A model comparison to predict heat transfer during spot GTA welding

The present work deals with the estimation of the time evolution of the weld fusion boundary. This moving boundary is the result of a spot GTA welding process on a 316L stainless steel disk. The estimation is based on the iterative regularization method. Indeed, the three problems: direct, in variation and adjoint, classically associated with this method, are solved by the finite element method in a two-dimensional axisymmetric domain. The originality of this work is to treat an experimental estimation of a front motion using a model with a geometry including only the solid phase. In this model, the evolution of this solid domain during the fusion is set with the ALE moving mesh method (Arbitrary Lagrangian Eulerian). The numerical developments are realized with the commercial code Comsol Multiphysics® coupled with the software Matlab®. The estimation method has been validated in a previous work using theoretical data ([1]). The experimental data, used here for this identification are, temperatures measured by thermocouples in the solid phase, the temporal evolution of the melt pool boundary observed at the surface by a fast camera and the maximal dimensions of the melted zone measured on macrographs. These experimental data are also compared with numerical results obtained from a heat and fluid flow model taking into account surface tension effects, Lorentz forces and the deformation of the melt pool surface under arc pressure

Peer Review Report 2008

The purpose of this document is to report findings from the research peer reviews held May 1, May 6 and May 14, 2008 for PHMSA’s Pipeline Safety Research and Development Program. The findings and recommendations in this report derive from the scoring and comments collected from the peer review panelists. Department of Transportation (DOT) Operating Agencies (OA) are required to develop and execute a systematic process for peer reviews and for all influential and highly influential information that the OA plans to disseminate in the foreseeable future. Through the Information Quality Act, Congress directed the Office of Management and Budget (OMB) to “provide policy and procedural guidance to Federal agencies for ensuring and maximizing the quality, objectivity, utility, and integrity of information, (including statistical information) disseminated by Federal agencies.” A resulting OMB Bulletin, titled “Final Information Quality Bulletin for Peer Review,” was issued prescribing required procedures for Federal programs. The Office of the Secretary of Transportation (OST) produced procedures governing modal implementation of this OMB Bulletin. These procedures, as well as the OMB Bulletin, serve as the basis and justification for the PHMSA Pipeline Safety R&D Program peer reviews. The purpose of these peer reviews is to uncover technical problems, keep projects on target or aligned with stakeholder needs and to give technical guidance with technically competent and independent, objective experts

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

Latest Developments in Industrial Hybrid Machine Tools that Combine Additive and Subtractive Operations

Hybrid machine tools combining additive and subtractive processes have arisen as a solution to increasing manufacture requirements, boosting the potentials of both technologies, while compensating and minimizing their limitations. Nevertheless, the idea of hybrid machines is relatively new and there is a notable lack of knowledge about the implications arisen from their in-practice use. Therefore, the main goal of the present paper is to fill the existing gap, giving an insight into the current advancements and pending tasks of hybrid machines both from an academic and industrial perspective. To that end, the technical-economical potentials and challenges emerging from their use are identified and critically discussed. In addition, the current situation and future perspectives of hybrid machines from the point of view of process planning, monitoring, and inspection are analyzed. On the one hand, it is found that hybrid machines enable a more efficient use of the resources available, as well as the production of previously unattainable complex parts. On the other hand, it is concluded that there are still some technological challenges derived from the interaction of additive and subtractive processes to be overcome (e.g., process planning, decision planning, use of cutting fluids, and need for a post-processing) before a full implantation of hybrid machines is fulfilledSpecial thanks are addressed to the Industry and Competitiveness Spanish Ministry for the support on the DPI2016-79889-R INTEGRADDI project and to the PARADDISE project H2020-IND-CE-2016-17/H2020-FOF-2016 of the European Union's Horizon 2020 research and innovation program

