Real-time quantification of damage in structural materials during mechanical testing

Abstract

A novel methodology is introduced for quantifying the severity of damage created during testing in composite components. The method uses digital image correlation combined with image processing techniques to monitor the rate at which the strain field changes during mechanical tests. The methodology is demonstrated using two distinct experimental datasets, a ceramic matrix composite specimen loaded in tension at high temperature and nine polymer matrix composite specimens containing fibre-waviness defects loaded in bending. The changes in the strain field owing to damage creation are shown to be a more effective indicator that the specimen has reached its proportional limit than using load-extension diagrams. The technique also introduces a new approach to using experimental data for creating maps indicating the spatio-temporal distribution of damage in a component. These maps indicate where damage occurs in a component, and provide information about its morphology and its time of occurrence. This presentation format is both easier and faster to interpret than the raw data which, for some tests, can consist of tens of thousands of images. This methodology has the potential to reduce the time taken to interpret large material test datasets while increasing the amount of knowledge that can be extracted from each test.

Quantitative Comparisons of Volumetric Datasets From Experiments and Computational Models

“…Whilst both approaches yield the same coefficients when applied to a dataset, they differ substantially in terms of the computing time required. When decomposing the data for the reinforced rubber matrix on a PC with an Intel ® Core TM i5-8400 CPU and 8GB of RAM, the approach based on equation ( 8) took 1004s whereas the matrix-based approach using equations ( 17) to (21) took less than 0.1s. This disparity stems from two factors.…”

“…The development of the image-based orthogonal decomposition method has led to new approaches for finite element (FE) model updating [16] and quantitative validation of computational mechanics models [18], [19] using displacement or strain fields on the surface of structures. The Euclidean distance between feature vectors describing the strain fields has also been used to develop novel approaches for detecting and monitoring damage in both metallic [20] and composite [21] components.…”

A thermal emissions-based real-time monitoring system for in situ detection of fatigue cracks

“…An image-based decomposition technique based on discrete orthogonal polynomials [26,27] is used for automated tracking of fatigue cracks from the CATE maps. This technique is well-established for quantitative comparisons of data fields [28], including tracking of damage [20,29], and treats strain fields as digital images that are decomposed by fitting a pre-defined set of two-dimensional polynomial kernels to the intensity distribution of the image. The coefficients of the fitted kernels are collated into a column vector, often referred to as a feature vector, which provides a unique representation of the data field in the image.…”

Industrial application of a low-cost structural health monitoring system in large-scale airframe tests

“…DIC has been used for damage detection in laboratory and industrial applications. 1–4 Similarly, infrared (IR) sensors are employed for NDE, with methods including passive and active thermography, and thermoelastic stress analysis (TSA). 5–11…”

“…DIC has been used for damage detection in laboratory and industrial applications. [1][2][3][4] Similarly, infrared (IR) sensors are employed for NDE, with methods including passive and active****#thermography, and thermoelastic stress analysis (TSA). [5][6][7][8][9][10][11] As technology develops, visible and IR sensors are becoming smaller and cheaper.…”