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Prediction of Temperature Evolution During Self-Pierced Riveting of Sheets
Abstract
Weight reduction of automotive components by tailoring materials is the state of the art. This basically has resulted in the development of advanced joining methods like clinching, friction stir welding, self-pierced riveting etc. to assemble similar or dissimilar materials, with significant change in sheet properties. In the present work, the main aim is to predict the temperature evolution during Self-Pierced Riveting (SPR) of sheets by Finite Element (FE) analyses. Load evolution is also predicted. Generally temperature estimation during SPR is not attempted. The influence of a few selected SPR parameters has been studied on the temperature and load evolution through FE simulations. The relationship between these parameters and the temperature and the load evolution are revealed. Later a neural network model is developed to predict the temperature rise during SPR. The same has been validated at 20 intermediate levels and the predictions are accurate. Thus a hybrid FEM-ANN model for SPR has been developed to predict the SPR outputs efficiently.
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