Review on the prediction of residual stress in welded steel components

Residual stress after welding has negative effects on the service life of welded steel components or structures. This work reviews three most commonly used methods for predicting residual stress, namely, empirical, semi-empirical and process simulation methods. Basic principles adopted by these methods are introduced. The features and limitations of each method are discussed as well. The empirical method is the most practical but its accuracy relies heavily on experiments. Mechanical theories are employed in the semi-empirical method, while other aspects, such as temperature variation and phase transformation, are simply ignored. The process simulation method has been widely used due to its capability of handling with large and complex components. To improve its accuracy and efficiency, several improvements need to be done for each simulation aspect of this method

Machine Learning Based Defect Detection in Robotic Wire Arc Additive Manufacturing

In the last ten years, research interests in various aspects of the Wire Arc Additive Manufacturing (WAAM) processes have grown exponentially. More recently, efforts to integrate an automatic quality assurance system for the WAAM process are increasing. No reliable online monitoring system for the WAAM process is a key gap to be filled for the commercial application of the technology, as it will enable the components produced by the process to be qualified for the relevant standards and hence be fit for use in critical applications in the aerospace or naval sectors. However, most of the existing monitoring methods only detect or solve issues from a specific sensor, no monitoring system integrated with different sensors or data sources is developed in WAAM in the last three years. In addition, complex principles and calculations of conventional algorithms make it hard to be applied in the manufacturing of WAAM as the character of a long manufacturing cycle. Intelligent algorithms provide in-built advantages in processing and analysing data, especially for large datasets generated during the long manufacturing cycles. In this research, in order to establish an intelligent WAAM defect detection system, two intelligent WAAM defect detection modules are developed successfully. The first module takes welding arc current / voltage signals during the deposition process as inputs and uses algorithms such as support vector machine (SVM) and incremental SVM to identify disturbances and continuously learn new defects. The incremental learning module achieved more than a 90% f1-score on new defects. The second module takes CCD images as inputs and uses object detection algorithms to predict the unfused defect during the WAAM manufacturing process with above 72% mAP. This research paves the path for developing an intelligent WAAM online monitoring system in the future. Together with process modelling, simulation and feedback control, it reveals the future opportunity for a digital twin system

Technology for the Future: In-Space Technology Experiments Program, part 1

The purpose of the Office of Aeronautics and Space Technology (OAST) In-Space Technology Experiment Program (In-STEP) 1988 Workshop was to identify and prioritize technologies that are critical for future national space programs and require validation in the space environment, and review current NASA (In-Reach) and industry/university (Out-Reach) experiments. A prioritized list of the critical technology needs was developed for the following eight disciplines: structures; environmental effects; power systems and thermal management; fluid management and propulsion systems; automation and robotics; sensors and information systems; in-space systems; and humans in space. This is part one of two parts and is the executive summary and experiment description. The executive summary portion contains keynote addresses, strategic planning information, and the critical technology needs summaries for each theme. The experiment description portion contains brief overviews of the objectives, technology needs and backgrounds, descriptions, and development schedules for current industry, university, and NASA space flight technology experiments

Vision-based Monitoring System for High Quality TIG Welding

The current study evaluates an automatic system for real-time arc welding quality assessment and defect detection. The system research focuses on the identification of defects that may arise during the welding process by analysing the occurrence of any changes in the visible spectrum of the weld pool and the surrounding area. Currently, the state-of-the-art is very simplistic, involving an operator observing the process continuously. The operator assessment is subjective, and the criteria of acceptance based solely on operator observations can change over time due to the fatigue leading to incorrect classification. Variations in the weld pool are the initial result of the chosen welding parameters and torch position and at the same time the very first indication of the resulting weld quality. The system investigated in this research study consists of a camera used to record the welding process and a processing unit which analyse the frames giving an indication of the quality expected. The categorisation is achieved by employing artificial neural networks and correlating the weld pool appearance with the resulting quality. Six categories denote the resulting quality of a weld for stainless steel and aluminium. The models use images to learn the correlation between the aspect of the weld pool and the surrounding area and the state of the weld as denoted by the six categories, similar to a welder categorisation. Therefore the models learn the probability distribution of images’ aspect over the categories considered