Self-piercing riveting (SPR) is a key joining method in multi/thin-material
automotive structures, yet accurately predicting the mechanical strength of SPR
joints remains challenging due to numerous influencing factors. Empirical
engineering equations [1] provide a
foundation for estimating lap-shear and cross-tension strength but require
several geometric parameters that are often unavailable in the design phase.
To address this limitation, we extract and leverage the core physical
relationships embedded in these formulas. By reformulating the dependence of
joint strength on the yield strength and total thickness of the sheet stack as
practical regression models, we enable strength prediction using only commonly
available material properties.
Furthermore, a Bayesian convolutional neural network (BCNN) model is developed to
incorporate additional material features, offering improved prediction accuracy
and uncertainty quantification.