Development of aviation industry-oriented methodology for failure predictions of brittle bonded joints using probabilistic machine learning. (1st October 2022)

• Record Type: Journal Article
• Authors: Freed, Yuval; Zobeiry, Navid; Salviato, Marco
• Is Part Of: Composite structures. Volume 297(2022)
• Publication Date: 2022-10-01
• Subjects: Adhesively Bonded Joints -- Composite Materials -- Virtual Crack Closure Technique -- Machine Learning -- Gaussian Process Regression -- Mixed Mode Bending -- Failure Predictions -- Design Allowables; Composite construction -- Periodicals; Composites -- Périodiques; 624.18
• DOI: 10.1016/j.compstruct.2022.115979
• Languages: English
• ISSNs: 0263-8223

Abstract

Abstract: The bonding assembly concept of cured aerospace composite parts is considered an efficient approach from almost every perspective. It simplifies the design and provides great opportunities for weight and cost reductions. However, even with strict quality assurance, such assembly may lead to undetectable low bond-line strengths, usually referred to as "kissing bonds". As part of a certification effort of bonded composites, the maximum allowed disbond size has to be determined. In this study, an approach for determination of the residual strength of bonded joints is proposed. Virtual crack closure failure parameters of the Loctite EA 9394 paste adhesive are determined based on a comprehensive test campaign. Predictions from more than 5, 000 finite element simulations were analyzed using a machine learning strategy. The accuracy of the optimal failure parameters was assessed, and the failure parameters were statistically adjusted to account for predictions variability. The validity of the proposed approach and the corresponding failure parameters were examined using two classic bonded joints design concepts, bonded scarf joint and bonded T-joint.