Investigation of weld joint detection capabilities of a coaxial weld vision system

This report describes the second phase of a series of evaluations of a vision-based welding control sensor for the Space shuttle Main Engine Robotic Welding System. The robotic welding system is presently under development at the Marshall Space Flight Center. This evaluation determines the factors influencing the minimum joint gap required for consistent detection of the weld joint

TOWARD INTELLIGENT WELDING BY BUILDING ITS DIGITAL TWIN

To meet the increasing requirements for production on individualization, efficiency and quality, traditional manufacturing processes are evolving to smart manufacturing with the support from the information technology advancements including cyber-physical systems (CPS), Internet of Things (IoT), big industrial data, and artificial intelligence (AI). The pre-requirement for integrating with these advanced information technologies is to digitalize manufacturing processes such that they can be analyzed, controlled, and interacted with other digitalized components. Digital twin is developed as a general framework to do that by building the digital replicas for the physical entities. This work takes welding manufacturing as the case study to accelerate its transition to intelligent welding by building its digital twin and contributes to digital twin in the following two aspects (1) increasing the information analysis and reasoning ability by integrating deep learning; (2) enhancing the human user operative ability to physical welding manufacturing via digital twins by integrating human-robot interaction (HRI). Firstly, a digital twin of pulsed gas tungsten arc welding (GTAW-P) is developed by integrating deep learning to offer the strong feature extraction and analysis ability. In such a system, the direct information including weld pool images, arc images, welding current and arc voltage is collected by cameras and arc sensors. The undirect information determining the welding quality, i.e., weld joint top-side bead width (TSBW) and back-side bead width (BSBW), is computed by a traditional image processing method and a deep convolutional neural network (CNN) respectively. Based on that, the weld joint geometrical size is controlled to meet the quality requirement in various welding conditions. In the meantime, this developed digital twin is visualized to offer a graphical user interface (GUI) to human users for their effective and intuitive perception to physical welding processes. Secondly, in order to enhance the human operative ability to the physical welding processes via digital twins, HRI is integrated taking virtual reality (VR) as the interface which could transmit the information bidirectionally i.e., transmitting the human commends to welding robots and visualizing the digital twin to human users. Six welders, skilled and unskilled, tested this system by completing the same welding job but demonstrate different patterns and resulted welding qualities. To differentiate their skill levels (skilled or unskilled) from their demonstrated operations, a data-driven approach, FFT-PCA-SVM as a combination of fast Fourier transform (FFT), principal component analysis (PCA), and support vector machine (SVM) is developed and demonstrates the 94.44% classification accuracy. The robots can also work as an assistant to help the human welders to complete the welding tasks by recognizing and executing the intended welding operations. This is done by a developed human intention recognition algorithm based on hidden Markov model (HMM) and the welding experiments show that developed robot-assisted welding can help to improve welding quality. To further take the advantages of the robots i.e., movement accuracy and stability, the role of the robot upgrades to be a collaborator from an assistant to complete a subtask independently i.e., torch weaving and automatic seam tracking in weaving GTAW. The other subtask i.e., welding torch moving along the weld seam is completed by the human users who can adjust the travel speed to control the heat input and ensure the good welding quality. By doing that, the advantages of humans (intelligence) and robots (accuracy and stability) are combined together under this human-robot collaboration framework. The developed digital twin for welding manufacturing helps to promote the next-generation intelligent welding and can be applied in other similar manufacturing processes easily after small modifications including painting, spraying and additive manufacturing

Development of a real-time ultrasonic sensing system for automated and robotic welding

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.The implementation of robotic technology into welding processes is made difficult by the inherent process variables of part location, fit up, orientation and repeatability. Considering these aspects, to ensure weld reproducibility consistency and quality, advanced adaptive control techniques are essential. These involve not only the development of adequate sensors for seam tracking and joint recognition but also developments of overall machines with a level of artificial intelligence sufficient for automated welding. The development of such a prototype system which utilizes a manipulator arm, ultrasonic sensors and a transistorised welding power source is outlined. This system incorporates three essential aspects. It locates and tracks the welding seam ensuring correct positioning of the welding head relatively to the joint preparation. Additionally, it monitors the joint profile of the molten weld pool and modifies the relevant heat input parameters ensuring consistent penetration, joint filling and acceptable weld bead shape. Finally, it makes use of both the above information to reconstruct three-dimensional images of the weld pool silhouettes providing in-process inspection capabilities of the welded joints. Welding process control strategies have been incorporated into the system based on quantitative relationships between input parameters and weld bead shape configuration allowing real-time decisions to be made during the process of welding, without the need for operation intervention.British Technology Group (BTG

Robotic friction stir welding—Seam-tracking control, force control and process supervision

Purpose – This study aims to enable robotic friction stir welding (FSW) in practice. The use of robots has hitherto been limited, because of the large contact forces necessary for FSW. These forces are detrimental for the position accuracy of the robot. In this context, it is not sufficient to rely on the robot’s internal sensors for positioning. This paper describes and evaluates a new method for overcoming this issue.Design/methodology/approach – A closed-loop robot control system for seam-tracking control and force control, running and recording data in real-time operation, was developed. The complete system was experimentally verified. External position measurements were obtained from a laser seam tracker and deviations from the seam were compensated for, using feedback of the measurements to a position controller.Findings – The proposed system was shown to be working well in overcoming position error. The system is flexible and reconfigurable for batch and short production runs. The welds were free of defects and had beneficial mechanical properties.Research limitations/implications – In the experiments, the laser seam tracker was used both for control feedback and for performance evaluation. For evaluation, it would be better to use yet another external sensor for position measurements, providing ground truth.Practical implications – These results imply that robotic FSW is practically realizable, with the accuracy requirements fulfilled.Originality/value – The method proposed in this research yields very accurate seam tracking as compared to previous research. This accuracy, in turn, is crucial for the quality of the resulting material.Keywords Friction stir welding, Robotics, Force control, Seam-tracking control, Control, Sensors, Robot weldin

Welding Inspection System

This Project aims at building an automated welding system that can track a weld seam, calculate its area and depth and accordingly do the welding and also consider to keep the defects to a minimum so that better quality of weld can be given as output. It consists of a laser sensor that illuminates the weld seam so that the CCD/CMOS camera used can get the image of the weld seam. Then this image is transferred to MATLAB for image processing where the image is converted to grey scale and the grey scale image is then divided into two parts first part consists of the seam section which is colored grey and have red dots and other sections is considered as white section which don’t have any utilization till the welding starts. After the image processing the part is carried to the welding section where the welding can be done with the help of the data abstracted from the above methods and then it minimizes the defect of welding and produces the output

Feasibility of remotely manipulated welding in space. A step in the development of novel joining technologies

In order to establish permanent human presence in space technologies of constructing and repairing space stations and other space structures must be developed. Most construction jobs are performed on earth and the fabricated modules will then be delivered to space by the Space Shuttle. Only limited final assembly jobs, which are primarily mechanical fastening, will be performed on site in space. Such fabrication plans, however, limit the designs of these structures, because each module must fit inside the transport vehicle and must withstand launching stresses which are considerably high. Large-scale utilization of space necessitates more extensive construction work on site. Furthermore, continuous operations of space stations and other structures require maintenance and repairs of structural components as well as of tools and equipment on these space structures. Metal joining technologies, and especially high-quality welding, in space need developing