Research on Residual Stress Distribution in Different Areas of Laser-MAG Arc Hybrid Welding by Numerical Simulation

In this research, through experiments and numerical simulations, the residual stress distribution of the top and bottom surfaces of the laser (TruDisk16002)-arc (MAG) hybrid welding seam and the weld cross-section are studied. The results show that when the arc power is 6.5KW and the laser power is 7.5KW, the weld is formed well. The residual stress on the bottom surface near the weld is higher than that on the top surface. The laser zone in the center of the weld has the largest residual stress, the arc zone is smaller, and the mixed zone is the smallest. The laser zone has the largest residual stress at the fusion line and the heat-affected zone, followed by the mixed zone, and the arc zone is the smallest. followed by the mixed zone, and the arc zone has the smallest.

Welding seam recognition and tracking for a novel mobile welding robot based on multi-layer sensing strategy

Mobile welding robot with adaptive seam tracking ability can greatly improve the welding efficiency and quality, which has been extensively studied. To further improve the automation in multiple station welding, a novel intelligent mobile welding robot consists of a four-wheeled mobile platform and a collaborative manipulator is developed. Under the support of simultaneous localization and mapping (SLAM) technology, the robot is capable of automatically navigating to different stations to perform welding operation. To automatically detect the welding seam, a composite sensor system including an RGB-D camera and a laser vision sensor is creatively applied. Based on the sensor system, the multi-layer sensing strategy is performed to ensure the welding seam can be detected and tracked with high precision. By applying hybrid filter to the RGB-D camera measurement, the initial welding seam could be effectively extracted. Then a novel welding start point detection method is proposed. Meanwhile, to guarantee the tracking quality, a robust welding seam tracking algorithm based on laser vision sensor is presented to eliminate the tracking discrepancy caused by the platform parking error, through which the tracking trajectory can be corrected in real-time. The experimental results show that the robot can autonomously detect and track the welding seam effectively in different station. Also, the multiple station welding efficiency can be improved and quality can also be guaranteed.

A Efficient System based on Model Segmentation for Weld Seam Grinding Robot

Uneven surface quality often occurs when butt welds are manually grinding, so robotic weld grinding automation has become a fast-developing trend. Weld seam extraction and trajectory planning are important for automatic control of grinding process. However, most of the research on weld extraction is focused on before welding. Due to the irregular shape of the weld after welding, and too little work has been devoted to the weld identification after welding. Consequently, in this paper, a novel simple and efficient weld extraction algorithm is proposed, and the robot grinding path is planned. Firstly, a new flexible bracket structure for welding seam extraction is designed. Secondly, the weld seam section profile model is established, and the processing of spatial point cloud problem is transformed into the processing of two-dimensional point cloud problem. The least square method (LSM) based on threshold comparison is used to segment the weld seam, which greatly improved the processing speed and accuracy. Then the grinding path and pose are obtained according to the extracted weld space structure. Finally, a robotic welding seam automatic grinding system is built. Experiments show that the proposed method could well extract the irregular weld contour after welding and the grinding system built is reliable, which greatly improves the grinding efficiency.

Semi-Supervised Training for Positioning of Welding Seams

Robotic welding often uses vision-based measurement to find the correct placement of the welding seam. Traditional machine vision methods work well in many cases but lack robustness when faced with variations in the manufacturing process or in the imaging conditions. While supervised deep neural networks have been successful in increasing accuracy and robustness in many real-world measurement applications, their success relies on labeled data. In this paper, we employ semi-supervised learning to simultaneously increase accuracy and robustness while avoiding expensive and time-consuming labeling efforts by a domain expert. While semi-supervised learning approaches for various image classification tasks exist, we purpose a novel algorithm for semi-supervised key-point detection for seam placement by a welding robot. We demonstrate that our approach can work robustly with as few as fifteen labeled images. In addition, our method utilizes full image resolution to enhance the accuracy of the key-point detection in seam placement.

Deep Learning Model Explainability for Inspection Accuracy Improvement in the Automotive Industry

The welding seams visual inspection is still manually operated by humans in different companies, so the result of the test is still highly subjective and expensive. At present, the integration of deep learning methods for welds classification is a research focus in engineering applications. This work intends to apprehend and emphasize the contribution of deep learning model explainability to the improvement of welding seams classification accuracy and reliability, two of the various metrics affecting the production lines and cost in the automotive industry. For this purpose, we implement a novel hybrid method that relies on combining the model prediction scores and visual explanation heatmap of the model in order to make a more accurate classification of welding seam defects and improve both its performance and its reliability. The results show that the hybrid model performance is relatively above our target performance and helps to increase the accuracy by at least 18%, which presents new perspectives to the developments of deep Learning explainability and interpretability.

Weld defect inspection based on machine vision and weak magnetic technology

This study proposes a weld defect inspection method based on a combination of machine vision and weak magnetic technology to inspect the quality of weld formation comprehensively. In accordance with the principle of laser triangulation, surface information about the weldment is obtained, the weld area is extracted using mutation characteristics of the weld edge and an algorithm for identifying defects with abnormal average height in the weld surface is proposed. Subsequently, a welding seam inspection process is developed and implemented, which is composed of a camera, a structured light sensor, a magnetic sensor and a motion control system. Inspection results from an austenitic stainless steel weldment show that the method combining machine vision and magnetism can identify defect locations accurately. Comprehensive analysis of the test results can effectively classify surface and internal defects, estimate the equivalent sizes of defects and evaluate the quality of weld formation in multiple dimensions.

Analysis of Defects at Laser Welding of Heat-Resistant Alloy KhN45VMTYuBR

The article presents the results of the impact of laser welding parameters on defect occurrence in the welded seam of the Ni-based heat-resistant alloy Kh45VMTYuBR applied for the manufacture of gas-turbine engines. On the basis of the research the authors conduct the analysis of dimensions as well as the number of pores and micro-cracks in the welded seam. The paper provides the recommendations on the selection of the laser welding mode for the heat-resistant alloy to reduce defect occurrence in a welding seam.