Intelligent Modeling for Optimization of A-TIG Welding Process

Abstract

An intelligent model combining artificial neural network (ANN) and genetic algorithm (GA) has been developed for determining the optimum process parameters for achieving the desired depth of penetration and weld bead width during Activated Flux Tungsten Inert Gas Welding (A-TIG) welding of type 316LN and 304LN stainless steels. First, ANN models correlating process parameters with depth of penetration and weld bead width have been developed. There was good correlation between the measured and models predicted depth of penetration as well as the weld bead width for both training and test data. A GA code was developed in MATLAB in which the objective function was evaluated using the ANN models. The optimized values for GA parameters such as crossover rate, population size, and mutation probability were identified. The developed GA model produced multiple outputs such as current, torch speed, voltage, and arc gap for the same target depth of penetration and bead width, and validation was carried out by experiments. There was good agreement between the target values and the actual values of depth of penetration and weld bead width obtained for both the stainless steels.

Keywords:

• Artificial neural network
• A-TIG welding
• Genetic algorithm
• Intelligent modeling
• Optimum process parameters

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