Explore the vast range of titles available.

Real-time tracking and alignment of the welding torch with the center of the weld seam are critical for automatic root pass welding of medium-thick plates. However, assembly errors, heat input, and interference from droplets on the arc signal cause real-time changes in the weld seam and gap, making real-time tracking of root pass welding with a variable gap highly challenging. In this study, we propose a tracking method of root pass welding with variable gap based on the magnetically controlled arc sensor. First, a coded magnetically controlled arc sensor was developed for the acquisition of weld seam information. Secondly, combined with the simplified model of arc scanning welds, an arc signal compensation method of selectively taking the average based on the Ransac algorithm was proposed. Finally, we proposed an optimized weld deviation detection method by combining the mathematical model of magnetically controlled arc oscillation with the mathematical model of droplet transition size and frequency under the influence of an alternating magnetic field. Weld deviation detection was performed using the compensated arc signal combined with the positioning information provided by the excitation current coding sequence. The experiment results showed that the maximum detection errors in the Y-axis and Z-axis directions do not exceed 0.40 mm and 0.23 mm, respectively. Our proposed method can effectively track the weld seam of root pass welding with a variable gap.

The inner wall of tubular equipment features a concave structure, making it difficult to perform conventional ultrasonic testing. This paper proposes a novel method for ultrasonic testing by placing a flexible phased array on the inner wall of pipelines and develops a flexible phased array ultrasonic mechanical scanning device suitable for internal inspection of tubular equipment. This device can control the flexible phased array probe to achieve 360° circumferential scanning and axial scanning, thereby obtaining C-scan imaging of the entire two-dimensional cross-section of tubular equipment. Through testing experiments, defects such as cracks, porosity, and lack of fusion within tubular equipment have been successfully detected, and the reliability of the device is verified. Finally, engineering applications on a batch of pressure vessels produced by pressure vessel manufacturers revealed welding defects in the fillet welds of these vessels, demonstrating high potential for promotion and application.

Friction stir welding (FSW) is a green, environmentally amicable, and solid-state joining technology. In this research, FSW is successfully executed for yellow/naval brass 405–20 that has ample applications in plumbers’ flanges, fittings, ornamental, hardware, and ship trim. Moreover, both the naval brass 405–20 and the tool material (M2 HSS) utilized are novel. The effect of two friction stir weld factors (FSWF), rotational speed (RS) and traverse speed (TS), was found on three output parameters, i.e., weld temperature, weld strength, and weld hardness. Three levels of both rotational speed (1300, 1450, and 1600) rpm and traverse speed (40, 50, and 60) mm/s were selected in this work to execute full factorial design of experiments (DOEs). The highest weld temperature developed while FSW was found to be 63.72% of melting point of base metal. A significant improvement in friction stir weld strength (FSWS) was also measured that was found to be 106.37% of the base brass strength. Finally, weld hardness was measured which was found to be 87.80% of original brass hardness. Optimal FSW factors were found to be 1450 rpm and 60 mm/min resulting interestingly in optimal temperature, optimal strength, and optimal hardness. Rotational speed (RS) was also found to be significant only towards the weld temperature at the friction stir weld zone (FSWZ) with the highest percent contribution (PCR) of 65.69%. Moreover, a numerical validation for weld temperature only was also performed along with empirical validations each for temperature, strength, and hardness of joint. The investigation of FSWZ using possible magnification settings of coordinate measuring machine (CMM) and scanning electron microscope (SEM) resulted in findings such as regular circular patterns, thin burrs of welded brass, crazing pores initiating the cracks leading further to joint failure, and likelihood of dezincification within brass.

Weld seam detection is an important part of automated welding. At present, few studies have been conducted on annular weld seams, and a lot of defects exist in the point cloud model of the tube sheet obtained by RGB-D cameras and photography methods. Aiming at the above problems, this paper proposed an annular weld seam detection network named WeldNet where a voxel feature encoding layer was adaptively improved for annular weld seams, the sparse convolutional network and region proposal network (RPN) were used to detect annular weld seam position, and an annular weld seam detection loss function was designed. Further, an annular weld seam dataset was established to train the network. Compared with the random sampling consistency (RANSAC) method, WeldNet has a higher detection accuracy, as well as a higher detection success rate which has increased by 23%. Compared with U-Net, WeldNet has been proven to achieve a better detection result, and the intersection over the union of the weld seam detection is improved by 17.8%.

One of the prospective methods of robotic welding with a consumable electrode in shield gas metal arc welding is the GMAW Rapid HF process (GRHF, HF-high frequency), in which welded joints with deep penetration welds are obtained thanks to the specially programmed welding characteristics of the arc. A pulsed frequency equalized to 5000 Hz was used to achieve consumable electrode arc stabilization and improve penetration. This work consists of two main sections, including the research and analysis of wire electrode melting and weld pool formation in the innovative GRHF process and its influences on joint strength and the economic advantages of welding. As a result of our research and strength tests, as well as an image analysis of phenomena occurring in the welding arc and weld pool, assumptions were developed about the use of the GRHF process, which is characterized by deep penetration welds without welding imperfections that reduce the quality of the welded joints and their strength. Welding conditions and parameters leading to welded joints characterized by high relative strength related to the weight of the used filler material were proposed. As a result of our research, it was found that the use of welding processes with deep penetration leads to material savings related to the reduced consumption of filler materials while maintaining the required high strength of welded joints. Savings of filler materials reaching 80% were achieved compared with hitherto used methods. At the same time, the maximum load-carrying capacity of welding joints was maintained.

