舒意峰,龙盛蓉,陈学宽,等. 基于相位迁移的渐进式多阵元频域全聚焦成像方法[J]. 失效分析与预防,2025,20(4):298-307. doi: 10.3969/j.issn.1673-6214.2025.04.006
    引用本文: 舒意峰,龙盛蓉,陈学宽,等. 基于相位迁移的渐进式多阵元频域全聚焦成像方法[J]. 失效分析与预防,2025,20(4):298-307. doi: 10.3969/j.issn.1673-6214.2025.04.006
    SHU Yifeng,LONG Shengrong,CHEN Xuekuan,et al. Phase-migration-based progressive multi-element array frequency-domain full-focus imaging method[J]. Failure analysis and prevention,2025,20(4):298-307. doi: 10.3969/j.issn.1673-6214.2025.04.006
    Citation: SHU Yifeng,LONG Shengrong,CHEN Xuekuan,et al. Phase-migration-based progressive multi-element array frequency-domain full-focus imaging method[J]. Failure analysis and prevention,2025,20(4):298-307. doi: 10.3969/j.issn.1673-6214.2025.04.006

    基于相位迁移的渐进式多阵元频域全聚焦成像方法

    Phase-migration-based Progressive Multi-element Array Frequency-domain Full-focus Imaging Method

    • 摘要: 为提升超声全聚焦检测对强横向变速介质的缺陷检测能力,本文提出基于相位迁移的渐进式多阵元频域全聚焦方法成像技术(GMTFM-PSM)。该技术在传统超声全聚焦成像检测的基础上,利用多阵元组激励信号,降低强横向变速复合材料的声速衰减,提高声束指向性、增强信号强度;通过引入相位迁移算法并对材料中传播声速进行矫正,提高迁移因子的准确度,有效降低复合材料复杂多层结构对成像精度的影响;结合渐进式全聚焦算法,降低旁瓣影响,优化成像效果。结果表明:与传统B扫、SAFT-FW和TFM-FW成像算法对比,引入GMTFM-PSM算法定量误差分别降低了391%、50%和13%,显著提高了检测精度。该方法为碳纤维增强复合材料等强横向变速材料的缺陷检测提供了可靠的技术保障。

       

      Abstract: To improve the defect detection ability of ultrasonic full focusing inspection for media with strong lateral velocity variation, a gradual multi-transducer frequency-domain full focusing method based on phase shift migration (GMTFM-PSM) is proposed. Based on the traditional ultrasonic full focusing imaging inspection, this technique employs excitation signals of multiple transducer groups, reducing the sound velocity attenuation of composite materials with strong lateral velocity variation, as well as enhancing sound beam directivity and signal strength. The phase shift migration algorithm is introduced to correct the sound velocity propagating in material, improving the migration factor accuracy and effectively reducing the influence of the complex multi-layer structure of composite materials on imaging precision. Combined with the gradual full focusing algorim, side-lobe influence is reduced and the imaging performance is optimized. The results show that compared with the traditional B-scan, SAFT-FW, and TFM-FW imaging algorithms, GMTFM-PSM algorithm reduces quantitative error by 391%, 50%, and 13% respectively, and the defect quantitative error is significantly reduced. The GMTFM-PSM method provides robust technical support for defect detection in materials with strong lateral velocity variations, particularly carbon fiber reinforced composites.

       

    /

    返回文章
    返回