A path to in-space welding and to other in-space metal processing technologies using Space Shuttle small payloads

As we venture into space, it becomes necessary to assemble, expand, and repair space-based structures for our housing, research, and manufacturing. The zero gravity-vacuum of space challenges us to employ construction options which are commonplace on Earth. Rockwell International (RI) has begun to undertake the challenge of space-based construction via numerous options, of which one is welding. As of today, RI divisions have developed appropriate resources and technologies to bring space-based welding within our grasp. Further work, specifically in the area of developing space experiments to test RI technology, is required. RI Space Welding Project's achievements to date, from research and development (R&E) efforts in the areas of microgravity, vacuum, intra- / extra- vehicular activity and spinoff technologies, are reviewed. Special emphasis is given to results for G-169's (Get Away Special) microgravity flights aboard a NASA KC-135. Based on these achievements, a path to actual development of a space welding system is proposed with options to explore spinoff in-space metal processing technologies. This path is constructed by following a series of milestone experiments, of which several are to utilize NASA's Shuttle Small Payload Programs. Conceptual designs of the proposed shuttle payload experiments are discussed with application of lessons learned from G-169's design, development, integration, testing, safety approval process, and KC-135 flights

Vision-aided Monitoring and Control of Thermal Spray, Spray Forming, and Welding Processes

Vision is one of the most powerful forms of non-contact sensing for monitoring and control of manufacturing processes. However, processes involving an arc plasma or flame such as welding or thermal spraying pose particularly challenging problems to conventional vision sensing and processing techniques. The arc or plasma is not typically limited to a single spectral region and thus cannot be easily filtered out optically. This paper presents an innovative vision sensing system that uses intense stroboscopic illumination to overpower the arc light and produce a video image that is free of arc light or glare and dedicated image processing and analysis schemes that can enhance the video images or extract features of interest and produce quantitative process measures which can be used for process monitoring and control. Results of two SBIR programs sponsored by NASA and DOE and focusing on the application of this innovative vision sensing and processing technology to thermal spraying and welding process monitoring and control are discussed

Measurement of local creep properties in stainless steel welds

A high temperature measurement system for creep deformation based on the digital image correlation (DIC) technique is described. The new system is applied to study the behaviour of a multi-pass welded joint in a high temperature tensile test and a load controlled creep test at 545°C. Spatially resolved tensile properties and time dependent creep deformation properties across a thick section type 316 stainless steel multi-pass welded joint are presented and discussed. Significantly lower creep strain rates are observed in the HAZ than in the parent material which is attributed to the introduction of substantial plastic strain in the parent material on initial loading. The weld metal shows the fastest creep rates and a variation that appear to correlate with individual weld passes. The visual information provides not only the local creep strain distribution but also the reduction of area and true stress distribution based on strains measured in the transverse direction. The results demonstrate the capability of the DIC technique for full field measurement of displacement and strain at high temperature long term creep tests

Weld analysis and control system

The invention is a Weld Analysis and Control System developed for active weld system control through real time weld data acquisition. Closed-loop control is based on analysis of weld system parameters and weld geometry. The system is adapted for use with automated welding apparatus having a weld controller which is capable of active electronic control of all aspects of a welding operation. Enhanced graphics and data displays are provided for post-weld analysis. The system provides parameter acquisition, including seam location which is acquired for active torch cross-seam positioning. Torch stand-off is also monitored for control. Weld bead and parent surface geometrical parameters are acquired as an indication of weld quality. These parameters include mismatch, peaking, undercut, underfill, crown height, weld width, puddle diameter, and other measurable information about the weld puddle regions, such as puddle symmetry, etc. These parameters provide a basis for active control as well as post-weld quality analysis and verification. Weld system parameters, such as voltage, current and wire feed rate, are also monitored and archived for correlation with quality parameters

