Deep-learning architectures were developed for the self-piercing riveting (SPR) process to predict the cross-sectional shape from the scalar input of the punch force. Traditionally, the SPR process is studied using a physic-based approach, including finite element modeling, but in this study, a data-driven approach consisting of two supervised deep-learning models was proposed. The first model was used for data transformation from an optical microscopic image to a material segmentation map, which characterizes the shape and location of the two sheets and the rivet by applying a convolutional neural network (CNN)based deep-learning structure. To validate the developed models, two types of sheet combinations were tested, namely, carbon-fiber-reinforced plastic (CFRP) and galvanized dual-phase steel (GA590DP) sheets, and steel alloy (SPFC590DP) and aluminum alloy (Al5052-H32) sheets. The transformation was performed with a mean intersection-over-union of 98.50% and a mean pixel accuracy of 99.78%. The next model, which was a novel generative model based on a CNN and conditional generative adversarial network with residual blocks, was then trained to predict the cross-sectional shape from the input punch force. The predicted cross-sectional shapes were compared with the experimental results of SPR. The overall accuracy was 94.20% for CFRP-GA590DP and 96.31% for SPFC590DP-Al5052, with respect to three key geometrical indexes, namely, rivet head height, interlock length, and bottom thickness.
In this study, the self-piercing rivet (SPR) joining of vibration-damping steel and aluminum alloy (Al5052-H32) is successfully carried out, for the first time to our knowledge, and the effects of die type and joint configuration on the mechanical performance, failure mode, and geometrical characteristics of the new joint are investigated. The vibration-damping steel and Al5052-H32 SPR joint exhibits the largest tensile–shear load when a flat die is used. An increase in the die taper angle and diameter decreases the mechanical performance of the joint due to the increase in volume of the die, leading to a smaller interlock width of the joint. The joint configuration with Al5052-H32 as a top sheet has superior mechanical performance compared with the reverse configuration, owing to the increase of the interlock width. All SPR joints of vibration-damping steel and Al5052-H32 show consistent rivet pull-out failure, regardless of the joint configuration, because of relatively small interlock width. It is also found that these SPR joints show better mechanical performance than those of SPFC590DP (a skin material of the vibration-damping steel) and Al5052-H32 under the Al5052-H32–top configuration.
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