Welding quality online detection method based on machine vision

By using multimodal data acquisition and nonlinear deep fusion models, the problems of insufficient data and mismatched judgment in welding quality inspection have been solved, achieving a comprehensive, accurate and reliable improvement in weld quality inspection, and adapting to diverse industrial needs.

CN121707988APending Publication Date: 2026-03-20TAIZHOU GENTECK ELECTRIC
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Patent Information

Application Number
CN202511929063.X
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-12-19
Publication Date
2026-03-20

AI Technical Summary

Technical Problem

Existing welding quality inspection technologies suffer from problems such as insufficient single-dimensional data collection, inaccurate feature extraction, inadequate fusion of multi-dimensional features, and unsuitability for defect judgment, resulting in a lack of comprehensiveness, accuracy, and reliability in the inspection results.

Method used

Multimodal acquisition equipment is used to acquire full-dimensional data of the weld. A time protocol hardware synchronization system is used to realize the temporal and spatial synchronization of each device. A raw dataset of the weld containing geometric, thermal, texture, three-dimensional topology and process parameters is constructed. A dedicated quantization feature function is constructed, a nonlinear deep fusion model is established, and a dynamic threshold judgment system is built.

Benefits of technology

It has achieved a comprehensive improvement in the accuracy and reliability of weld quality inspection, avoiding missed defects, ensuring that the inspection results are consistent with the actual quality status, and adapting to diverse industrial application scenarios.

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Abstract

The invention discloses a welding quality on-line detection method based on machine vision, and particularly relates to the field of welding quality detection.The welding quality on-line detection method comprises the steps that multi-dimensional basic data collection is conducted, the two-dimensional form, heat distribution, micro texture, three-dimensional topological data and welding process parameters of a welding seam are obtained, and a full-dimensional original data set with time-space synchronization is constructed; performing feature decoupling on the data, respectively constructing geometric morphology, thermal, texture and three-dimensional topological feature functions, and converting unstructured data into structured feature values; integrating multi-dimensional features through a nonlinear deep fusion model, introducing thermal probability mapping, texture attention weighting and a three-dimensional residual compensation mechanism, and outputting a weld comprehensive quality value; and finally, positioning dominant defect dimensions and identifying defect types in combination with a dynamic threshold model to finish quality grading judgment. Multi-dimensional accurate detection and dynamic grading of the welding quality are achieved, different scene requirements are met, and the detection efficiency and the judgment accuracy are improved.
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