Hybrid vision-based quantitative evaluation method of complex wear scratches on honeycomb seals

A hybrid vision-based method for adaptive processing and quantitative evaluation of sealing honeycomb scratch measurement data was proposed. Taking into account the characteristics of edge blurring and complex cross-sections in honeycomb scratches, the method integrated point cloud and image data through fusion analysis to achieve adaptive scratch identification, feature extraction, and quantitative evaluation. In point cloud data analysis, the measured point cloud was realigned, and the primary plane was extracted based on the prior geometric features of the honeycomb structure, enabling alignment of the measurement coordinate system with the theoretical coordinate system. In image data analysis, image morphology algorithms were employed to address challenges such as the abundance of honeycomb cavities and deep holes, ensuring precise extraction of the honeycomb regions and quantitative computation of the scratch geometry. Testing on multiple sets of measured data from worn and ablated honeycomb surfaces demonstrated the ability of the proposed method to accurately identify all scratches, with deviations in scratch width and depth measurements being less than 5% compared with manual measurements. Moreover, the identification speed was improved by more than sevenfold. The results indicated that the proposed method effectively utilized surface morphology point cloud data for the adaptive identification and quantitative evaluation of honeycomb scratches caused by wear and ablation.