Many studies have been conducted into the control of weld joint penetration utilizing a weld-face sensor. In this paper, a weld-face vision-based strategy is presented. The strategy employs a structured-light three-dimensional vision sensor to measure the weld-face geometric parameters of the cross-section of full joint penetration welds in real-time. The principle behind this strategy depends on the relationship between the root surface weld width and the measured weld-face parameters

Determining joint penetration in GTAW with vision sensing of weld face geometry

This chapter analyzes the automatic control techniques that have been used in the Gas Metal Arc Welding (GMAW) process. It provides a brief discussion of manual control and many automatic control techniques. Automatic welding simply means that some aspects of the welding operation are performed without the intervention of human such as welder or welding operator. Closed-loop control strategies have several forms such as optimal control, adaptive control, robust control, and learning or intelligent control. Statistical quality control (SQC) is the application of statistical methods for the purpose of determining if a given component of production (input) is within acceptable statistical limits and if there is some result of production (output) that can be shown to be statistically acceptable to required specifications. On the other hand, statistical process control (SPC) is the application of statistical methods for the purpose of determining if a given process is within the operating control parameters established by statistical procedures. A number of different hazards present in electric arc welding and their effective control are achieved by conventional environmental engineering solutions.

Chapter 4 – Gas Metal Arc Welding: Automatic Control

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Dual Bypass Gas Metal Arc Welding Process and Control

Nonlinear modelling and control of full-penetration dual bypass gas metal arc welding of aluminium

Based on system identification of pulsed MIG welding process for aluminum alloy, due to strong coupling among the welding parameters, PID controller, fuzzy PID controller and neural network inverse controller were designed, the strong coupling multi-input and multi-output (MIMO) system was simulated using pulse current duty cycle and wire feed speed as control input, and wire extension and pool width as control output. The results show that the neural network inverse control provides faster response and better robustness, which lays a foundation for decoupling control of aluminum pulsed MIG welding.

Simulation of Decoupling Control of Pulsed MIG Welding for Aluminum Alloy

The control of a nonminimum-phase plant with a large and variable order, large and variable delays, and variable model parameters is addressed. The model concern is the full penetration process. The generalized predictive algorithm presented by D.W. Clarke et al. (1987) is selected as the principal control strategy, and an adaptive generalized predictive decoupling control scheme is constructed. Simulations are performed to determine the default parameters of the algorithm. The effectiveness and the robustness to various disturbances (which result in variation in order, delays and parameters) of the designed control scheme have been confirmed by both the simulations and the experiments. >

B.L. Walcott

Papers

Adaptive predictive decoupling control of full penetration process in GTAW