REAL-TIME MODEL PREDICTIVE CONTROL OF QUASI-KEYHOLE PIPE WELDING

Quasi-keyhole, including plasma keyhole and double-sided welding, is a novel approach proposed to operate the keyhole arc welding process. It can result in a high quality weld, but also raise higher demand of the operator. A computer control system to detect the keyhole and control the arc current can improve the performance of the welding process. To this effect, developing automatic pipe welding, instead of manual welding, is a hot research topic in the welding field. The objective of this research is to design an automatic quasi-keyhole pipe welding system that can monitor the keyhole and control its establishment time to track the reference trajectory as the dynamic behavior of welding processes changes. For this reason, an automatic plasma welding system is proposed, in which an additional electrode is added on the back side of the workpiece to detect the keyhole, as well as to provide the double-side arc in the double-sided arc welding mode. In the automatic pipe welding system the arc current can be controlled by the computer controller. Based on the designed automatic plasma pipe welding system, two kinds of model predictive controller − linear and bilinear − are developed, and an optimal algorithm is designed to optimize the keyhole weld process. The result of the proposed approach has been verified by using both linear and bilinear model structures in the quasi-keyhole plasma welding (QKPW) process experiments, both in normal plasma keyhole and double-sided arc welding modes

Virtualized Welding Based Learning of Human Welder Behaviors for Intelligent Robotic Welding

Combining human welder (with intelligence and sensing versatility) and automated welding robots (with precision and consistency) can lead to next generation intelligent welding systems. In this dissertation intelligent welding robots are developed by process modeling / control method and learning the human welder behavior. Weld penetration and 3D weld pool surface are first accurately controlled for an automated Gas Tungsten Arc Welding (GTAW) machine. Closed-form model predictive control (MPC) algorithm is derived for real-time welding applications. Skilled welder response to 3D weld pool surface by adjusting the welding current is then modeled using Adaptive Neuro-Fuzzy Inference System (ANFIS), and compared to the novice welder. Automated welding experiments confirm the effectiveness of the proposed human response model. A virtualized welding system is then developed that enables transferring the human knowledge into a welding robot. The learning of human welder movement (i.e., welding speed) is first realized with Virtual Reality (VR) enhancement using iterative K-means based local ANFIS modeling. As a separate effort, the learning is performed without VR enhancement utilizing a fuzzy classifier to rank the data and only preserve the high ranking “correct” response. The trained supervised ANFIS model is transferred to the welding robot and the performance of the controller is examined. A fuzzy weighting based data fusion approach to combine multiple machine and human intelligent models is proposed. The data fusion model can outperform individual machine-based control algorithm and welder intelligence-based models (with and without VR enhancement). Finally a data-driven approach is proposed to model human welder adjustments in 3D (including welding speed, arc length, and torch orientations). Teleoperated training experiments are conducted in which a human welder tries to adjust the torch movements in 3D based on his observation on the real-time weld pool image feedback. The data is off-line rated by the welder and a welder rating system is synthesized. ANFIS model is then proposed to correlate the 3D weld pool characteristic parameters and welder’s torch movements. A foundation is thus established to rapidly extract human intelligence and transfer such intelligence into welding robots

Applications of aerospace technology in the electric power industry

An overview of the electric power industry, selected NASA contributions to progress in the industry, linkages affecting the transfer and diffusion of technology, and, finally, a perspective on technology transfer issues are presented

MODEL ANALYSIS AND PREDICTIVE CONTROL OF DOUBLE ELECTRODE SUBMERGED ARC WELDING PROCESS FOR FILLET JOINTS WITH ROOT OPENING

Submerged Arc Welding (SAW) for fillet joints is one of the major applications in the shipbuilding industry. Due to the requirement for the weld size, a sufficient amount of metal must be deposited. In conventional SAW process, the heat input is proportional to the amount of metal melted and is thus determined by the required weld size. To meet this requirement, an excessive amount of heat is applied causing large distortions on the welded structures whose follow-up straightening is highly costly. In order to reduce the needed heat input, Double-Electrode (DE) technology has been practiced creating the Double-Electrode SAW (DE-SAW) method for fillet joints. The reduction in the heat input, however, also reduces the penetration capability of the process, and the ability to produce required weld beads has to be compromised. To eliminate the unwanted side effect after using DE-SAW, a root opening between the panel and the tee has been proposed in this dissertation to form a modified fillet joint design. Experimental results verified that the use of root opening improves the ability of DE-SAW to produce the required weld beads at reduced heat input and penetration capability. Unfortunately, the use of root opening decreases the stability of the process significantly. To control the heat input at a minimally necessary level that guarantees the weld size and meanwhile the process stability, a feedback is needed to control the currents at their desired levels. To this end, the fillet DE-SAW process is modeled and a multivariable predictive control algorithm is developed based on the process model. Major parameters including the root opening size, travel speed and heat input level have been selected/optimized/minimized to produce required fillet weld beads with a minimized heat input based on qualitative and quantitative analyses. At the end of this dissertation, a series of experiments validated the feasibility and repeatability of the predictive control based DE-SAW process for fillet joints with root opening

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

Data Analysis and Modeling Techniques of Welding Processes: The State-of-the-Art

Information contributes to the improvement of decision-making, process improvement, error detection, and prevention. The new requirements of the coming Industry 4.0 will make these new information technologies help in the improvement and decision-making of industrial processes. In case of the welding processes, several techniques have been used. Welding processes can be analyzed as a stochastic system with several inputs and outputs. This allows a study with a data analysis perspective. Data mining processes, machine learning, deep learning, and reinforcement learning techniques have had good results in the analysis and control of systems as complex as the welding process. The increase of information acquisition and information quality by sensors developed at present, allows a large volume of data that benefits the analysis of these techniques. This research aims to make a bibliographic analysis of the techniques used in the welding area, the advantages that these new techniques can provide, and how some researchers are already using them. The chapter is organized according to some stages of the data mining process. This was defined with the objective of highlighting evolution and potential for each stage for welding processes

Optimization of different welding processes using statistical and numerical approaches – A reference guide

Welding input parameters play a very significant role in determining the quality of a weld joint. The joint quality can be defined in terms of properties such as weld-bead geometry, mechanical properties, and distortion. Generally, all welding processes are used with the aim of obtaining a welded joint with the desired weld-bead parameters, excellent mechanical properties with minimum distortion. Nowadays, application of design of experiment (DoE), evolutionary algorithms and computational network are widely used to develop a mathematical relationship between the welding process input parameters and the output variables of the weld joint in order to determine the welding input parameters that lead to the desired weld quality. A comprehensive literature review of the application of these methods in the area of welding has been introduced herein. This review was classified according to the output features of the weld, i.e. bead geometry and mechanical properties of the welds

NASA SBIR abstracts of 1990 phase 1 projects

The research objectives of the 280 projects placed under contract in the National Aeronautics and Space Administration (NASA) 1990 Small Business Innovation Research (SBIR) Phase 1 program are described. The basic document consists of edited, non-proprietary abstracts of the winning proposals submitted by small businesses in response to NASA's 1990 SBIR Phase 1 Program Solicitation. The abstracts are presented under the 15 technical topics within which Phase 1 proposals were solicited. Each project was assigned a sequential identifying number from 001 to 280, in order of its appearance in the body of the report. The document also includes Appendixes to provide additional information about the SBIR program and permit cross-reference in the 1990 Phase 1 projects by company name, location by state, principal investigator, NASA field center responsible for management of each project, and NASA contract number