Strength Prediction of Self-Piercing Riveted Joints Using Practical Regression and Bayesian Neural Network Magna, Cosma Engineering Europe GmbH

Format:

Book

Conference/Event

Author/Creator:

Soproni, Istvan, author.

Contributor:

Balaji, Ashwin

Kulange, Deepak

Liu, Zongyue

Womack, Darren

Conference Name:

Automotive Technical Papers (2025-01-01 : Warrendale, Pennsylvania, United States)

Language:

English

Physical Description:

1 online resource cm

Place of Publication:

Warrendale, PA SAE International 2025

Summary:

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

Notes:

Vendor supplied data

Publisher Number:

2025-01-5068

Access Restriction:

Restricted for use by site license