Prediction of Weld Bead Geometry and Its Optimization During TIG Welding Process

Abstract

TIG welding is used for nonferrous material like aluminium, copper or thin plate of stainless steel. This paper presents measurement of weld bead geometry of aluminium sheet, which is output of TIG welding. Optimization of welding parameters is evaluated by genetic algorithm. ANN is used for prediction of output data i.e. front height, front width, back height and back width of TIG welding. Welding parameters for experiments are welding current, travel speed and electrode feed consumption rate. This paper presents a neural network and genetic algorithm for optimization and prediction on experimental data available from manual TIG welding experimentation. Quality of weld is affected by welding parameters. In the current investigation, physical experiments were conducted to optimize several input process parameters (welding current, welding speed and feed consumption rate) to get optimum parameters in aluminium plates using TIG welding. By using ANN models the welding output parameters predicted. Using Genetic Algorithm (GA) the weld bead geometry was optimized to get optimum weld bead geometry (front height, front width, back height and back width).