An investigation of strain energy release rate models for real-time prognosis of fiber-reinforced laminates. (1st April 2017)

• Record Type: Journal Article
• Authors: Corbetta, Matteo; Saxena, Abhinav; Giglio, Marco; Goebel, Kai
• Is Part Of: Composite structures. Volume 165(2017)
• Page Start: 99
• Page End: 114
• Publication Date: 2017-04-01
• Subjects: CFRP -- Fatigue -- Damage estimation -- Energy release rate -- Structural health monitoring -- Damage prognosis; Composite construction -- Periodicals; Composites -- Périodiques; 624.18
• DOI: 10.1016/j.compstruct.2017.01.002
• Languages: English
• ISSNs: 0263-8223

Abstract

Abstract: Technological advancements in real-time distributed sensing and processing for structural health monitoring systems have enabled exploration of the next frontier in structural health monitoring for in situ condition-based prediction of remaining life of damaged or aging structures. In that context, model-based prognostics methods have shown considerable promising results. These methods require that suitable damage progression models are available or be developed. Recent works have shown that energy release rate models work effectively for predicting material stiffness degradation based on matrix-cracking. However, since delamination and matrix-cracking damage modes are known to co-exist and fuel each other's progression, it is desirable to investigate extension of these models for multiple damage modes. To that end, this paper analyzes several multiple damage-mode models from composite modeling literature and assesses them against experimental data from run-to-failure aging experiments. These models aim to estimate and correlate strain energy release rate and the residual stiffness as a function of the damage extent. Model review in this work reports modeling behavior and mathematical complexity along with strengths and limitations of these models. This is expected to guide selection of suitable model for a more robust prognostic solution generalized for more realistic degradation scenarios.