Development of General data-based Process Models for Self-Pierce Riveting

The determination of ideal process parameters for mechanical joining processes such as self-pierce riveting currently requires a comprehensive understanding of the process, the availability of the materials to be joined and the corresponding system technology. General process models can simplify the use of these joining technologies, accelerate development cycles and thereby reduce the effort for implementation into production. In this paper, the development of general data-based process models for the mechanical joining method self-pierce riveting with semi-tubular rivet is described. Extensive experimental and numerical investigations with more than 2300 joint combinations for steel and aluminum sheets with tensile strengths between 240 - 1020 MPa were generated for the building of the models. Based on these results, different meta-models are fused into general data-based process models for the self-pierce riveting process in order to show the general relationships between material properties, process parameters and joining results. The paper discusses the acquisition of the experimental and numerical data, the statistical methods for evaluation and the application of the data-based process models.