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The process parameters to manufacture structural aluminum alloys are critical to their ductility. In particular, quench rate after solution heat treatment impacts the extent of grain boundary precipitation and the formation of precipitate free zone (PFZ) during later artificial aging. Cu-containing 6XXX alloys used for high strength automotive applications are quench sensitive as the Cu addition leads to Q-phase precipitation at grain boundaries, resulting in loss of ductility, which can negatively affect downstream manufacturing steps such as automotive joining and forming processes. Self-piercing rivet (SPR) joining, is a single step, spot joining process used to mechanically connect sheet materials together in automotive body structures. Ductility has been identified as an important metric of material rivet-ability or the ability to make a successful, crack-free SPR joint.

Self-piercing rivet (SPR) joints are a key joining technology for lightweight materials, and they have been widely used in automobile manufacturing. Manual visual crack inspection of SPR joints could be time-consuming and relies on high-level training for engineers to distinguish features subjectively. This paper presents a novel machine learning-based crack detection method for SPR joint button images. Firstly, sub-images are cropped from the button images and preprocessed into three categories (i.e., cracks, edges and smooth regions) as training samples. Then, the Artificial Neural Network (ANN) is chosen as the classification algorithm for sub-images. In the training of ANN, three pattern descriptors are proposed as feature extractors of sub-images, and compared with validation samples. Lastly, a search algorithm is developed to extend the application of the learned model from sub-images into the original button images.

Tensile and fatigue properties of continuous braided carbon fiber reinforced polymer (CFRP) composite to AA6111 self-piercing riveted (SPR) lap shear joints are presented. Rivets were inserted at two target head heights separated by 0.3 mm. Even within the narrow range of head heights considered, the flushness of the rivet head was found to have a dominant effect on both the monotonic and fatigue properties of the lap shear SPR joints. Joints created with a flush head resulted in a greater degree of fiber breakage in the top ply of the CFRP laminate, which resulted in lower lap shear failure load as compared to SPR joints produced with a proud rivet head. Irrespective of the lap shear failure load, rivet pullout was the most common failure mode observed for both rivet head heights. In fatigue tests, the SPR joints produced with a proud head exhibited higher fatigue life compared to SPR joints produced with a flush head.