Robert Joel Barnett

Weld modeling and control using artificial neural networks

Artificial neural networks were evaluated for monitoring and control of the variable polarity plasma arc welding (VPPAW) process. Three areas of welding application were investigated: weld process modeling, weld process control, and weld bead profile analysis for quality control. Experiments and analysis confirm that artificial neural networks are powerful tools for analysis, modeling, and control applications. They are particularly attractive in view of their capabilities to process nonlinear and noisy data, learn from actual welding data, and execute at relatively high speed. It is shown that neural networks are capable of modeling parameters of the VPPAW process to on the order of 10% accuracy or better. The same was observed when neural networks were used to select welding equipment parameters and the resulting bead geometries were estimated. These performance figures suggest that a VPPA welding control system can be implemented based on neural network models and control mechanisms. >

Synchronous weld pool oscillation for monitoring and control

A novel approach for inducing and monitoring oscillations in a molten weld pool is presented. Research efforts have illustrated that the weld pool resonates at natural frequencies that are related to its dimensions and state of penetration. This phenomenon may be used to monitor the weld pool, and particularly its depth of penetration, in a closed-loop feedback control system. The approach used to induce pool oscillations was to excite the weld pool with current pulses synchronized to the natural oscillations of the pool. Implementation of this synchronous weld pool pulsing technique was based on the use of a phase locked loop (PLL) system. The natural weld pool oscillations are used as the reference frequency source and a pulsing circuit is controlled by the PLL oscillator so that the arc current pulses repeatedly impact the pool after a fixed number of reference oscillation periods. An optical sensor detects the pool oscillations which are amplified, filtered, and limited to eliminate amplitude variations from the optical signal. A model of the weld pool is developed which uses a fluid droplet formulation for the relation of weld pool geometry and other physical parameters to the natural frequencies of the weld pool. Comparison of the weld pools actual resonant frequency with the expected resonant frequency as predicted by weld pool geometry models and measurements of the pool width (or area) allows and assessment of the state of penetration of the weld pool into the workpiece.<<ETX>>

Statistical process control application to weld process

A statistical weld process monitoring system is described. Using data collected while welding, the welding statistical process control (SPC) tool provides weld process quality control by implementing techniques of data trending analysis, tolerance analysis, and sequential analysis. The SPC system computes the mean, standard deviation, and range of each of the parameters sampled by the data collection system. Changes in the mean, standard deviation, and range are displayed using control (or trend) charts. The control chart displays a function of a parameter with respect to the ordering of the weld records (for a single weld) or weld number (for multiple welds). The SPC tool also permits plotting tolerance charts of the mean, standard deviation, and range for each of the sampled parameters. The tolerance chart is plotted versus the record number (or weld number) and consists of a vertical line for each record (or weld number) showing the minimum and maximum value of that parameter for that record (or weld number). The upper control limit (UCL), lower control limit (LCL), and nominal value may also be displayed on the tolerance chart printout. The SPC also performs sequential analysis, which allows the user to examine the process as it goes along, which in turn may permit the user to locate a possible change in the process before it goes out of control. Work directed toward developing an expert interpreter of the voluminous statistical output generated by the SPC is also described.

Automated visual inspection and interpretation system for weld quality evaluation

The visual inspection of weld beads and subsequent evaluation of the weld quality is an integral part of the commercial welding environment. However, this inspection process tends to be both time and labor intensive. An automated system for the performance of this task has been developed. The sequence of events in the operation of the vision-based system are: (1) image capture; (2) image enhancement; (3) image processing; and (4) image evaluation. To minimize cost and complexity, the system uses conventional video camera and related hardware and software for the image-capture and image enhancement portion of the evaluation process. Various weld processes were observed to have certain characteristic features which were most relevant for the inspection and evaluation of the particular process. Image processing codes were written to extract those features of the weld beads and store this information in data files for subsequent assessment. Numerical algorithms were written, tailored to each of the weld processes, to perform the image evaluation portion of the quality evaluation process. This result is expressed as a relative quality rating which was found to correlate well with a quality rating derived by direct observation by a human inspector of various quality welds. The vision-based weld quality evaluation system has potential for use as a post-weld quality evaluation system, or, due to the high update rate of the overall vision system (>10 Hz), as part of a real-time control system.

PC-based arc ignition and arc length control system for gas tungsten arc welding

A personal computer-based digital control system for gas tungsten arc welding (GTAW), which controls the ignition process, the arc length, and the process of welding termination, is presented. The arc ignition control, arc voltage control, arc length control, and arc termination control using the digital controller, the arc characteristics, and the welding process monitoring are described.<<ETX>>

Statistical weld process monitoring and interpretation

A statistical process control tool is described that provides weld quality control and documentation by implementing techniques of data trending analysis, tolerance analysis, and sequential analysis. The SPC tool has been used in combination with an arc data acquisition and monitoring system for industrial weld quality assurance. Rules have also been developed for providing equipment/materials diagnostic assistance based on observations of the SPC control chart trends.<<ETX>>

Intelligent fusion control throughout varying thermal regions [arc welding]

An intelligent monitoring and control system is presented that regulates the total heat input in order to maintain constant fusion zone geometry under varying thermal regions. The integrity and quality of the final fused joint rely heavily on the geometrical properties of the fusion zone, in particular the width-to-depth ratio. Assessment of the joint geometry is performed using Rayleighs suspended droplet analogy in which the mass, and hence the volume, of the droplet is related to the natural resonant frequency of the molten region. A nonintrusive, noncontact, top-side sensor collects the arc light reflected from the oscillations of the molten metals surface. An improved approach for inducing and monitoring the oscillations of a molten weld pool is presented. A software-based phase locked loop (PLL) technique enables synchronized excitation of the molten pool. Improved locking and tracking characteristics in the presence of noise, signal distortions and harmonic conditions are achieved through the use of intelligent signal monitoring algorithms which co-exist in parallel and cooperate with the PLL. Dynamic reconfiguration of the PLLs digital filter coefficients allows superior tracking performance over a wide range of resonant frequency conditions. A fuzzy logic rule set, modeled after expert human welding knowledge, regulates the total heat input to the fusion process in order to demonstrate this unique synchronous excitation, monitoring and control technique. A detailed discussion regarding each components contribution to the overall system is provided. Finally, an in-depth comparison between various open and closed loop experiments of the joining process is discussed.