Full-field Strain Prediction using Mode Shapes Measured with Digital Image Correlation

Document Type

Article

Publication Date

6-1-2019

Publication Title

Measurement

Abstract

Health and condition monitoring of composite structures are critical in engineering especially in the wind, civil, aviation, and auto industries. However, considering the geometry and size of the structures, analyzing critical locations can become challenging. Traditional sensors such as strain-gauges are widely used to collect operating data, but these conventional methods cannot present full-field data and only show the measurement data at a few discrete locations. Baqersad and Bharadwaj have recently developed a Strain Expansion-Reduction Approach (SERA) to bridge this gap and to expand a limited set of measurements and obtain full-field strain data. This approach uses the strain mode shapes from Finite Element Analysis (FEA) to develop a transformation matrix that expands the limited strain data measured using strain-gauges and predicts full-field strain over the entire structure. However, for many structures, it is challenging to accurately model the geometry or material properties for finite element analysis. Many of these structures are made of composite materials and material modes for these structures might not be readily available. In this paper, we use the strain mode shapes extracted using Digital Image Correlation (DIC) in the expansion process. These mode shapes represent actual properties of the structures. The strain mode shapes for a sample structure of a product can be extracted in a test facility using this approach (e.g., a wind turbine blade or a suspension A-arm). An in situ limited set of measurement can be performed using strain-gauges or fiber optic sensors on the structure. Then, the limited data can be expanded using the strain mode shapes to extract full-field strain results. To demonstrate the merit of the approach, we applied the proposed technique to expand real-time operating data measured using a few strain-gauges mounted to a composite spoiler. Using a transformation matrix generated using the DIC operating deflection shapes, the expansion technique predicted the full field strain on the spoiler. It was shown that the proposed methodology could effectively expand the strain data at limited locations to accurately predict the strain at locations where no sensors were placed.

Volume

139

Issue

1

First Page

326

Last Page

333

DOI

https://doi.org/10.1016/j.measurement.2019.03.024

ISSN

0263-2241

Rights

© 2019 Elsevier Ltd.

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