Ultrasonic signal classification in nondestructive testing is of great significance for the detection of defects. The current methods have mainly utilized low-level handcrafted features based on traditional signal processing approaches, such as the Fourier transform, wavelet transform and the like, to interpret the information carried by signals for classification. This paper proposes an automatic classification method via a convolutional neural network (CNN) which can automatically extract features from raw data to classify ultrasonic signals collected of a circumferential weld composed of austenitic and martensitic stainless steel with internal slots. Experiments demonstrate that our method outperforms the traditional classifier with manually extracted features, achieving an accuracy rate of classification up to 0.982. Furthermore, we visualize the shape, location and orientation of defects with a C-scan imaging process based on classification results, validating the effectiveness of the results.

The automatic phased array ultrasonic testing method has been developed for inspecting the pipe welds with complex geometry. Applying time of flight diffraction (TOFD) technique has two problems: one is the difficulty in recognition of head wave under complex structure; the other is the limited scanning space cannot provide the needed span for pitch-catch mode. To overcome these two problems, an array probe can be placed on the surface of base metal of the weld by the direct and double traverse techniques. In order to design the optimal scanning path for high probability of detection and inspection rate, the model of beam path on tilt steps was established to quantitatively analyze the effects of tilt steps on beam coverage and mode conversion. Experiments were conducted to validate the proposed rule of planning the optimal scanning path. All artificial defects in the mock-up block were detected on the scanning side of which exists the optimal scanning path. While for the scanning side without the optimal scanning path, a combination of scanning is needed to avoid missing inspection.