Showing 2 results for Single-Lap Joint
Mr Seyyed Mohsen Mousavi, Miss Seyyedeh Maryam Mamduhi, Dr Javad Marzbanrad,
Volume 15, Issue 4 (12-2025)
Abstract
In lightweight body-in-white design, joints must not only provide strength but also allow for ductility and sufficient energy absorption. In this study, Single Lap Joints (SLJs) made with adhesive bonding are compared experimentally with those joined by Resistance Spot Welding (RSW) in low-carbon steel sheets. The influence of overlap length (15 and 25 mm) and weld number (one or two spots) is examined. Tensile force–displacement tests, conducted at room temperature with a crosshead speed of 1 mm/min, revealed that extending the overlap from 15 to 25 mm improved the peak load, final displacement, and fracture energy of the adhesive joints. Among the tested configurations, double spot welds (2RSW) provided the greatest capacity and toughness. However, adhesive joints with a 25 mm overlap (AB25) exhibited higher strength than single spot welds (1RSW), while their ductility was comparable. The observed failure modes varied across the joint types. In resistance spot welds, failure occurred mainly through button pull-out, whereas adhesive joints exhibited a mixed adhesive–cohesive failure mode. In contrast, the 2RSW specimens displayed pull-out and necking sequences, reflecting load sharing between the weld nuggets. Overall, the findings suggest straightforward design guidelines. When maximum strength and energy absorption are required, two Spot Welds (2RSW) are the best choice. On the other hand, AB25 joints, with a 25 mm overlap, provide higher strength than single Spot Welds (1RSW).
Mr Mohamad Masoud Mohamadkhani, Dr Farzad Ghafoorian,
Volume 16, Issue 2 (6-2026)
Abstract
Adhesively bonded joints, particularly the Single-Lap Joint (SLJ), are widely used in structural applications. However, stress concentrations at the overlap edges significantly limit their load-bearing capacity. Modifying the adherend geometry by introducing notches is an effective technique for stress redistribution and strength improvement. In this study, a Machine Learning (ML) approach is proposed to predict the peak peel and shear stresses in notched SLJs based on geometric parameters, namely the notch depth, notch angle, notch width, and the notch distance from the end of the overlap. First, a comprehensive dataset comprising 1183 Finite Element Analysis (FEA) simulations was generated. Subsequently, four ML models—Artificial Neural Networks (ANN), Extreme Gradient Boosting (XGBoost), Random Forest (RF), and Support Vector Machines (SVM)—were developed to map the geometric features to the maximum stress values. To ensure robust performance and prevent overfitting, a Randomized Search Cross-Validation technique was employed for hyperparameter tuning across all models prior to final evaluation. The evaluation results demonstrated highly accurate predictions across all algorithms. Specifically, the ANN model achieved the most precise results, yielding the lowest Mean Absolute Percentage Error (MAPE) for both peel (0.28%) and shear (0.16%) stresses. The SVM, XGBoost, and RF models also exhibited excellent predictive capabilities, with all MAPE values remaining well below 1%. Feature importance analysis from XGBoost and SHapley Additive exPlanations (SHAP) applied to the optimal ANN revealed that notch distance from the edge and notch depth are the key design parameters influencing stress distributions.
