Predicting creep failure life in adhesive-bonded single-lap joints using machine learning.

ABSTRACT: Accurately predicting the creep failure life of adhesive joints, particularly single-lap adhesive joints (SLAJs), remains still a significant challenge, requiring substantial time and resources and the ability to predict the duration of creep failure in SLAJs is critical to ensuring structural integrity and reducing the failure of creep-prone adhesive joints. In this study, machine learning (ML) was used to identify the critical features that ultimately influence the durability of SLAJs due to creep. These key features were determined through correlation analysis and sequential feature selection. Multiple ML algorithms were employed to analyze complex relationships among key features and predict creep failure life. Finally, the results of the analysis highlight the importance of features s

SUBMITTER: Jan F 

PROVIDER: S-EPMC11865317 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature