Evaluation of stress distributions in trimaterial bonded joints with nano-resin adhesive using machine learning models

Adhesive bonded joints hold significant importance across various industrial sectors in modern engineering, owing to their lightweight nature and myriad advantages. The rising demand for trimaterial joints underscores their utility and versatility. In these joints, the choice of materials for both adherends greatly influences their strength, structural reliability, and overall characteristics. While numerous researches have extensively analyzed stress distributions, their effects, and behaviors, many have relied on a one-factor-at-a-time approach, focusing solely on individual design variables' effects. However, recognizing the intricate interplay among various material combinations and their collective impact on overall performance, this study employs various types of White-box, Black-box, and Grey-box machine learning algorithms to identify an optimized ML model as well as predict stress distributions for any random combinations of upper and lower adherend materials. Dataset of total 178 random material combinations were utilized for the training phases with 5-fold cross validation and model tuning. However, the decision tree regressor emerged as the optimized model by comparing the quantitative metrics of accuracy benchmark as well as the prediction outcomes obtained through all the machine learning models. The maximum prediction accuracy attained was an impressive 99.97 %, while the minimum recorded was 89.74 %. This research aims to identify tailored machine learning model specifically for trimaterial bonded joints where nano layer of resin is utilized as the adhesive.