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

Yosifov, M.; Reiter, Michael; Heupl, Sarah; Gusenbauer, Christian; Fröhler, Bernhard; Gutiérrez, Ricardo; Beenhouwer, Jan De; Sijbers, Jan; Kastner, Johann; Heinzl, Christoph

doi:10.1080/10589759.2022.2071892

Cited by 14 publications

(10 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Human in the loop means here that the network makes a prediction, which is then corrected by a human expert, until the quality of the prediction reaches a predefined threshold. Another important point is the use of additional measures to quantify the results of the prediction better, in similar directions as Yosifov et al [12] or Weissenböck et al [13]. Especially the shape and volume of the pores are important for aerospace components.…”

Section: Discussionmentioning

confidence: 99%

Usage of 3D U-net Convolutional Neural Network for the inspection of aerospace components

Yosifov¹,

Weinberger²,

Fröhler³

et al. 2023

eJNDT

View full text Add to dashboard Cite

This work illustrates the use of deep learning methods applied on X-ray computed tomography (XCT) datasets to segment pores and fibres in reinforced composite components from the aeronautic industry by binary semantic segmentation. We first apply data pre-processing, and then employ a modified 3D U-Net, representing a convolutional neural network. Tweaking hyper-parameters, we have reached an optimal model for our datasets. One of the models has reached 99% segmentation accuracy when testing using a Dice function. In our experiments, pores and fibres in XCT datasets of aerospace components, more specifically of glass and carbon fibre reinforced composites, were segmented and analysed. In order to compare this modified 3D U-Net architecture with segmentation methods currently used in the industry, the datasets were also input to conventional Otsu thresholding. Our results shows that modified 3D U-Net performs better than Otsu thresholding, especially on the segmentation of small pores. Modified 3D U-Net also showed reasonable prediction accuracy when testing with an optimised model which was trained with a low number of dataset both for fibre and pore segmentation.

show abstract

Section: Discussionmentioning

confidence: 99%

Usage of 3D U-net Convolutional Neural Network for the inspection of aerospace components

Yosifov¹,

Weinberger²,

Fröhler³

et al. 2023

eJNDT

View full text Add to dashboard Cite

show abstract

“…To generate realistic data using XCT simulation, optimised parameters were applied, which are listed in Table 1. The detailed XCT data generation process and analysis setup were presented in our previous work [12].…”

Section: Generation Of Virtual Xct Data Using Simulationmentioning

confidence: 99%

“…The specimen including the defects as explained were simulated via SimCT considering the following physical effects: focal spot blur, a modulation transfer function, image noise and scattering [23]. A more detailed discussion on the used parameters is published in our previous work [12].…”

Section: Generation Of Virtual Xct Data Using Simulationmentioning

confidence: 99%

“…Generalised linear models (GLM) [35] are continuous link functions with model parameters providing a matrix of probabilities for the binary response of success/failure scenarios. The Probit linking function [12] is used for binary regression in order to create POD curves in this experiment.…”

Section: Probability Of Detection Concept and Hit/miss Modelmentioning

confidence: 99%

“…An updated version of 3D U-Net (M-3DUnet) aimed at improving the detection in material science was presented in a recent work [9], extending open_iA [10] to use pre-trained networks of deep learning methods. In our previous work, we focused on applying the Zeiss automated defect detection (ZADD) [11] for global defect detection for the generation of respective POD curves [12]. In this paper, we investigate various traditional and deep learning methods for the segmentation of pores and defects locally (using sub-volumes for segmentation).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

Yosifov¹,

Weinberger²,

Reiter³

et al. 2023

eJNDT

View full text Add to dashboard Cite

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.

show abstract

AI-Driven Synthetization Pipeline of Realistic 3D-CT Data for Industrial Defect Segmentation

Tenscher-Philipp,

Schanz,

Harlacher

et al. 2024

J Nondestruct Eval

View full text Add to dashboard Cite

Training data is crucial for any artificial intelligence model. Previous research has shown that various methods can be used to enhance and improve AI training data. Taking a step beyond previous research, this paper presents a method that uses AI techniques to generate CT training data, especially realistic, artificial, industrial 3D voxel data. This includes that material as well as realistic internal defects, like pores, are artificially generated. To automate the processes, the creation of the data is implemented in a 3D Data Generation, called SPARC (Synthetized Process Artificial Realistic CT data). The SPARC is built as a pipeline consisting of several steps where different types of AI fulfill different tasks in the process of generating synthetic data. One AI generates geometrically realistic internal defects. Another AI is used to generate a realistic 3D voxel representation. This involves a conversion from STL to voxel data and generating the gray values accordingly. By combining the different AI methods, the SPARC pipeline can generate realistic 3D voxel data with internal defects, addressing the lack of data for various applications. The data generated by SPARC achieved a structural similarity of 98% compared to the real data. Realistic 3D voxel training data can thus be generated. For future AI applications, annotations of various features can be created to be used in both supervised and unsupervised training.

show abstract

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

Cited by 14 publications

References 18 publications

Usage of 3D U-net Convolutional Neural Network for the inspection of aerospace components

Usage of 3D U-net Convolutional Neural Network for the inspection of aerospace components

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

AI-Driven Synthetization Pipeline of Realistic 3D-CT Data for Industrial Defect Segmentation

Contact Info

Product

Resources

About