Intan Mardiono, Imang Eko Saputro, Hsuan-Fan Chen, Shao-Kang Lu, Hao-Han Chang, Wei-Tse Tseng, Yiin-Kuen Fuh
This study contributes to examine the impact of design variations on self-piercing riveting (SPR) in dissimilar metal joints, focusing on DP780, DP980, 1180MS steel, and Al6061 aluminum sheets with total thicknesses ranging from 2 to 4 mm. The study aligns with the target for aluminum content in light vehicles, which is set at 570 net pounds per vehicle by 2030. On the other hand, dual-phase steel combinations are crucial in the automotive industry due to their superior strength-to-weight ratio and excellent formability, which enhance vehicle safety and fuel efficiency. To address these factors, the study presents several key innovations: (1) the development of a novel artificial neural network (ANN) model for predicting riveting quality and mechanical properties, capturing parameters not covered by simulations alone; (2) the use of a contour graph method to optimize sheet thickness and die depth, introducing a new approach for achieving optimal SPR results; and (3) the establishment of a new correlation between process chain quality and shear test evaluations for self-piercing rivets in dissimilar metals. Results show that a die depth of 2.25 mm is most effective for joining 1-mm (1180MS) and 2-mm (Al6061) materials, achieving a maximum tensile force of 9.26 kN and absorbing up to 36.02 J of energy. The ANN model demonstrated high prediction accuracy with MAPEs ranging from 7.56 to 15.8%, highlighting its potential for integration into industrial applications. By allowing precise prediction of joint performance, the ANN model and the contour graph offer a transformative tool to optimize the SPR process, minimize development costs, and improve production efficiency in automotive manufacturing. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.
Department of Mechanical Engineering, National Central University, No. 300, Zhongda Road, Zhongli District, Taoyuan City, 32001, Taiwan; Lioho Machine Works Ltd., No. 334, Sec. 2, Xinsheng Rd., Zhongli Dist., Taoyuan City, 32056, Taiwan; Industrial Engineering Study Program, Institut Teknologi Sumatera, Terusan Ryacudu Street, South Lampung, 35365, Indonesia