Data-Driven, Physics-Based, or Both: Fatigue Prediction of Structural Adhesive Joints by Artificial Intelligence

Abstract

Here, a comparative investigation of data-driven, physics-based, and hybrid models for the fatigue lifetime prediction of structural adhesive joints in terms of complexity of implementation, sensitivity to data size, and prediction accuracy is presented. Four data-driven models (DDM) are constructed using extremely randomized trees (ERT), eXtreme gradient boosting (XGB), LightGBM (LGBM) and histogram-based gradient boosting (HGB). The physics-based model (PBM) relies on the Findley’s critical plane approach. Two hybrid models (HM) were developed by combining data-driven and physics-based approaches obtained from invariant stresses (HM-I) and Findley’s stress (HM-F). A fatigue dataset of 979 data points of four structural adhesives is employed. To assess the sensitivity to data size, the dataset is split into three train/test ratios, namely 70%/30%, 50%/50%, and 30%/70%. Results revealed that DDMs are more accurate, but more sensitive to dataset size compared to the PBM. Among different regressors, the LGBM presented the best performance in terms of accuracy and generalization power. HMs increased the accuracy of predictions, whilst reducing the sensitivity to data size. The HM-I demonstrated that datasets from different sources can be utilized to improve predictions (especially with small datasets). Finally, the HM-I showed the highest accuracy with an improved sensitivity to data size.

Investigating permafrost carbon dynamics in Alaska with artificial intelligence

“…Concurrently, multimodal applications with AI and observational data quantify, emulate, resolve, and improve upon the physical representation of the climate system with increased flexibility, efficiency, and precision (Koppe et al 2019, Xu et al 2021). Limitations inherent to tractable, modular-limited frameworks and sensitive, computationally intensive AI approaches present opportunities for unifying data harmonization, information sharing, and bias correction among physics-based and data-driven methods (Cuomo et al 2022, Slater et al 2022, Fernandes et al 2023. In response to these sustained and co-emerging limitations, we capture abrupt and persistent changes in subsurface conditions and disentangle control factors driving the PCF with a process-informed, data-driven formulation.…”

Fatigue life prediction of composite bolted joints based on finite element model and machine learning

“…Traditional fatigue prediction methods for composite bolted joints can be broadly categorized into three types: S–N curve model, residual stiffness/strength model, and progressive damage model 5–9 . The S–N curve model does not consider the fatigue cumulative damage and is usually used in combination with the fatigue failure criterion.…”

“…Traditional fatigue prediction methods for composite bolted joints can be broadly categorized into three types: S-N curve model, residual stiffness/strength model, and progressive damage model. [5][6][7][8][9] The S-N curve model does not consider the fatigue cumulative damage and is usually used in combination with the fatigue failure criterion. With the wide application of composite and the development of computers, it has been discovered that composite exhibits progressive damage characteristics.…”

Design of Adhesive Bonded Joints

“…Dynamic impact and fatigue testing: understanding how adhesive joints respond to dynamic loading, impact forces, and fatigue conditions is another area garnering significant attention [39][40][41]. Researchers are conducting experiments to elucidate the dynamic behavior of adhesive joints, providing insights into their resilience and failure mechanisms under varying loading rates [42].…”