Weld bead measurement and weld quality inspection are important parts in industrial welding. In this paper, a structured light vision sensor is developed to achieve on-line weld bead measurement and weld quality inspection. Firstly, a structured light vision sensor with a narrow-band optical filter is developed to reduce welding noises such as arc lights and splashes. Secondly, the weld bead type identification algorithm including image pre-processing, baseline extraction, and weld bead classification is proposed to classify filling weld bead and capping weld bead. Thirdly, feature extraction algorithms of filling weld bead and capping weld bead are presented to obtain corresponding feature points. Combining the image coordinates of feature points with structured light vision model, the weld bead size could be obtained and the weld quality could be evaluated. Finally, many weld bead measurement and weld quality inspection experiments are conducted. Experimental results demonstrate that the developed structured light vision sensor and proposed methods could achieve satisfactory performance for weld quality inspection.

Even though arc welding is widely utilized to join metallic parts with high reliability, the prediction and control of welding quality is challenging owing to difficulties in the prediction of weld penetration depth and the backside bead. In this study, an effective method for predicting weld penetration based on deep learning was proposed to control the welding quality in-process. The topside weld pool image was closely related to the welding quality and penetration depth and was also an accurate indicator of the state of welding over time. A prediction model for penetration depth using a topside weld pool image was constructed. Semantic segmentation based on a residual neural network was then performed on the acquired weld pool image. Consequently, an accurate weld pool shape was extracted. In addition, a penetration regression model was constructed based on a back-propagation neural network. Finally, the penetration depth (corresponding to the weld pool shape) was extracted via segmentation. The segmentation and regression models were combined to create a penetration prediction model. Considering a gas tungsten arc welding (GTAW) process, the predictions obtained from the proposed method were evaluated experimentally. In the validation process, the developed model quantitatively predicted the penetration depth in tungsten gas arc welding. The mean absolute error was 0.0596 mm with an R2 value of 0.9974. The model developed in this study can be utilized to predict weld depth penetration and in-processing time using surface images of the weld pool.

In the oil and gas pipeline industry, numerous small-diameter branch pipe fillet welds exist, which are prone to stress concentration because of diverse geometric shapes. The internal welding defects within these welds pose severe hazards to safe production. Specifically, the irregular geometry often leads to internal root defects where the weld metal fails to fully penetrate the joint or fuse with the base material (referred to as incomplete penetration and incomplete fusion). This study developed a GF-CF-GF (CF is carbon fiber, GF is glass fiber) sandwich composite reinforcement structure for pipe fittings with these specific internal defects (main pipe: Φ323.9 × 12.5 mm; branch pipe: Φ76 × 5 mm) through a combination of finite element analysis (FEA) and burst test verification. The inherent correlation between structural factors and pressure-bearing capacity was revealed by analyzing the influence of defect sizes. Based on FEA, the repair layer coverage should be designed to be within 400 mm from the defect along the main pipe wall direction and within 100 mm from the defect along the branch pipe wall direction, with required thicknesses of 5.6 mm for incomplete penetration and 3.2 mm for incomplete fusion. Analysis of the actual burst test pressure curve showed that the elastic-plastic transition interval of the repaired pipes increased by approximately 2 MPa compared to normal undamaged pipes, and their pressure-bearing capacities rose by 1.57 MPa (incomplete penetration) and 1.76 MPa (incomplete fusion). These results demonstrate the feasibility of the proposed reinforcement design, which has potential applications in the safety and integrity of oil and gas transportation.

The Harlem Renaissance was a watershed moment for racial uplift, poetic innovation, sexual liberation, and female empowerment.Aphrodite's Daughtersintroduces us to three amazing women who were at the forefront of all these developments, poetic iconoclasts who pioneered new and candidly erotic forms of female self-expression. Maureen Honey paints a vivid portrait of three African American women-Angelina Weld Grimké, Gwendolyn B. Bennett, and Mae V. Cowdery-who came from very different backgrounds but converged in late 1920s Harlem to leave a major mark on the literary landscape. She examines the varied ways these poets articulated female sexual desire, ranging from Grimké's invocation of a Sapphic goddess figure to Cowdery's frank depiction of bisexual erotics to Bennett's risky exploration of the borders between sexual pleasure and pain. Yet Honey also considers how they were united in their commitment to the female body as a primary source of meaning, strength, and transcendence. The product of extensive archival research,Aphrodite's Daughtersdraws from Grimké, Bennett, and Cowdery's published and unpublished poetry, along with rare periodicals and biographical materials, to immerse us in the lives of these remarkable women and the world in which they lived. It thus not only shows us how their artistic contributions and cultural interventions were vital to their own era, but also demonstrates how the poetic heart of their work keeps on beating.

Aiming at the interference of light and scratches in the welding scene, the traditional weld edge detection accuracy is limited. A weld edge detection method based on the improved Canny algorithm is proposed. First, a Gaussian adaptive bilateral filter (GABF) is constructed to suppress the noise and retain the edge details. Then, a four-direction 3×3 operator template is introduced for gradient calculation. Then, the optimal threshold is selected by combining the adaptive threshold iteration algorithm and the maximum inter-class variance method. Finally, false edges shorter than the threshold are filtered out by calculating the number and length of edge point connectivity. The experimental results show that the improved algorithm achieves more than 95% in structural similarity and significant improvement in connectivity, which realizes the effective recognition of weld seams.