Surface and back-side defects identification combined with magnetic flux leakage and boundary magnetic perturbation.

In magnetic flux leakage (MFL) detection, the identification of surface and back-side defects is required to obtain more accurate defect quantification and risk assessment results. However, current MFL techniques can detect both surface and back-side defects but are generally unable to distinguish between them. Therefore, this paper proposes a new boundary magnetic perturbation (BMP) testing method, combining the results of MFL to distinguish between surface and back-side defects. First, the detection mechanism of the BMP testing method and the impact of the tested magnetic flux density components are presented and analyzed by simulations to further develop an identification method. Then, the influences of the BMP sensor's lift-off and installation position are investigated by experiments to improve distinguishing performance. Finally, the repeated measurements show that the surface and back-side defects within the wide range of sizes can be identified accurately, even when the defect depths are in the range of 12.5%-87.5% of the sample thickness. Furthermore, the BMP testing method neither increases the length of the detection device nor requires additional magnetizers or signal generators. Therefore, the proposed method is highly suitable for the existing MFL detection devices to distinguish between surface and back-side defects.

Research on the Weld Position Detection for the T-Joints in Web-Core Sandwich Panels Based on Eddy Current Technology.

Web-core sandwich panels have gained the popularity in various fields, especially aviation and shipbuilding, etc. Penetration welding was considered as an effective process to manufacture such a structure through a T-joint. To ensure the formation quality and mechanical properties of weld, the welding torch needs to be aligned with the T-joint position. However, it is difficult to locate the T-joint position (i.e., the position of core panel) because of the shielding of the face panels. This paper investigated the detection of T-joint position from the face panel side in web-core sandwich panels based on eddy current technology. First, we designed an experimental system for the weld position detection of T-joints from the face panel side. The relationships are investigated between the characteristics of the eddy current detection signal and the primary parameters of the detection system (including excitation frequency, coil outer diameter, and lift off distance) and the T-joint (including thickness of the core panel, gap distance, and thickness of the cover panel). Corresponding experiments were carried out with variable primary parameters, and the influence mechanism of the primary parameters on the detection results in terms of sensitivity and dynamic performance was elaborated to set up the theoretical basis for the detection. Finally, weld position detection experiments were carried out on TC4 titanium alloy T-joint specimens with 3 mm-thick face panel and 5 mm-thick core panel. Results showed that the maximum detection error was 0.482 mm, and the average error was 0.234 mm. This paper provided a possible technical solution to the automatic tracking problem for the welding of T-joints in the web-core sandwich panels.

Research on the Weld Position Detection Method for Sandwich Structures from Face-Panel Side Based on Backscattered X-ray.

Web-core sandwich panels are a typical lightweight structure utilized in a variety of fields, such as naval, aviation, aerospace, etc. Welding is considered as an effective process to join the face panel to the core panel from the face panel side. However, it is difficult to locate the joint position (i.e., the position of core panel) due to the shielding of the face panel. This paper studies a weld position detection method based on X-ray from the face panel side for aluminum web-core sandwich panels used in aviation and naval structures. First, an experimental system was designed for weld position detection, able to quickly acquire the X-ray intensity signal backscattered by the specimen. An effective signal processing method was developed to accurately extract the characteristic value of X-ray intensity signals representing the center of the joint. Secondly, an analytical model was established to calculate and optimize the detection parameters required for detection of the weld position of a given specimen by analyzing the relationship between the backscattered X-ray intensity signal detected by the detector and the parameters of the detection system and specimen during the detection process. Finally, several experiments were carried out on a 6061 aluminum alloy specimen with a thickness of 3 mm. The experimental results demonstrate that the maximum absolute error of the detection was 0.340 mm, which is sufficiently accurate for locating the position of the joint. This paper aims to provide the technical basis for the automatic tracking of weld joints from the face panel side, required for the high-reliability manufacturing of curved sandwich structures.