Model-Based Predictive Control of Weld Penetration in Gas Tungsten Arc Welding

Abstract

Skilled welders can estimate and control the weld joint penetration, which is primarily measured by the backside bead width, based on weld pool observation. This suggests that an advanced control system could be developed to control the weld joint penetration by emulating the estimation and decisionmaking process of the human welder. In this paper an innovative 3-D vision sensing system is used to measure the characteristic parameters of the weld pool in real-time in gas tungsten arc welding. The measured characteristic parameters are used to estimate the backside bead width, using an adaptive neuro-fuzzy inference system (ANFIS) as an emulation of skilled welder. Dynamic experiments are conducted to establish the model that relates the backside bead width to the welding current and speed. The dynamic linear model is first constructed and the modeling result is analyzed. The linear model is then improved by incorporating a nonlinear operating point modeled by an ANFIS. Because the weld pool needs to gradually change, being controlled by a skilled welder, a model predictive control is used to follow a trajectory to reach the desired backside bead width and the control increment is penalized. Because the weld pool is not supposed to change in an extremely large range, the resultant model predictive control is actually linear and an analytical solution is derived. Welding experiments confirm that the developed control system is effective in achieving the desired weld joint penetration under various disturbances and initial conditions.