Predicting low velocity impact damage and Compression-After-Impact (CAI) behaviour of composite laminates

By Wei Tan, Brian G. Falzon, Louis N. S. Chiu, Mark Price. 2015

479 citations recorded by the source corpus.

Low-velocity impact damage can drastically reduce the residual strength of a composite structure even when the damage is barely visible. The ability to computationally predict the extent of damage and compression-after-impact (CAI) strength of a composite structure can potentially lead to the exploration of a larger design space without incurring significant time and cost penalties. A high-fidelity three-dimensional composite damage model, to predict both low-velocity impact damage and CAI strength of composite laminates, has been developed and implemented as a user material subroutine in the commercial finite element package, ABAQUS/Explicit. The intralaminar damage model component accounts for physically-based tensile and compressive failure mechanisms, of the fibres and matrix, when subjected to a three-dimensional stress state. Cohesive behaviour was employed to model the interlaminar failure between plies with a bi-linear traction–separation law for capturing damage onset and subsequent damage evolution. The virtual tests, set up in ABAQUS/Explicit, were executed in three steps, one to capture the impact damage, the second to stabilize the specimen by imposing new boundary conditions required for compression testing, and the third to predict the CAI strength. The observed intralaminar damage features, delamination damage area as well as residual strength are discussed. It is shown that the predicted results for impact damage and CAI strength correlated well with experimental testing without the need of model calibration which is often required with other damage models.

Subfields: Building and Construction, Mechanical Engineering, Mechanics of Materials

View Paper: https://www.semanticscholar.org/paper/a701b8610bbc4dfe6cd11284658c14b5f6488c70

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Building and Construction

  1. F. Naya
  2. Jon Molina-Aldareguia
  3. Brian G. Falzon
  4. Javier Llorca
  5. Wei Tan
  6. Carlos Gonz'alez

In Mechanical Engineering

  1. F. Naya
  2. Jon Molina-Aldareguia
  3. Brian G. Falzon
  4. Javier Llorca
  5. Wei Tan
  6. Carlos Gonz'alez

In Mechanics of Materials

  1. F. Naya
  2. Jon Molina-Aldareguia
  3. Wei Tan
  4. Chao Zhang
  5. Brian G. Falzon
  6. Javier Llorca
  7. Carlos Gonz'alez
  8. Cláudio S. Lopes

Citations in the corpus