The computational enhancement of automated non-destructive inspection

Brierley, Nick; Tippetts, Trevor; Cawley, P.

doi:10.1784/insi.2014.56.11.599

Cited by 2 publications

(3 citation statements)

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“…Rather, the major challenge concerns the decision of which p -values to fuse, given that the originating data channels are described in terms of aligned coordinate axes (post-registration) but different coordinates. Essentially, in each channel physical space is discretized differently, and in a somewhat arbitrary manner, such that determining associations between resels requires some approximation [ 39 ].…”

Section: Resultsmentioning

confidence: 99%

“…While there are parallels to medical imaging in this work, especially in terms of the dimensionality and potential multi-modality of the data [ 34 ], NDE test subject variability is low and the types of possible distortions limited compared with the human body [ 35 , 36 ]. The system developed and adopted by the authors is described in detail in [ 37 – 39 ]. Key features of this registration framework include a physical model of the data acquisition that attempts to describe all conceivable benign distortions of the data and the use of a multi-objective optimization [ 40 ], allowing alignment and hence positional uncertainty to be assessed, with consequences for the later data fusion.…”

Section: Data Fusion Contextmentioning

confidence: 99%

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Data fusion for automated non-destructive inspection

2014

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In industrial non-destructive evaluation (NDE), it is increasingly common for data acquisition to be automated, driving a recent substantial increase in the availability of data. The collected data need to be analysed, typically necessitating the painstaking manual labour of a skilled operator. Moreover, in automated NDE a region of an inspected component is typically interrogated several times, be it within a single data channel due to multiple probe passes, across several channels acquired simultaneously or over the course of repeated inspections. The systematic combination of these diverse readings is recognized to offer an opportunity to improve the reliability of the inspection, but is not achievable in a manual analysis. This paper describes a data-fusion-based software framework providing a partial automation capability, allowing component regions to be declared defect-free to a very high probability while readily identifying defect indications, thereby optimizing the use of the operator's time. The system is designed to applicable to a wide range of automated NDE scenarios, but the processing is exemplified using the industrial ultrasonic immersion inspection of aerospace turbine discs. Results obtained for industrial datasets demonstrate an orders-of-magnitude reduction in false-call rates, for a given probability of detection, achievable using the developed software system.

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Section: Resultsmentioning

confidence: 99%

Section: Data Fusion Contextmentioning

confidence: 99%

Data fusion for automated non-destructive inspection

2014

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“…The framework architecture is based on that developed in [ 18 ]. This lends itself well to the development of an optimization framework thanks to the inherently flexible and extensible design, as well as the incorporation of tools for efficient computation.…”

Section: Methods Overviewmentioning

confidence: 99%

Optimized multi-shot imaging inspection design

2018

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The inspection of complex-shaped components, such as those enabled by additive manufacturing, is a major challenge in industrial quality assurance. A frequently adopted approach to volumetric non-destructive evaluation is X-ray computed tomography, but this has major drawbacks. Two-dimensional radiography can overcome some of these problems, but does not generally provide an inspection that is as capable. Moreover, designing a detailed inspection for a complex-shaped component is a labour-intensive task, requiring significant expert input. In response, a computational framework for optimizing the data acquisition for an image-based inspection modality has been devised. The initial objective is to advance the capabilities of radiography, but the algorithm is, in principle, also applicable to alternative types of imaging. The algorithm exploits available prior information about the inspection and simulations of the inspection modality to allow the determination of the optimal inspection configuration, including specifically component poses with respect to the imaging system. As an intermediate output, spatial maps of inspection performance are computed, for understanding spatially varying limits of detection. Key areas of innovation concern the defect detectability evaluation for arbitrarily complex indications and the creation of an application-specific optimization algorithm. Initial trials of the algorithm are presented, with good results.

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The computational enhancement of automated non-destructive inspection

Cited by 2 publications

References 0 publications

Data fusion for automated non-destructive inspection

Data fusion for automated non-destructive inspection

Optimized multi-shot imaging inspection design

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