Prediction of tensile strength for carbon fiber reinforced composites through transfer learning

With the emergence of Industry 4.0 and the widespread use of smart computing, there is an increasing demand for lightweight, corrosion-resistant, high-performance materials in intelligent manufacturing, attracting extensive attention to researching new composite materials. Intelligent-driven composite material performance analysis and prediction based on big data and machine learning have become important means for research on new composites. However, previous studies on composite performance analysis often focused on material characteristics, neglecting the combined influence of processing parameters, structure, and application environments. On the other hand, in actual factories, the process of obtaining the above auxiliary data is very difficult and time-consuming. Therefore, this article focuses on carbon fiber reinforced composites (CFRP), takes advantage of transfer learning to achieve high accuracy in smaller datasets, and proposes a data-driven framework which can not only use the characteristics of the material itself but also increase the material structure and processing parameters to improve the prediction accuracy of the tensile strength of new composites. After comparing four commonly used base learning models, the coefficient of determination (R 2 ) of the ElasticNet model is improved by 7.67% and the mean absolute error (MAE) is reduced by 55.53%. For other models, please refer to results and discussion.