An analytical method to predict stiffener debonding in postbuckled compression panels
Year: 2025
Type: article
Abstract: Fuel efficiency, production rate and tooling costs for composite aircraft can all be improved if the current practice of co-curing skin and stiffeners is replaced by secondary bonding of pre-cured stiffeners to pre-cured skin, whilst exploiting postbuckling strength. Component-level Finite Element Analysis of skin-stiffener debonding in postbuckled panels is extremely costly due to the small mesh size required to resolve stresses at the local crack front. A new strip-based analytical method for long panels is presented, which calculates the strain energy release rate for debonding using the sensitivity of skin buckling loads to small perturbation of bonded skin widths, representing damage growth. The predictions of the strip analysis are compared with experimental test and FEA, namely the Cohesive Zone Method (CZM) and Virtual Crack Closure Technique (VCCT). For a skin panel with a co-cured thermoset flange, the debonding load predicted by the strip method is within 1% of the CZM. When a thermoplastic (welded) skin-flange bond is analyzed, the CZM load is 9% lower than the new method, due to the inherent inaccuracy of CZM in the case of ductile propagation. A welded hat-stiffened skin debonds from beneath the hat, as observed experimentally and by VCCT. In this case, the discrepancy of the new method is within 2% of experimental and VCCT debonding loads. The strip method is independent of mesh size, up to 1×107 times faster than FEA and can be used for design and uncertainty quantification at stiffened-panel scale, providing a route to weight saving and structural certification.
Source: Composite Structures
Authors: Richard Butler, Chanel Fallon, Thomas Maierhofer, David Kennedy
Institutions: University of Bath, GKN (United Kingdom), Cardiff University
Language: English
Subfield: Mechanics of Materials
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $4,660
OpenAlex ID: W4417302670
