Neuro hybrid model to predict weld bead width in submerged arcwelding process

350-355This paper presents development of neuro hybrid model (NHM) to predict weld bead width in submerged arc welding.Experiments were designed using Taguchi’s principles and results were used to develop a multiple regression model. Data setgenerated from Multiple Regression Analysis (MRA) was utilized in ANN model, which was trained with backpropagation algorithm in MATLAB platform and used to develop NHM to predict quality of weld. NHM is flexible and accurate than existing models for a better online monitoring system

Weld residual stress prediction using artificial neural network and Fuzzy logic modeling

351-360intelligent tools such as expert systems, artificial neural network and fuzzy logic support decision-making are being used in intelligent manufacturing systems. Success of intelligent manufacturing systems depends on effective and efficient utilization of intelligent tools. Weld residual stress depends on different process parameters and its prediction and control is a challenge to the researchers. In this paper, intelligent predictive techniques artificial neural network (ANN) and fuzzy logic models are developed for weld residual stress prediction. The models are developed using Matlab toolbox functions. Data set required to train the models are obtained through finite element simulation. Results from the fuzzy model are compared with the developed <span style="mso-bidi-font-weight: bold">artificial neural network model, and these models are also validated. </span