END-TO-END PREDICTION OF WELD PENETRATION IN REAL TIME BASED ON DEEP LEARNING

Welding is an important joining technique that has been automated/robotized. In automated/robotic welding applications, however, the parameters are preset and are not adaptively adjusted to overcome unpredicted disturbances, which cause these applications to not be able to meet the standards from welding/manufacturing industry in terms of quality, efficiency, and individuality. Combining information sensing and processing with traditional welding techniques is a significant step toward revolutionizing the welding industry. In practical welding, the weld penetration as measured by the back-side bead width is a critical factor when determining the integrity of the weld produced. However, the back-side bead width is difficult to be directly monitored during manufacturing because it occurs underneath the surface of the welded workpiece. Therefore, predicting back-side bead width based on conveniently sensible information from the welding process is a fundamental issue in intelligent welding. Traditional research methods involve an indirect process that includes defining and extracting key characteristic information from the sensed data and building a model to predict the target information from the characteristic information. Due to a lack of feature information, the cumulative error of the extracted information and the complex sensing process directly affect prediction accuracy and real-time performance. An end-to-end, data-driven prediction system is proposed to predict the weld penetration status from top-side images during welding. In this method, a passive-vision sensing system with two cameras to simultaneously monitor the top-side and back-bead information is developed. Then the weld joints are classified into three classes (i.e., under penetration, desirable penetration, and excessive penetration) according to the back-bead width. Taking the weld pool-arc images as inputs and corresponding penetration statuses as labels, an end-to-end convolutional neural network (CNN) is designed and trained so the features are automatically defined and extracted. In order to increase accuracy and training speed, a transfer learning approach based on a residual neural network (ResNet) is developed. This ResNet-based model is pre-trained on an ImageNet dataset to process a better feature-extracting ability, and its fully connected layers are modified based on our own dataset. Our experiments show that this transfer learning approach can decrease training time and improve performance. Furthermore, this study proposes that the present weld pool-arc image is fused with two previous images that were acquired 1/6s and 2/6s earlier. The fused single image thus reflects the dynamic welding phenomena, and prediction accuracy is significantly improved with the image-sequence data by fusing temporal information to the input layer of the CNN (early fusion). Due to the critical role of weld penetration and the negligible impact on system implementation, this method represents major progress in the field of weld-penetration monitoring and is expected to provide more significant improvements during welding using pulsed current where the process becomes highly dynamic

Robotic and automatic welding development at the Marshall Space Flight Center

Welding automation is the key to two major development programs to improve quality and reduce the cost of manufacturing space hardware currently undertaken by the Materials and Processes Laboratory of the NASA Marshall Space Flight Center. Variable polarity plasma arc welding has demonstrated its effectiveness on class 1 aluminum welding in external tank production. More than three miles of welds were completed without an internal defect. Much of this success can be credited to automation developments which stabilize the process. Robotic manipulation technology is under development for automation of welds on the Space Shuttle's main engines utilizing pathfinder systems in development of tooling and sensors for the production applications. The overall approach to welding automation development undertaken is outlined. Advanced sensors and control systems methodologies are described that combine to make aerospace quality welds with a minimum of dependence on operator skill

Visual Sensing and Defect Detection of Gas Tungsten Arc Welding

Weld imperfections or defects such as incomplete penetration and lack of fusion are critical issues that affect the integration of welding components. The molten weld pool geometry is the major source of information related to the formation of these defects. In this dissertation, a new visual sensing system has been designed and set up to obtain weld pool images during GTAW. The weld pool dynamical behavior can be monitored using both active and passive vision method with the interference of arc light in the image significantly reduced through the narrow band pass filter and laser based auxiliary light source.Computer vision algorithms based on passive vision images were developed to measure the 3D weld pool surface geometry in real time. Specifically, a new method based on the reversed electrode image (REI) was developed to calculate weld pool surface height in real time. Meanwhile, the 2D weld pool boundary was extracted with landmarks detection algorithms. The method was verified with bead-on-plate and butt-joint welding experiments.Supervised machine learning was used to develop the capability to predict, in real-time, the incomplete penetration on thin SS304 plate with the key features extracted from weld pool images. An integrated self-adaptive close loop control system consisting the non-contact visual sensor, machine learning based defect predictor, and welding power source was developed for real-time welding penetration control for bead on plate welding. Moreover, the data driven methods were first applied to detect incomplete penetration and LOF in multi-pass U groove welding. New features extracted from reversed electrode image played the most important role to predict these defects. Finally, real time welding experiments were conducted to verify the feasibility of the developed models

Intelligent 3D seam tracking and adaptable weld process control for robotic TIG welding

Tungsten Inert Gas (TIG) welding is extensively used in aerospace applications, due to its unique ability to produce higher quality welds compared to other shielded arc welding types. However, most TIG welding is performed manually and has not achieved the levels of automation that other welding techniques have. This is mostly attributed to the lack of process knowledge and adaptability to complexities, such as mismatches due to part fit-up. Recent advances in automation have enabled the use of industrial robots for complex tasks that require intelligent decision making, predominantly through sensors. Applications such as TIG welding of aerospace components require tight tolerances and need intelligent decision making capability to accommodate any unexpected variation and to carry out welding of complex geometries. Such decision making procedures must be based on the feedback about the weld profile geometry. In this thesis, a real-time position based closed loop system was developed with a six axis industrial robot (KUKA KR 16) and a laser triangulation based sensor (Micro-Epsilon Scan control 2900-25). [Continues.