George E. Cook

Artificial neural networks applied to arc welding process modeling and control

The authors explain some basic concepts relating to neural networks and discuss how they can be used to model weld bead geometry in terms of the parameters of the equipment selected to produce the weld. Approaches to utilization of neural networks in process control are discussed as well. The need for modeling transient as well as static characteristics of physical systems for closed-loop control is pointed out, and an approach for achieving this is presented. The performance of neural networks for modeling is presented and evaluated using actual welding data. It is concluded that the accuracy of neural network modeling is fully comparable to the accuracy achieved by more traditional modeling schemes.<<ETX>>

Robotic Arc Welding: Research in Sensory Feedback Control

Robotic arc welding and its dependence on sensory feed-back control for successful application is discussed. Problems unique to arc weld sensing are identified and sensor requirements are categorized as a function of welding design requirements, joint imperfections, weld shape deviations, and process characteristics. The two most prevalent approaches of weld sensing, i.e., optical and through-the arc sensing, are covered.

Robotic friction stir welding

The forces and torques associated with friction stir welding (FSW) are discussed as they relate to implementation of the welding process with industrial robots. Experimental results are presented that support the conclusions drawn from models developed by others. It is shown that even with heavy‐duty industrial robots with high stiffness, force feedback is important for successful robotic FSW. Methods of implementing force feedback are reviewed. Attention is paid to stability issues that arise with variations in tool rotation and travel speed. Successful implementations of robotic FSW are cited.

Experimental defect analysis and force prediction simulation of high weld pitch friction stir welding

Abstract Experimental data for AA 6061-T6 friction stir welded at rotational and travel speeds ranging from 1000 to 5000 rev min−1 and from 290 to 1600 mm min−1 (11–63 ipm) are presented. The present paper examines the forces and torques during friction stir welding (FSW) with respect to mechanistic defect development owing to process parameter variation. Two types of defects are observed: wormholes and weld deformation in the form of significant excess flash material. A 3D numerical model, implemented using the computational fluids dynamics package Fluent, is used to simulate and investigate the parametric relationship of the forces and torques during FSW. In order to establish a mechanistic quantification of the FSW process, two mechanical models, the Couette and the viscoplastic fluid flow models, were simulated and compared with experimental data for AA 6061-T6.

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.

Heated Friction Stir Welding: An Experimental and Theoretical Investigation into How Preheating Influences Process Forces

As friction stir welding (FSW) has expanded to welding higher strength materials, large process forces and extreme tool wear have become issues. One possible solution is introducing an additional heating source in front of the FSW tool which softens the material and reduces the tool loads. We investigate the advantages of elevating temperature. Bead on plate welds were performed with a Trivex tool in aluminum alloy (AA 6061) heated to initial material temperatures up to 300°C. Macrograph cross-sections of the welds revealed a slight increase in material flow with increasing temperatures. More significant, the welding forces were analyzed to reveal up to a 43% reduction in the axial force with even moderate heating. An intriguing trend is observed that the process forces do not decrease steadily with increasing initial temperature, as might be expected, but exhibit a more complex polynomial shape, which actually increases for some heating intervals.

In‐process gap detection in friction stir welding

Purpose – This paper aims to investigate methods of implementing in‐process fault avoidance in robotic friction stir welding (FSW).Design/methodology/approach – Investigations into the possibilities for automatically detecting gap‐faults in a friction stir lap weld were conducted. Force signals were collected from a number of lap welds containing differing degrees of gap faults. Statistical analysis was carried out to determine whether these signals could be used to develop an automatic fault detector/classifier.Findings – The results demonstrate that the frequency spectra of collected force signals can be mapped to a lower dimension through discovered discriminant functions where the faulty welds and control welds are linearly separable. This implies that a robust and precise classifier is very plausible, given force signals.Research limitations/implications – Future research should focus on a complete controller using the information reported in this paper. This should allow for a robotic friction stir ...

A real-time prediction model of electrode extension for GMAW

This paper presents the development of an electrode extension model for the gas metal arc welding process based on the process voltage. The full dynamic model for the electrode extension is derived by combining a dynamic resistivity model with the voltage model. The electrode extension model was found to be represented mathematically by a nonlinear, time-varying, second-order ordinary differential equation. This model can be used in through-the-arc sensing and arc length control systems. To experimentally verify the model, the process dynamics were excited by a continuous sinusoidal variation of arc current. Using a constant current power source with the electrode positive, sinusoidal perturbations of variable amplitude were superimposed on the current to allow direct measurement of changes in electrode extension, arc length, and total voltage. A high-speed video system was used to capture the experimental electrode extension dynamics. The model was verified by comparing the frequency response of the model to the frequency response of the real process. Agreement between the simulations and the experimental results was found to be very good. The accuracy of this model was found to be approximately /spl plusmn/0.6 mm, which is considered to be suitable for process control applications.

Effect of Pin Length and Rotation Rate on the Tensile Strength of a Friction Stir Spot-Welded Al Alloy: A Contribution to Automated Production

Friction stir spot welding (FSSW) is performed on thin plates of an aluminum alloy in a lap joint configuration with tools of different pin lengths and various rotation rates. The effects these process parameters have on the joint properties of the welds are investigated. The tensile strength of the welds decreased when the rotation rate was increased. The tensile strength of welds made with a pinless tool is on average 90% the strength of the full penetration spot welds. Intermediate pin lengths were tested between these two extremes. It was found that the tensile strength decreases as the pin length increases from pinless to 10% bottom plate penetration. Three distinct failure modes were identified when the welds were placed under tensile loading: shear mode, mixed mode, and nugget-pullout mode. The dependence of static joint strength on these process parameters is discussed.