Verification and validation of residual stresses in composite structures. (15th June 2018)

• Record Type: Journal Article
• Authors: Nelson, Stacy; Hanson, Alexander; Briggs, Timothy; Werner, Brian
• Is Part Of: Composite structures. Volume 194(2018)
• Page Start: 662
• Page End: 673
• Publication Date: 2018-06-15
• Subjects: Computational modeling -- Finite element analysis (FEA) -- Statistical properties/methods; Composite construction -- Periodicals; Composites -- Périodiques; 624.18
• DOI: 10.1016/j.compstruct.2018.04.017
• Languages: English
• ISSNs: 0263-8223

Abstract

Abstract: Process-induced residual stresses occur in composite structures composed of dissimilar materials. As these residual stresses could result in fracture, their consideration when designing composite parts is necessary. However, the experimental determination of residual stresses in prototype parts can be time and cost prohibitive. Alternatively, it is possible for computational tools to predict potential residual stresses. Therefore, the objectives of the presented work are to demonstrate an efficient method for simulating residual stresses in composite parts, as well as the potential value of statistical methods during analyses for which material properties are unknown. Specifically, a simplified residual stress modeling approach is implemented within Sandia National Laboratories' SIERRA/SolidMechanics code. Concurrent with the model development, bi-material composite structures are designed and manufactured to exhibit significant residual stresses. Then, the presented modeling approach is rigorously verified and validated through simulations of the bi-material composite structures' manufacturing processes, including a mesh convergence study, sensitivity analysis, and uncertainty quantification. The simulations' final results show adequate agreement with the experimental measurements, indicating the validity of a simple modeling approach, as well as a necessity for the inclusion of material parameter uncertainty in the final residual stress predictions.