Mechanics-Based Optimization of Shear Lap Joints for Enhanced Force Capacity

Bonded single lap joints are used to join structural components in automotive, aerospace, and other engineered systems. The stresses in the joint are nonuniform, and high stresses near the edge typically limit the force capacity of the joint. Here, we optimize the design of the adherend geometry to improve the stress uniformity at the interface and enhance the force capacity. Through the use of machine learning and mechanics-based finite element analysis, we quantify the functional relationship between the geometry of the adherend and the variance of the interface stress distribution. A neural network is used to identify an optimized adherend geometry with improved stress uniformity for cases with different constraints on joint stiffness and manufacturability. A fracture mechanics analysis is used to predict the force capacity enhancement of the optimized designs, and the results are verified through experiments on joints consisting of aluminum adherends bonded with a cyanoacrylate adhesive. The experimentally measured force-capacity enhancement is 2.4×, which closely agrees with model predictions.