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Model-based Optimization of Setup Parameters for Dimensional Measurements on Monomaterial and Multimaterial Workpieces in Industrial Computed Tomography

ISBN: 978-3-86359-643-9

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Kurzübersicht

This manuscript presents a methodology for optimizing setup parameters for dimensional measurements in industrial computed tomography (CT). The methodology relates the influence of setup parameters on measurement uncertainty to five conditions on scan quality. A set of models for optimizing setup parameters was developed and implemented into a software application. Experimental investigations showed that predicted setup parameters are optimal for the vast majority of the considered features.

Model-based Optimization of Setup Parameters for Dimensional Measurements on Monomaterial and Multimaterial Workpieces in Industrial Computed Tomography

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Computed tomography (CT) has the potential for overcoming the limitations of conventional coordinate measuring techniques. Due to the complexity of the CT measurement chain, there is currently no established model for determining 1.) the uncertainty of measurements for a given measurement task and 2.) the optimal setup parameters. CT users choose suitable setup parameters according to their experience and intuition, leading to high variability in the measurement outcome.
This manuscript presents a systematic methodology for determining optimal CT setup parameters for dimensional measurements on monomaterial and multimaterial workpieces. The methodology is based on the hypothesis that the contribution of setup parameters to measurement uncertainty is minimized when five conditions on scan quality are achieved: maximized image contrast, maximized image signal, minimized image noise, minimized blurring, and minimized CT artifacts. Based on this hypothesis, a set of models for optimizing workpiece placement, imaging and the number of projections was developed.
The optimization models were implemented into a software application. The validation of the models occurred by falsifying the rival hypothesis, i.e. by showing that there is no other parameter set that induces a lower contribution to measurement uncertainty. Experimental investigations were carried out to determine the influence of the setup parameters on the variance of the measurement values. Experimental results showed that predicted setup parameters are optimal for the vast majority of the considered features. A performance comparison between the software application and an expert user was also carried out. Results showed that the software application performed significantly better than the expert user.

Zusatzinformation

Autor Buratti, Andrea
ISBN/Artikelnr. 978-3-86359-643-9
Gewicht 0.256 kg
Erscheinungsdatum 05.09.2018
Lieferzeit 3-4 Tage
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