Spatter rate estimation of GMAW-S based on partial least square regression

Abstract

This paper analyzes the drop transfer process in gas metal arc welding in short-circuit transfer mode (GMAW-S) in order to develop an optimized spatter rate model that can be used on line. According to thermodynamic characters and practical behavior, a complete arcing process is divided into three sub-processes: arc re-ignition, energy output and shorting preparation. Shorting process is then divided as drop spread, bridge sustention and bridge destabilization. Nine process variables and their distribution are analyzed based on welding experiments with high-speed photos and synchronous current and voltage signals. Method of variation coefficient is used to reflect process consistency and to design characteristic parameters. Partial least square regression (PLSR) is utilized to set up spatter rate model because of severe correlativity among the above characteristic parameters. PLSR is a new multivariate statistical analysis method, in which regression modeling, data simplification and relativity analysis are included in a single algorithm. Experiment results show that the regression equation based on PLSR is effective for on-line predicting spatter rate of its corresponding welding condition.

Numerical Simulation of Electron Beam Welding Process of Inconel 706 Sheets

“…Impact of 6 input parameters: wire feed rate, wire extension, welding speed, gas flow, welding voltage, and welding current on weld pool width, weld pool depth, and weld reinforcement was analyzed. Cai et al [10] used PLS for on-line prediction of spatter rate of gas metal arc welding in short-circuit transfer mode. The error between estimation and measured value is less than 11%.…”