Prediction of mechanical properties of welding joints by hybrid cluster fuzzy RBF neural network

The build of hybrid clustering algorithmcombined with the pseudo-inverse method was carried out to establish the RBF neural network model to predict the mechanical properties of welded joints. Taking TC4 titanium alloy TIG welding experiments as basis, the welding parameters was set as model input and the mechanical properties of welded joints was set as output. Through simulation, the mean relative error of the predictions ranged from 1.74 to 6.69%, indicating that the model has higher prediction accuracy, adaptability and better generalization ability to predict the mechanical properties of welded joints. The model decomposed by using the mathematical analysis method, can obtain a functional expression between the welding parameters and mechanical properties of the joint process. The welding parameters can also be optimized simultaneously. The utilization of welding professional knowledge was applied to adjust the RBF unit parameter of model, allowing an increase of the prediction accuracy of the model. It has opened a new way to take the welding expert knowledge into the RBF neural network model.