Research on Surface Cracks Identification of Cr/Zr Coating in Circumferential Compression

doi:10.3901/JME.2024.08.001

Abstract

Abstract: Objective to realize the accurate recognition and analysis of Cr coating surface cracks, aiming at the crack image on the surface of Cr/Zr coating has problems such as dark vision, high noise and low manual recognition efficiency. we present Cr coating surface cracks recognition and analysis framework (CSCRA) for crack detecting and crack analyzing in crack images. The crack images are produced by the circumferential compression experiment on zirconium alloy Cr-coated cladding tube. Our approach, based on the U²-Net neural network, obtain each crack location in the Cr coated surface crack images by the semantic segmentation. Then, we design a novel algorithm to analyze the four important indicators, two of them are the amount of cracks and the density of cracks in whole crack images, others are length and width of each crack. The algorithm combines the connected domain analyze and distance calculation with the central axis extraction algorithm. Experimental results on the Cr coating surface crack images show that CSCRA can detect multiform crack images accurately and effectively, meanwhile, the amount of the cracks and the density of cracks in one crack image calculated by our method are almost consistent with the real data. Due to the detailed crack segmentation output, the two indicators, length and width, achieve highly accuracy. Compared to the edge detection method or the threshold segmentation method, CSCRA has much better segmentation accuracy even with high noise and low continuity crack images. While analyzing the relationship between the circumferential compression experiment deformation and the four indicators, we find that both the crack numbers and width show an increasing trend as the amount of hoop compression increases, but are different in increase rate at different stages.

Key words: Cr/Zr coating, circumferential compression, crack identification, CSCRA algorithm, neural network

CLC Number:

TG144

ZHENG Jie, QUAN Wei, LIU Yang, LU Xuemin, LIU Xiaohong, YUE Yanan, ZHOU Teng, CAI Zhenbing. Research on Surface Cracks Identification of Cr/Zr Coating in Circumferential Compression[J]. Journal of Mechanical Engineering, 2024, 60(8): 1-10.

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