Automotive Science and Engineering

en Stress Prediction in Notched Single-Lap Adhesive Joints Using Machine Learning Approach ساخت Manufacturing پژوهشي Research 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. Machine learning, Adhesive bond joint, Single-Lap Joint, Notched Adherend, Stress Prediction 5080 5093 http://ase.iust.ac.ir/browse.php?a_code=A-10-903-1&slc_lang=en&sid=1 Mohamad Masoud Mohamadkhani mohamadmasoudmohamadkhani@gmail.com 180031947532846005344 180031947532846005344 No School of Mechanical Engineering, Iran University of Science and Technology, Tehran 16846-13114, Tehran, Iran Farzad Ghafoorian farzadghafoorianiust@gmail.com 180031947532846005345 180031947532846005345 Yes School of Mechanical Engineering, Iran University of Science and Technology, Tehran 16846-13114, Tehran, Iran