<div>In this article, we investigated the effects of material parameters on the
clinching joint geometry using finite element model (FEM) simulation and machine
learning-based metamodels. The FEM described in this study was first developed
to reproduce the shape of clinching joints between two AA5052 aluminum alloy
sheets. Neural network metamodels were then used to investigate the relation
between material parameters and joint geometry as predicted by FEM. By
interpreting the data-driven metamodels using explainable machine learning
techniques, the effects of the hard-to-measure material parameters during the
clinching are studied. It is demonstrated that the friction between the two
metal sheets and the flow stress of the material at high (up to 100%) plastic
strain are the most influential factors on the interlock and the neck thickness
of the clinching joints. However, their dependence on the material parameters is
found to be opposite. First, while the friction between the two metal sheets
promotes the formation of the interlock, it reduces the neck thickness and thus
increases the risk of breaking in this region. Second, it is easier to form the
interlock if the deformed material exhibits small flow stress at high plastic
strain, but the neck thickness tends to be thinner in this case. The identified
material parameters help to significantly reduce the relative error between the
simulated results and the experimental results, not only in the configurations
from which they are identified but also in a new configuration. This methodology
shows its potential in the cases where material parameters are not available or
difficult to measure.</div>
