Automatic Quantification of Subsurface Defects by Analyzing Laser Ultrasonic Signals Using Convolutional Neural Networks and Wavelet Transform

Assessment of mechanical-loss property of 3D printing metal and its application to ultrasonic transducers as vibrating bodies

As miniaturized ultrasonic transducers with sophisticated structure have become increasingly demanded, the vibrating bodies made by conventional metals face the problem of fabricating difficulty and high expenses. The 3D printing metals are prospective materials for their flexibility in forming complicated configurations, but their mechanical-loss properties need clarification as they greatly affect the vibration properties. As a pilot trail, first, an approach to measure the attention coefficients according to the distributions of the vibration velocity and the phase was developed to evaluate their dependence on the strain and the frequency. Then, an aluminum alloy via 3D printing (AlSi10Mg) was employed as the vibrating bodies to form the ultrasonic transducers, whose performance, e.g., vibration properties, temperature rise, and sound pressure level (SPL) in water, was assessed and compared with conventional aluminum alloy (7075). As typical results, AlSi10Mg's damping coefficient is 1.16 times that of 7075 at 33 kHz frequency; this implies the 3D printing process does not deteriorate the aluminum alloy's mechanical-loss property. Meanwhile, AlSi10Mg's damping coefficient reaches 2.19 × 10-4 at the laser power of 350 W, relatively small compared to the values corresponding to other laser powers; this indicates the capability to reduce the mechanical loss by adjusting the parameters during 3D printing possesses. Moreover, the maximum vibration velocity and the SPL of the AlSi10Mg transducer are 1.13 and 1.11 times those of the 7075 transducer that has the same configuration and operates in the same vibration modes. This study enriches the candidate materials as the vibrating bodies of ultrasonic transducers, which potentially meet the demands in wider ultrasonic application fields.

A Review of Laser Ultrasonic Lamb Wave Damage Detection Methods for Thin-Walled Structures

Thin-walled structures, like aircraft skins and ship shells, are often several meters in size but only a few millimeters thick. By utilizing the laser ultrasonic Lamb wave detection method (LU-LDM), signals can be detected over long distances without physical contact. Additionally, this technology offers excellent flexibility in designing the measurement point distribution. The characteristics of LU-LDM are first analyzed in this review, specifically in terms of laser ultrasound and hardware configuration. Next, the methods are categorized based on three criteria: the quantity of collected wavefield data, the spectral domain, and the distribution of measurement points. The advantages and disadvantages of multiple methods are compared, and the suitable conditions for each method are summarized. Thirdly, we summarize four combined methods that balance detection efficiency and accuracy. Finally, several future development trends are suggested, and the current gaps and shortcomings in LU-LDM are highlighted. This review builds a comprehensive framework for LU-LDM for the first time, which is expected to serve as a technical reference for applying this technology in large, thin-walled structures.

A Review of Non-Destructive Testing (NDT) Techniques for Defect Detection: Application to Fusion Welding and Future Wire Arc Additive Manufacturing Processes

In Wire and Arc Additive Manufacturing (WAAM) and fusion welding, various defects such as porosity, cracks, deformation and lack of fusion can occur during the fabrication process. These have a strong impact on the mechanical properties and can also lead to failure of the manufactured parts during service. These defects can be recognized using non-destructive testing (NDT) methods so that the examined workpiece is not harmed. This paper provides a comprehensive overview of various NDT techniques for WAAM and fusion welding, including laser-ultrasonic, acoustic emission with an airborne optical microphone, optical emission spectroscopy, laser-induced breakdown spectroscopy, laser opto-ultrasonic dual detection, thermography and also in-process defect detection via weld current monitoring with an oscilloscope. In addition, the novel research conducted, its operating principle and the equipment required to perform these techniques are presented. The minimum defect size that can be identified via NDT methods has been obtained from previous academic research or from tests carried out by companies. The use of these techniques in WAAM and fusion welding applications makes it possible to detect defects and to take a step towards the production of high-quality final components.

Automated Classification of Ultrasonic Signal via a Convolutional Neural Network

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.