Machine learning-based assessment of hygrothermal aging performance in CFRP-aluminum alloy adhesive bonded structures

ABSTRACT

Adhesive bonding is an effective technique for connecting fiber reinforced composites (FRPs) and plays a crucial role in determining load transfer capability and overall structural safety. However, long-term exposure to temperature and moisture can degrade adhesive joints, highlighting the significant importance of accurately predicting their durability when designing reliable adhesive structures. This study aims to address the industry’s demand and academic interest by proposing a convenient and accurate prediction method to evaluate the durability of adhesive structures subject to complex stresses. Laboratory accelerated aging experiments were conducted on butt joints (experiencing pure normal stress) and shear joints (experiencing pure shear stress). Quasi-static tensile tests and adhesive FTIR tests were performed to establish a dataset of adhesive functional group absorbance and failure strength under single stress conditions. To compare and assess the predictive performance of different models for aging behaviors, Artificial Neural Network (ANN), Support Vector Regression (SVR) and Random Forest (RF) models were selected. The model hyperparameters were further optimized using the Genetic Algorithm (GA). The results demonstrated that the ANN model exhibited superior correlation and accuracy compared to the SVR, and RF. The characteristic importance analysis revealed that the hydrolysis and post-curing of adhesives were the primary factors affecting joint performance under hygrothermal environments. By incorporating environmental degradation factors and defining initial failure criteria, the CZM constitutive model was modified and a predictive method was established for assessing the hygrothermal aging behavior of CFRP/aluminum alloy bonding structures. This method effectively eliminates the effect the intricate aging process (e.g. aging time, aging conditions) and establishes a direct correlation between the molecular structure of adhesive and the macroscopic mechanical properties of the bonded joint, which serves as a valuable reference and guidance for the safety design of automotive composite material bonding structures throughout their entire lifespan.

KEYWORDS:

• CFRP
• adhesively bonded joints
• hygrothermal aging
• artificial neural network
• CZM

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by the National Natural Science Foundation of China [Grant No. 51775230] and Key Scientific Research Project of Colleges and Universities in Henan Province [Grant No. 22A416007]; Postdoctoral research grant in Henan Province [Grant No. 202103081].