Browsing by Author "Brown, Stephen"

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    Establishment of X-ray computed tomography traceability for additively manufactured surface texture evaluation
    (Elsevier, 2021-12-14) Sun, Wenjuan; Giusca, Claudiu; Lou, Shan; Yang, Xiuyuan; Chen, Xiao; Fry, Tony; Jiang, Xiangqian; Wilson, Alan; Brown, Stephen; Boulter, Hal
    Metal additively-manufactured (AM) parts are increasingly used as safety-critical components in industry. Surface textures of metal AM parts are different to conventionally machined surfaces and can directly influence the functional performance of the parts. However, it is difficult or impossible to access and measure non-line-of-sight AM surfaces by conventional measurement techniques. X-ray computed tomography (XCT) is a promising technique that can measure non-line-of-sight surfaces non-destructively. However, the metrology framework for XCT to evaluate surface texture of AM parts is yet to be fully established, and there is a lack of development in surface texture reference standards that fit the purpose. In this paper, we have established a route to calibrate XCT for AM surface texture evaluation using a prototype three-dimensional roughness standard (3DRS) developed by the National Physical Laboratory that has a range of AM surface texture features and was designed for compatibility between 2D (profile), 2½D (areal), and tomography measuring instruments. A measurement protocol has been established between XCT and the contact stylus system, and uncertainty evaluation of 3DRS surface texture was established.
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    Three-spool turbofan pass-off test data analysis using an optimization-based diagnostic technique
    (Sage, 2021-04-15) Saias, Chana Anna; Pellegrini, Alvise; Brown, Stephen; Pachidis, Vassilios
    Production engine pass-off testing is a compulsory technique adopted to ensure that each engine meets the required performance criteria before entering into service. Gas turbine performance analysis greatly supports this process and substantial economic benefits can be achieved if an effective and efficient analysis is attained. This paper presents the use of an integrated method to enable engine health assessment using real pass-off test data of production engines obtained over a year. The proposed method is based on a well-established diagnostic technique enhanced for a highly-complex problem of a three-spool turbofan engine. It makes use of a modified optimization algorithm for the evaluation of the overall engine performance in the presence of component degradation, as well as, sensor noise and bias. The developed method is validated using simulated data extracted from a representative adapted engine performance model. The results demonstrate that the method is successful for 82% of the fault scenarios considered. Next, the pass-off test data are analyzed in two stages. Initially, correlation and trend analyses are conducted using the available measurements to obtain diagnostic information from the raw data. Subsequently, the method is utilized to predict the condition of 264 production turbofan engines undergoing a compulsory pass-off test