Simulation-Based Analysis of Complex Radiographic Images
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Simulation-Based Analysis of Complex Radiographic Images Nick Brierley1 Received: 18 May 2018 / Accepted: 18 June 2020 © Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract The paper describes an algorithm developed to enhance the analysis (manual or automatic) of radiographic images acquired for samples of complex geometry (such as those enabled by additive manufacturing techniques), where the imprint of the sample’s geometric complexity in the radiograph is likely to undermine the ability to identify defect indications. The underlying premise is that, assuming the sample geometry is known (at least the CAD specification), a simulation of the experimental radiograph can be used to essentially subtract out the geometric complexity from an experimental image, revealing the deviations from the expected inspection output. The approach is especially relevant when the uniqueness of the sample (for example due to personal customisation) limits the availability of comparable experimental data. However, in practice, this technique requires the simulation to be accurately calibrated to the experimental configuration, necessitating the use of a numerical optimisation to fit the simulation parameters. As a by-product, the parameters of an imperfectly specified experimental set-up are recovered. The algorithm architecture described can operate on multiple input radiographs simultaneously, and is readily adaptable to other image-based inspection modalities. Results for several test inputs are presented, starting with synthetic test cases and ending with a set of three experimental radiographs. The results are convincing, as a difference image enables a substantial reduction in image bit-depth, making deviations of interest more apparent and demonstrating the value of the approach. Keywords Radiography · Simulation · Optimisation · Image processing
1 Introduction Some of the latest manufacturing technologies, such as additive manufacturing (AM), enable both the creation of extremely complex geometries and cost-effective part customisation [12]. The highly complex components created present a significant inspection challenge, both in the data acquisition and in the data analysis. The non-contact imaging provided by radiographic inspection modalities are frequently the only viable options for a detailed volumetric inspection [18]—but the obtained images can be very challenging to interpret due to the imprint of the sample’s geometric complexity, which can easily obfuscate defect indications. One of the reasons X-ray computed tomography (XCT) is frequently used in practice at present in such circumstances is that the sample geometry is easier to understand in the output than in 2D radiographic imag-
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Nick Brierley [email protected] Metrology & Non-Destructive Testing, The Manufacturing Technology Centre, Pilot Way, Ansty Business Park, Coventry CV7 9JU, UK
ing [5]. However, XCT has numerous drawbacks over 2D radiography, most obviously the associated time/cost penalty incurred. The work prese
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