Identification of Composite Delamination Using the Krawtchouk Moment Descriptor

Damage assessment of composite materials is crucial for health monitoring of engineering structures. It is particularly important to detect damage invisible to the human eye caused by low speed impact. Optical non-contact sensing techniques enable full-field measurements from structural responses. However, damage is generally associated with its local effect on deformation and strain patterns while full-field measured data is highly information redundant. It is possible to apply image processing techniques [1, 2] to extract succinct and efficient features, or attributes, from full-field data. Iterative reanalysis of large and detailed numerical models is generally very expensive and may not be feasible. Meta modeling is one of the practical ways to overcome the problem of high computational cost. In this paper, a case study of composite delamination assessment based on simulation is discussed. The damage mode in the composite plate is assumed to be a delamination of elliptical shape. Surface strain of a specimen under tensile loading is considered to be the measured structural output. The Krawtchouk moment descriptor is applied to extract a small number of features from the strain map. A meta model in the form of a Kriging predictor [3] is constructed to map the damage parameters to Krawtchouk shape features of the strain distribution. The delamination region is quantified using an inverse procedure based on the trained Meta model. Furthermore, a biomimicry algorithm, particle swarm optimisation, is applied to detect the location of the delamination.