Porosity segmentation in X-ray computed tomography scans of metal additively manufactured specimens with machine learning

Gobert, Christian; Kudzal, Andelle; Sietins, Jennifer M.; Mock, Clara; Sun, Jessica; McWilliams, Brandon

doi:10.1016/j.addma.2020.101460

Cited by 30 publications

(23 citation statements)

References 17 publications

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“…Additive manufacturing (AM) technology, which is also known as 3D printing [33], is broadly used in diverse industrial applications with high material and geometric complexities, such as car manufacturing. AM technology can use several techniques including directed energy deposition [31], powder bed fusion [34], binder jetting [31], and additive friction stir deposition [31], to built final or near-net-shape (i.e., initial roughly shaped) parts in a layer-by-layer manner directly from digital files. However, structural defects, such as pores, internal micro-cracks, air bubbles, surface pits, surface scratches, and porosity arrays, are inevitable in current AM processes [31].…”

Section: A Additive Manufacturingmentioning

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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Section: A Additive Manufacturingmentioning

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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“…The deep learning approach used in this study is based on the U-Net architecture developed by Ronnenberger et al [38]. U-Net is a convolution neural network (CNN) that has been successfully applied to phase segmentation in a wide range of fields, such as civil engineering [39], biology [40] and additive manufacturing [41]. A major advantage of U-Net is in providing satisfactory results despite noise, artefacts or overall low quality of the input data set.…”

Section: Model Definitionmentioning

confidence: 99%

Phase segmentation of uncured prepreg X-Ray CT micrographs

Galvez-Hernandez

Gąska

Kratz

2021

Composites Part A: Applied Science and Manufacturing

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“…In general, ML techniques are mainly comprised by supervised ML, unsupervised ML and reinforced ML (Sun et al, 2021). As shown in Figure 3, the general process of supervised ML includes data pre-processing, training set selection, Due to the huge potential of data mining, ML techniques have been widely used in various engineering fields, such as ecological informatics (Fan et al, 2020;Lehikoinen et al, 2019), manufacturing system (Gobert et al, 2020;Tayal et al, 2020), Construction monitoring (Cheng et al, 2020;Choi et al, 2020), material engineering (Marani and Nehdi, 2020;Song et al, 2020) and wind energy (Richmond et al, 2020;Ti et al, 2020;Yin and Zhao, 2019). In recent years, several attempts have been made to utilize ML techniques in wind engineering.…”

Section: Introductionmentioning

confidence: 99%

Machine learning based algorithms for wind pressure prediction of high-rise buildings

Huang

et al. 2022

Advances in Structural Engineering

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In recent years, machine learning (ML) techniques have been used in various fields of engineering practice. In order to evaluate the feasibility of machine learning algorithms for prediction of wind-induced effects on high-rise buildings, four ML algorithms including ridge regression, decision tree, random forest and gradient boosting regression tree are adopted in this study to predict wind pressures on Commonwealth Advisory Aeronautical Research Council standard tall building. The gradient boosting regression tree model is proved to be well performed in predicting both mean wind pressures and fluctuating wind pressures. Compared to expensive wind tunnel tests and time-consuming computational fluid dynamic simulations, it is expected that the gradient boosting regression tree model is an efficient and economical alternative for predicting wind pressures on high-rise buildings.

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Porosity segmentation in X-ray computed tomography scans of metal additively manufactured specimens with machine learning

Cited by 30 publications

References 17 publications

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

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

Phase segmentation of uncured prepreg X-Ray CT micrographs

Machine learning based algorithms for wind pressure prediction of high-rise buildings

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