Delamination detection and quantification in composites using co-Kriging with Lamb wave data

Abstract

Delamination is a typical form of damage in laminated composites that must be detected and quantified early to prevent severe consequences, such as total structural failure. However, experimental testing to investigate this kind of damage mechanism can be expensive and, in some cases, unfeasible. The main contribution of this paper is to detect and quantify delamination damage using a cost-effective regression technique called co-Kriging, which combines two datasets of distinct natures. By leveraging a second, simpler, and more affordable co-variable, co-Kriging improves the prediction accuracy of a target variable while reducing data acquisition costs. The method integrates Lamb wave datasets from low-cost numerical simulations with experimental laminate tests. Based on a simplified plate theory model, the numerical data includes a basic delamination model to capture its influence on signal features without requiring a complex or fully detailed model. This numerical data is then integrated with experimental results from a laminate with piezoceramics used for actuation and sensing. The combined datasets are utilized to develop a surrogate model that provides more accurate predictions of the size of the delamination area. The results showcased that co-Kriging, by fusing low-cost numerical data with experimental information, significantly enhances the ability to detect and quantify early damage in composite structures.