A Water Environment-Based Simulated Method for Ultrasonic Testing of Slag Inclusion Weld Defects Based on Improved VMD.

The identification of slag inclusion defects in welds is of the utmost importance in guaranteeing the integrity, safety, and prolonged service life of welded structures. Most research focuses on different kinds of weld defects, but branch research on categories of slag inclusion material is limited and critical for safeguarding the quality of engineering and the well-being of personnel. To address this issue, we design a simulated method using ultrasonic testing to identify the inclusion of material categories in austenitic stainless steel. It is based on a simulated experiment in a water environment, and six categories of cubic specimens, including four metallic and two non-metallic materials, are selected to simulate the slag materials of the inclusion defects. Variational mode decomposition optimized by particle swarm optimization is employed for ultrasonic signals denoising. Moreover, the phase spectrum of the denoised signal is utilized to extract the phase characteristic of the echo signal from the water-slag specimen interface. The experimental results show that our method has the characteristics of appropriate decomposition and good denoising performance. Compared with famous signal denoising algorithms, the proposed method extracted the lowest number of intrinsic mode functions from the echo signal with the highest signal-to-noise ratio and lowest normalized cross-correlation among all of the comparative algorithms in signal denoising of weld slag inclusion defects. Finally, the phase spectrum can ascertain whether the slag inclusion is a thicker or thinner medium compared with the weld base material based on the half-wave loss existing or not in the echo signal phase.

WD-YOLO: A More Accurate YOLO for Defect Detection in Weld X-ray Images.

X-ray images are an important industrial non-destructive testing method. However, the contrast of some weld seam images is low, and the shapes and sizes of defects vary greatly, which makes it very difficult to detect defects in weld seams. In this paper, we propose a gray value curve enhancement (GCE) module and a model specifically designed for weld defect detection, namely WD-YOLO. The GCE module can improve image contrast to make detection easier. WD-YOLO adopts feature pyramid and path aggregation designs. In particular, we propose the NeXt backbone for extraction and fusion of image features. In the YOLO head, we added a dual attention mechanism to enable the model to better distinguish between foreground and background areas. Experimental results show that our model achieves a satisfactory balance between performance and accuracy. Our model achieved 92.6% mAP@0.5 with 98 frames per second.

A Lossless hybrid wavelet-fractal compression for welding radiographic images.

In this work a lossless wavelet-fractal image coder is proposed. The process starts by compressing and decompressing the original image using wavelet transformation and fractal coding algorithm. The decompressed image is removed from the original one to obtain a residual image which is coded by using Huffman algorithm. Simulation results show that with the proposed scheme, we achieve an infinite peak signal to noise ratio (PSNR) with higher compression ratio compared to typical lossless method. Moreover, the use of wavelet transform speeds up the fractal compression algorithm by reducing the size of the domain pool. The compression results of several welding radiographic images using the proposed scheme are evaluated quantitatively and compared with the results of Huffman coding algorithm.

Deep convolutional neural network for weld defect classification in radiographic images.

The quality of welds is critical to the safety of structures in construction, so early detection of irregularities is crucial. Advances in machine vision inspection technologies, such as deep learning models, have improved the detection of weld defects. This paper presents a new CNN model based on ResNet50 to classify four types of weld defects in radiographic images: crack, pore, non-penetration, and no defect. Stratified cross-validation, data augmentation, and regularization were used to improve generalization and avoid over-fitting. The model was tested on three datasets, RIAWELC, GDXray, and a private dataset of low image quality, obtaining an accuracy of 98.75 %, 90.255 %, and 75.83 %, respectively. The model proposed in this paper achieves high accuracies on different datasets and constitutes a valuable tool to improve the efficiency and effectiveness of quality control processes in the welding industry. Moreover, experimental tests show that the proposed approach performs well on even low-resolution images.

Joining of Polyethylene Using a Non-Conventional Friction Stir Welding Tool.

The objective of the current study was to butt-weld 6 mm-thick polyethylene (PE) plates by friction stir welding (FSW) using a non-conventional stationary shoulder tool. The welds were performed with an unheated shoulder and with a shoulder temperature of 85 °C. Additionally, rotational speeds of 870, 1140 and 1500 rpm; welding speeds of 60 and 120 mm/min; and plunge depths of 5.5 and 5.7 mm were used. The influence of these parameters on morphology, hardness, ultimate tensile strength, elongation at break and fracture modes was evaluated. Shoulder heating proved to be crucial for the optimization of PE joints by FSW, as it clearly improved joint efficiency. Furthermore, shoulder heating promoted the reduction in internal and external defects, such as porosity and surface burning. Defect-free weld seams were obtained with higher rotational speeds and a lower welding speed. A maximum joint efficiency of about 97% was achieved with a shoulder temperature of 85 °C, a rotational speed of 1500 rpm, a welding speed of 60 mm/min and a plunge depth of 5.7 mm. A weld with the average joint efficiency of 92% was produced at 120 mm/min, which based on the literature found is the highest welding speed reported that achieved a joint efficiency above 90%.

Stability of Weld Pool and Elimination of Weld Defects in Aluminum Alloy Plasma Arc Keyhole Welding at Continuously Varying Positions.

Mathematical statistics were used to study the stability of weld pool and the elimination of weld defects in aluminum alloy plasma arc keyhole welding at continuously varying positions. In the mathematical model, the mass transfer position and spatial welding position were taken as the input, and the shape of the welded joints (symmetry/deviation) was taken as the output. The results showed that the fitted curves of the front, back, and average deviations of the weld seam were all similar to the actual curves. According to the optimum results obtained in the experiment and the mathematical models, the mass transfer position only needs to be adjusted once (near to 30°) during the continuously varying positions, from vertical-up to horizontal welding. A breakthrough from fixed environmental variables to dynamic environmental variables in the process control of the keyhole weld pool was realized, which enabled the Al-alloy keyhole weld pool to resist the disturbance caused by gravity during variable position welding. The deviation of the welded joints of the whole plate was smaller than 0.5 mm, and the mechanical properties of the weld reached at least 85% compared to those of the base material, thus meeting the requirements of Al-alloy welding.