Visualizing Defects of Irregular Weld Seams Based on MultiMode TFM Detection

Materials Evaluation Pub Date : 2024-03-01 DOI:10.32548/2024.me-04391

Shaofeng Wang, Yaowen Zhang, Shenrong Zhou, Wenjing Liu, Fei Du, Jian Wang, Fei Hui, Mingyuan Yang

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Abstract

An image reconstruction method based on the multimode total focusing method (MTFM) is proposed to overcome the limitations of traditional total focusing method (TFM) imaging in detecting tiny discontinuities at complex locations. We conducted MTFM detection and TFM image reconstruction experiments for irregular welds containing multiple discontinuities. In an experiment using four 1 mm diameter manufactured defects fabricated on two aluminum alloy welded test blocks, we achieved two significant contributions. First, we accurately detected small discontinuities by combining CIVA simulation with robotic arm assistance. Second, we proposed fusion factor coefficients for TFM image processing, which considered different modal weights for image fusion and de-noising, thereby preserving the integrity of the fused images. Our experimental results demonstrate that the reconstructed TFM images effectively represented all defect information. Compared with other modal TFM images with the highest signal-to-noise ratio (SNR), the amplitude-corrected optimized TFM image exhibits an improved SNR of 51.95% without losing defect information.

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基于多模式 TFM 检测的不规则焊缝缺陷可视化

我们提出了一种基于多模全聚焦法（MTFM）的图像重建方法，以克服传统全聚焦法（TFM）成像在检测复杂位置的微小不连续性方面的局限性。我们对包含多个不连续面的不规则焊缝进行了 MTFM 检测和 TFM 图像重建实验。在使用两个铝合金焊接试块上制造的四个直径为 1 毫米的人造缺陷进行的实验中，我们做出了两项重大贡献。首先，我们将 CIVA 仿真与机械臂辅助相结合，准确检测出了小的不连续性。其次，我们提出了用于 TFM 图像处理的融合因子系数，该系数考虑了图像融合和去噪的不同模态权重，从而保持了融合图像的完整性。实验结果表明，重建的 TFM 图像有效地代表了所有缺陷信息。与信噪比（SNR）最高的其他模态 TFM 图像相比，经过振幅校正的优化 TFM 图像在不丢失缺陷信息的情况下，信噪比提高了 51.95%。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Materials Evaluation

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