首页|Khulna University Researchers Discuss Research in Machine Learning (Evaluation o f stress distributions in trimaterial bonded joints with nano-resin adhesive usi ng machine learning models)
Khulna University Researchers Discuss Research in Machine Learning (Evaluation o f stress distributions in trimaterial bonded joints with nano-resin adhesive usi ng machine learning models)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Investigators discuss new findings in artificial intelligence. According to news reporting from Khulna University by N ewsRx journalists, research stated, “Adhesive bonded joints hold significant imp ortance across various industrial sectors in modern engineering, owing to their lightweight nature and myriad advantages.” The news correspondents obtained a quote from the research from Khulna Universit y: “The rising demand for trimaterial joints underscores their utility and versa tility. In these joints, the choice of materials for both adherends greatly infl uences their strength, structural reliability, and overall characteristics. Whil e numerous researches have extensively analyzed stress distributions, their effe cts, and behaviors, many have relied on a one-factor-at-a-time approach, focusin g solely on individual design variables’ effects. However, recognizing the intri cate interplay among various material combinations and their collective impact o n 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 w ell as predict stress distributions for any random combinations of upper and low er adherend materials. Dataset of total 178 random material combinations were ut ilized for the training phases with 5-fold cross validation and model tuning. Ho wever, the decision tree regressor emerged as the optimized model by comparing t he quantitative metrics of accuracy benchmark as well as the prediction outcomes obtained through all the machine learning models.”
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