Defect detection in CT scans of cast aluminum parts: A machine vision perspective

Fuchs, Patrick; Kröger, Thorben; Garbe, Christoph S.

doi:10.1016/j.neucom.2021.04.094

Cited by 49 publications

(29 citation statements)

References 30 publications

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“…V-Net share a lot of similarities with U-Net (skip connections and U shape), the difference between them is that V-Net uses instead of max-pooling layer, also convolutional layers and residual blocks [7]. Also in the field of NDT several publication have shown promising results using CNNs for the inspection of XCT datasets [9,29,30]. Firstly, the modification of the 3D U-Net architecture to segment both pores and fibres in carbon and glass fibre-reinforced material was presented in 2020 by Yosifov [9].…”

Section: Convolutional Neural Networkmentioning

confidence: 99%

Defect detectability analysis via Probability of defect detection between traditional and deep learning methods in numerical simulations

Yosifov¹,

Weinberger²,

Reiter³

et al. 2023

eJNDT

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X-ray computed tomography (XCT) is one of the most powerful imaging techniques in non-destructive testing (NDT) for detecting, analysing and visualising defects such as pores, fibres, cracks etc. in industrial specimens. Detecting defects in X-ray images, however, is still a challenging problem, as it strongly depends on the quality of the XCT images. Numerical XCT simulation proved to be valuable in order to increase both image quality and detection performance. In this work, we thus analyse the differences between traditional segmentation techniques (i.e., k-means, watershed, Otsu thresholding) and deep learning-based methods (i.e., U-Net, V-Net, modified 3D U-Net) in terms of their defect detection capacity using virtual XCT images. For this purpose, we apply the probability of defect detection (POD) approach on simulated X-ray computed tomography data from aluminium cylinder heads. The XCT simulation tool SimCT was used to generate X-ray radiographs and respective reconstructions from a specimen series which features different well-defined defects with varying sizes, shapes and locations. To generate POD curves and to specify detection limits, the segmentation algorithms are used in predefined regions for defect detection via a hit/miss approach. A comparison and visualisation of six different types of defects is illustrated in 2D and 3D images, together with their POD curves and detection limits.

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Section: Convolutional Neural Networkmentioning

confidence: 99%

Defect detectability analysis via Probability of defect detection between traditional and deep learning methods in numerical simulations

Yosifov¹,

Weinberger²,

Reiter³

et al. 2023

eJNDT

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Section: B Castingmentioning

confidence: 99%

“…Semantic segmentation methods for casting inspection were assessed in [45], [46]. Authors in [45] used only realistically simulated X-ray data to train a network to perform semantic segmentation on cast aluminum parts. Large defect scale variation, small inter-class differences, and annotation uncertainty issues were tackled in [46] for defect semantic segmentation.…”

Section: B Castingmentioning

confidence: 99%

Computer Vision on X-Ray Data in Industrial Production and Security Applications: A Comprehensive Survey

Rafiei

Raitoharju²,

Iosifidis

2023

IEEE Access

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X-ray imaging technology has been used for decades in clinical tasks to reveal the internal condition of different organs, and in recent years, it has become more common in other areas such as industry, security, and geography. The recent development of computer vision and machine learning techniques has also made it easier to automatically process X-ray images and several machine learningbased object (anomaly) detection, classification, and segmentation methods have been recently employed in X-ray image analysis. Due to the high potential of deep learning in related image processing applications, it has been used in most of the studies. This survey reviews the recent research on using computer vision and machine learning for X-ray analysis in industrial production and security applications and covers the applications, techniques, evaluation metrics, datasets, and performance comparison of those techniques on publicly available datasets. We also highlight some drawbacks in the published research and give recommendations for future research in computer vision-based X-ray analysis.

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“…for in-line XCT inspections of industrial specimens. For this purpose, Fuchs et al [11] use XCT simulation for generating a meaningful synthetic ground truth for defect detection. Generally, XCT simulation tools are rather diverse and can be classified into two types.…”

Section: Xct Simulationmentioning

confidence: 99%

“…Before the simulation process was started, the computed transformations (tilt and shift) were applied in SimCT for all geometry files (8). Simulated scans were created of the specimen with 100 variations of differing defects (11), and 100 scans of the specimen without any defects were simulated with differences in the physical effects (10). Reconstructions were automatically computed by CERA per volume stack.…”

Section: Processing and Data Characteristicsmentioning

confidence: 99%

Probability of Detection applied to X-ray inspection using numerical simulations

Yosifov¹,

Reiter²,

Rendl³

et al. 2022

eJNDT

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In this work, we apply and adapt established Probability of Detection (POD) methods on inline inspection of aluminium cylinder heads using X-ray computed tomography. The CT simulation tool SimCT [4] is used to acquire virtual images of the specimens including artificial defects, which avoids the manufacturing of calibrated defects of known type (e.g., pore, inclusion, crack etc.), size and location. One of the exemplary defects is discussed as representative result together with the generated POD curves as well as its characteristics (i.e., the minimum detected defect, the maximum missed defect, POD(a90) =0.90 and a90/95).

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Defect detection in CT scans of cast aluminum parts: A machine vision perspective

Cited by 49 publications

References 30 publications

Defect detectability analysis via Probability of defect detection between traditional and deep learning methods in numerical simulations

Defect detectability analysis via Probability of defect detection between traditional and deep learning methods in numerical simulations

Computer Vision on X-Ray Data in Industrial Production and Security Applications: A Comprehensive Survey

Probability of Detection applied to X-ray inspection using numerical simulations

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