This paper introduces a fuzzy logic system that is able to recognize common disturbances during automatic gas metal arc welding (GMAW) using measured welding voltage and current signals. A statistical method was employed to process the captured transient raw data, and the probability density distributions (PDDs) and the class frequency distributions (CFDs) were obtained. Based on the processed data (PDD values of welding voltage and current and CFD values of the short-circuiting time), the system automatically generates fuzzy rules and membership functions of linguistic variables, conducts inference and defuzzification, and completes the evaluation process without further expert knowledge.

Achieving an effective utilization and exploitation of TIG welding arcs require a thorough understanding of the plasma properties and its physical processes. Through simultaneous solutions of the set of conservation equations for mass, momentum. Energy and current. a mathematical model has been developed to predict the velocity, temperature, and current density distributions in argon welding arcs. The predicted temperature fields in arc regions, and the distribution of current density and heat flux at the anode agree well with measurements reported in literatures. This work could lay the foundation for developing a comprehensive model of the TIG welding process wheve a dynamic, two-way coupling between the welding arc and the weld pool surface is properly represented.

A real-time monitoring system is developed for detecting abnormal conditions in robotic gas metal arc welding. The butt-joint test pieces with cut gap are used to intentionally introduce the step disturbance of welding conditions. During the welding process, the welding voltage and current signals are sampled and processed on-line to extract the characteristic information reflecting the process quality. After 1st statistical processing, it is found that seven statistical parameters (the mean, standard deviation, variance, and kurtosis of welding voltage; the mean, variance, and kurtosis of welding current) show variations during the step disturbance. Through 2nd statistical processing of the means of the welding voltage for subgroups of continuous measurement, the statistical control chart is obtained, and SPC (statistical process control) -based on-line identifying method is developed. Ten robotic welding experiments are conducted to verify the real-time monitoring system. It is found that the correct identification rate for normal and abnormal welding conditions are 100% and 95%, respectively.

The gas tungsten arc welding process is an uncertain nonlinear multivariable system. In order to control the welding process, the nonlinear dynamic relationship between the weld pool geometry reflecting the weld quality and the welding parameters must be developed. A three-dimensional numerical model is developed to investigate the dynamic characteristics of the weld pool geometry when the welding current and welding speed undergo a step-change. Under the welding conditions employed in this research, the transformation periods are about 4s for a 20A down step-change of welding current, and about 2s for a 20mmmin−1 up step-change of welding speed, respectively. At the initial stage during the step-change of welding current and welding speed, the responses of weld pool geometry are quicker, but they slow down subsequently until the weld pool reaches a newquasi-steady state. Welding experiments were conducted to verify the simulation results. It was found that the predicted weld pool geometries agree with the measured ones.