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Self-piercing rivets (SPR) are efficient and economical joining methods used in the manufacturing of lightweight automotive bodies. The finite element method (FEM) is a potentially effective way to assess the joining process of SPRs. However, uncertain parameters could lead to significant mismatches between the FEM predictions and physical tests. Thus, a sensitivity study on critical model parameters is important to guide the high-fidelity modeling of the SPR insertion process. In this paper, an axisymmetric FEM model is constructed to simulate the insertion process of the SPR using LS-DYNA/explicit. Then, several surrogate models are evaluated and trained using machine learning methods to represent the relations between selected inputs (e.g., material properties, interfacial frictions, and clamping force) and outputs (cross-section dimensions).

Weld lines occur when melt flow fronts meet during the injection molding of plastic parts. It is important to investigate the weld line because the weld line area can induce potential failure of structural application. In this paper, a weld line factor (W-L factor) was adopted to describe the strength reduction to the ultimate strength due to the appearance of weld line. There were two engineering thermoplastics involved in this study, including one neat PP and one of talc filled PP plastics. The experimental design was used to investigate four main injection molding parameters (melt temperature, mold temperature, injection speed and packing pressure). Both the tensile bar samples with/without weld lines were molded at each process settings. The sample strength was obtained by the tensile tests under two levels of testing speed (5mm/min and 200mm/min) and testing temperatures (room temperature and -30°C). The results showed that different materials had various values of W-L factor.

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.