Publication

Abstract : This paper presents an integrated method with a new approach using experimental design matrix of experimental design techniques on experimental data available from conventional experimentation, application of neural network for predicting weld bead geometry and use of simulated annealing algorithm for optimizing percentage of dilution. Quality of weld is affected by large number of welding parameters .Modelling of weld bead geometry is important for predicting quality of weld. In this study an experimental work is conducted to optimize various input process parameters (welding current, welding speed, gun angle, contact tip to work distance and pinch) to get optimum dilution in stainless steel cladding of low carbon structural steel plates using Gas Metal Arc Welding (GMAW). Experiments were conducted based on central composite rotatable design with full replication technique and mathematical models were developed using multiple regression method. The developed models have been checked for adequacy and significance. By using ANN models the welding output parameters predicted. Using Simulated annealing Algorithm (SA) the process parameters were optimized to get optimum dilution.