Variational mode decomposition based self-adaptive denoising imaging method for ultrasonic array testing of coarse-grained titanium alloys processed by additive manufacturing

Abstract

The coarse columnar grain structure in titanium alloys processed by additive manufacturing leads to strong grain noise in ultrasonic testing, which affects the accurate detection and quantification of its defects. In this paper, a self-adaptive denoising imaging method for ultrasonic array testing is proposed based on variational mode decomposition (VMD). The proposed method uses VMD to decompose complex ultrasonic signals in full matrix capture (FMC) data collected by a linear ultrasonic array transducer. According to the particle swarm optimization (PSO) method, VMD parameters for FMC methods of the total focusing method (TFM), the plane wave imaging (PWI), and the Hadamard spatial encoding are self-adaptively determined. The evaluation parameters are set to select the intrinsic mode function components by VMD to complete the denoising reconstruction and imaging of FMC data. An additive-manufactured titanium alloy specimen was used to verify the improvement of defect detection capability by PSO-VMD denoising method. For a defect at large thickness in the specimen, compared with the conventional TFM and PWI methods, VMD denoising method based on the Hadamard spatial encoding can improve the signal-to-noise ratio by 7.7 dB and 9.0 dB, and reduce the quantitative error by 74 % and 68 %.