GRNN-Immune Based Strategy for Estimating and Optimizing the Vibratory Assisted Welding Parameters to Produce Quality Welded Joints
Welding is the process of producing permanent joints with the application of pressure and/or heat energy. During welding operation, weldments may be subjected to uneven thermal stresses. These stresses influence the metallurgical structure of the component. Due to this, the strength of the weld joint is reduced. Therefore, vibratory weld treatment during welding has been proposed in the present work to enhance the flexural and impact strength of weldments. However, it is found that the mechanical properties have shown nonlinear behavior with the chosen input parameters. Hence, an efficient Neural Network (NN) based prediction tool is developed to approximate the mechanical properties of weldments without performing the experiments, output values can be predicted for the given input values. Further, an immune based strategy is integrated to the developed prediction tool in order to obtain desired quality welded joints.