Metal-Polymer Assembly Dimensional Evaluation by X-Ray Computed Tomography: An Experimental Approach Through Relative Intensity Intercomparison
Source abstract
Abstract
Abstract Accuracy of metrological inspection by X-ray computed tomography (XCT) relies on a good adjustment of evaluation settings. This can be challenging in multi material objects, especially if the differences of density are high. A good indicator of the attenuation of X-rays is the relative intensity (I/I 0 ): the difference between the beam energy emitted by the tube and received by the detector; however, it is not clear if it could be used alone for generalization. In this paper, an analysis of the attenuation ratio, represented by relative intensity, and its usage to define the expected quality variation of XCT measurements of metal-polymer assemblies is presented. An ad hoc test object has been designed including a polymeric base, interior polymeric cylinders and several outer metallic cylinders with two purposes: (i) obtain similar relative intensity in all projections, and (ii) create different scenarios with a range of I/I 0 values. Experimental results confirm the trend observed in simulations, as better quality of the measurements in terms of metrological data and contrast-to-noise ratio (CNR) is directly related to higher I/I 0 values. The threshold of I/I 0 ≈ 0.16 has been found to be determinant for dimensional evaluation, as in presence of elements with higher radiopacity, lower– density features could present non– acceptable errors in their measurements. As well, it has been found that same attenuation values do not work similarly on different materials, as higher attenuation coefficient materials (in this case, steel vs. aluminium) create bigger noise levels (in the form of scatter). These findings will help to predict more easily the expected results on metal– polymer assemblies’ evaluation by XCT, being able to estimate more precisely the errors on dimensional measurements.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Advanced X-ray and CT Imaging · Manufacturing Process and Optimization · Machine Learning in Materials Science
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (47)
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