.
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