Prediction of weld quality using intelligent decision making tools

Decision-making process in manufacturing environment is increasingly difficult due to the rapid changes in design and demand of quality products. To make decision making process online, effective and efficient artificial intelligent tools like neural networks are being attempted. This paper proposes the development of neural network models for prediction of weld quality in Submerged Arc Welding (SAW). Experiments are designed according to Taguchi’s principles and mathematical equations are developed using multiple regression model. Proposed neural network models are developed using experimental data, supported with the data generated by regression model. The performances of the developed models are compared in terms of computational speed and prediction accuracy. It is found that Neural Network trained with Particle Swarm Optimization (NNPSO) performs better than Neural Network trained with Back Propagation (BPNN) algorithm, Radial Basis Functional Neural Network (RBFNN) and Neural Network trained with Genetic Algorithm (NNGA). The developed scheme for weld quality prediction is flexible, competent, and accurate than existing models and it scopes better online monitoring system. Finally the developed models are validated. The proposed and developed technique finds a good scope and a better future in the relevant field where human can avoid unwanted risks during operations with the deployment of robots.