Data Fusion of Probabilistic Full-Field Measurements for Material Characterization

Abstract:

This paper presents a data fusion technique to model more certain probabilistic full-field strain/displacement measurements for stochastic energy-based characterization proposed by the authors. The proposed technique measures the full-field measurements by using multiple cameras, constructing a Gaussian probability density function (PDF) for each camera, fusing the PDFs and developing the total PDF of the full-field measurements. Since the certainty of measurements is magnified by the use of multiple cameras, the use of multiple well-calibrated cameras could achieve the accuracy which no single camera could attain. The validity of the proposed energy-based characterization and its superiority to the original formulation were investigated using numerical analysis of an anisotropic material, and the proposed technique was found to improve the accuracy significantly with the addition of cameras.

• Periodical: Key Engineering Materials (Volumes 462-463)
• Pages: 686-691
• DOI: https://doi.org/10.4028/www.scientific.net/KEM.462-463.686
• Online since: January 2011
• Authors: Jan Wei Pan, Jin Quan Cheng, Tomonari Furukawa
• Keywords: Data Fusion, Material Characterisation, Probabilistic Full-Field Measurement

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