bearing response prediction in hydrothermal aged carbon fiber reinforced epoxy composite joints using machine learning techniques

The work focuses on predicting the bearing response in hydrothermal-aged carbon fiber-reinforced epoxy composite (cfrec) joints through the utilization of machine learning techniques. cfrec are extensively employed in aerospace and other high-performance applications, and their long-term structural integrity is of paramount importance. the hydrothermal aging process can significantly affect the mechanical behavior of such composites, particularly in joint configurations. in this research, an innovative support vector regression approach is present that leverages machine learning algorithms to forecast the bearing response of cfrec joints after undergoing hydrothermal aging. the study encompasses the development of predictive models using a comprehensive dataset of experimental observations. the machine learning technique, support vector regression is trained and evaluated to assess their accuracy and reliability in predicting bearing response. the results show that the overall percent reduction in bearing response, after 30 days of pristine composite bolted joints at 0 nm bolt torque shows reductions of 23.22 % at 65°c, respectively. conversely, under the same conditions, mwcnts added composite bolted joints exhibit only a 9.2% reduction. the predictive models find the value of 0.0081 rsme and 0.8 r2 respectively through support vector regression confirming that the predicted values lie in between the upper and lower bond.