Data-Based and Analytical Models for Strength Prediction of Mechanical Joints

Abstract

Currently the design of mechanical joining processes like self-pierce riveting with semi-tubular rivet (SPR-ST) and clinching for production is subject to complex and experimental test series in which process parameters such as the rivet or die geometry are varied iteratively and based on experience until a suitable joint contour and strength is achieved. To simplify the use of mechanical joining technologies these development cycles and thereby the effort for implementation into production must be reduced. In this paper, the numerical data-acquisition and the development of algorithm-based and analytical models for strength prediction for SPR-ST and Clinching is described. Therefore, an extensive experimental and numerical database regarding the SPR-ST process and strength of steel and aluminum joints with tensile strengths of the sheets between 200 - 1000 MPa was generated for the building of the models. This process data could then be used for the training and evaluation of different prediction models. The goal of the research presented here is to enable an immediate prediction of the quasi-static joint strength, based on the input parameters such as properties of the parts to be joined and process parameters.