Genetic algorithm for optimization of welding variables for height to width ratio and application of ANN for prediction of bead geometry for TIG welding process
This paper explains an integrated method with a new approach using experimental design matrix of experimental designs technique on the experimental data available from conventional experimentation, application of neural network for predicting the weld bead geometric descriptors and use of genetic algorithm for optimization of process parameters. The properties of the welded joints are affected by a large number of welding parameters. Modeling of weld bead shape is important for predicting the quality of welds. In an attempt to model the welding process for predicting the bead shape parameters (also known as bead geometry parameters) of welded joints, modeling and optimization of bead shape parameters in tungsten inert gas (TIG) welding process has been tried in the present work. Multiple linear regression technique has been used to develop mathematical models for weld bead shape parameters of TIG welding process, considering the effects of main variables as well as two factor interactions. Also by using the same experimental data, an attempt has been made to predict the bead shape parameters using back-propagation neural network. To optimize the process parameters for the desired front height to front width ratio and back height to back width ratio, genetic algorithmic approach has been applied.
genetic algorithm ann bead geometry tig welding