It is a key issue to establish an appropriate model of the heat source in the simulation of the keyhole plasma arc welding (PAW). It requires that the model account for the keyhole effect and have the characteristic of volumetric distribution along the direction of the plate thickness. For available heat source models, neither Gaussian nor double ellipsoidal modes of heat source is applicable to keyhole PAW process. With considering the force of the high speed plasma jet and the associated strong momentum, a modified three-dimensional conical heat source model is proposed as the basis for the numerical analysis of temperature fields in keyhole PAW process. Further, a new heat source model for quasi-steady state temperature field in keyhole PAW is developed to consider the “bugle-like” configuration of keyhole and the decay of heat intensity distribution of the plasma arc along the direction of the workpiece thickness. Based on this heat source model, finite-element analysis of temperature profile in keyhole PAW is conducted and the weld geometry is determined. The results show that the predicted location and locus of the melt-line in the PAW weld cross section are in good agreement with experimental measurements.

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.

This paper introduces an intelligent system for monitoring and recognition of process disturbances during short-short-Cirutiong gas-metal arc welding. It is based on the measured and statisticlly processed dataof welding electrical parameters. A 12-dimensional array of process features is designed to describevarious welding conditions and is employed as input vector of the intelligent system. Three methods, Such as fuzzy c-means, neural netowrk and fuzzy Kohonen clustering network areused to conduct process monitoring and automatic recognition. The correct recoginition rates of these three methods are compared.

This paper is concerned with numerical studies of the anode region of a high-intensity argon arc. The anode region is divided into three subzones: the anode boundary layer, the presheath and the sheath. The governing equations of the dominating processes with the boundary conditions taken from the solutions of LTE plasmas are solved by applying the Runge

It is of great significance to determine the sheath potential of gas tungsten arcs because it has a marked e ect on the heat flux at the anode. In this paper one of the parameters a, i.e., the percentage of the electrons entering the lattice of anode, is introduced for determining the sheath potential. The results indicate that the sheath potential is strongly dependent on the parameter a which is used to make a di erence between an anode conducting electricity and a cool wall isolated. For determining the sheath potential, a formula is established, which takes account of the electron temperature, the electron flux and the ion flux at the free-fall edge as well as the parameter α.