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Self-piercing riveting (SPR) are one of most important joining approaches in lightweight vehicle design for Body-in-white (BIW) manufacturing. Numerical simulation of the riveting process could significantly boost design efficiency by reducing trial-and-error experiments. The traditional Finite Element Method (FEM) with element erosion is hard to capture the large plastic deformation and complex failure behaviors in the SPR process. The smoothed Particle Galerkin Method (SPG) is a genuine meshless method based on Galerkin's weak form, which uses a novel bond-based failure mechanism to keep the conservation of mass and momentum during the material failure process. This study utilizes a combined FEM and SPG approach to join Aluminum sheet 5754 using a full three-dimensional (3D) model in LS-DYNA/explicit.

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).

As an alternative lightweight material, chopped carbon fiber reinforced Sheet Molding Compound (SMC) composites, formed by compression molding, provide a new material for automotive applications. In the present study, the monotonic and fatigue behavior of chopped carbon fiber reinforced SMC is investigated. Tensile tests were conducted on coupons with three different gauge length, and size effect was observed on the fracture strength. Since the fiber bundle is randomly distributed in the SMC plaques, a digital image correlation (DIC) system was used to obtain the local modulus distribution along the gauge section for each coupon. It was found that there is a relationship between the local modulus distribution and the final fracture location under tensile loading. The fatigue behavior under tension-tension (R=0.1) and tension-compression (R=-1) has also been evaluated.

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.

Self-pierced rivet (SPR) and adhesive are two important joining technologies widely used in automobile industry, and they are often used together to form a hybrid joint. SPR and adhesives can often be used in close proximity in a component, leading to an interaction of the two joints. This interaction can influence the corrosion and noise, vibration and harshness (NVH) characteristics of the structure, as well as its strength and durability. In this paper, the stress distribution in an SPR-adhesive hybrid joint is evaluated by using the finite element method, and then compared with that in an adhesive joint. Results indicate that the stress concentrates at the edge of adhesive layer in hybrid joint and adhesive joint and around the rivet in an SPR joint. The effect of rivet is numerically investigated by either removing the rivet from the hybrid joint or changing the position of the rivet on the overlapping area.

In present paper, the process of joining aluminum alloy 6111T4 and steel HSLA340 sheets by self-piercing riveting (SPR) is studied. The rivet material properties were obtained by inverse modeling approach. Element erosion technique was adopted in the LS-DYNA/explicit analysis for the separation of upper sheet before the rivet penetrates into lower sheet. Maximum shear strain criterion was implemented for material failure after comparing several classic fracture criteria. LS-DYNA/implicit was used for springback analysis following the explicit riveting simulation. Large compressive residual stress was observed near frequent fatigue crack initiation sites, both around vicinity of middle inner wall of rivet shank and upper 6111T4 sheet.