Computer vision technology for X-ray testing

Mery, Domingo

doi:10.1784/insi.2014.56.3.147

Cited by 20 publications

(6 citation statements)

References 65 publications

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“…Figure 1 and Eqs. (1) and (2) show that the emitted X-rays are less intense than the incident X-rays (I 0 ), as reported by Cullity and Stock [17]:…”

Section: Fundamental Principles Of X-ray Image Inspectionsupporting

confidence: 52%

“…Computer vision methodologies based on X-rays are used to characterize casting and welding and facilitate the inspection of baggage and cargo [1]. Marrow et al [2] applied digital volume correlation to X-ray computed micro-tomography datasets to measure three-dimensional crack opening displacement and thereby identify the crack opening modes in magnesium alloy.…”

Section: Introductionmentioning

confidence: 99%

“…Micro defects can grow into micro cracks, which expand and converge through repeated stress into macroscopic cracks, leading to fatigue damage and fracturing in the structure [13][14]. X-ray computed tomography has been used to determine the pore fraction and pore distribution in Al and Mg alloy castings [1][2][15][16]. This method can detect defects; however, it cannot be used to measure the percentage of internal defect area (PIDA).…”

Section: Introductionmentioning

confidence: 99%

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Process parameters optimization of magnesium alloy quasi-vacuum casting using X-ray image defect inspection

Huang¹,

Lin²,

Shiou³

et al. 2017

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This study investigated the influence of vacuum casting parameters on defects in magnesium alloy as well as changes in the mechanical properties of these alloys and microstructural evolution under the effects of annealing. We used the Taguchi method in conjunction with an X-ray image detector to determine the percentage of area affected by internal defects, the results of which were verified experimentally. X-ray images of magnesium alloy specimens were filtered, binarized, and edge enhanced. The calculated area of internal defects was then used to optimize process parameters according to multiple performance indicators. Based on the average signal-to-noise ratio, Batch 1 (720 • C, 30 torr, 2 h, steel, and 120 min of argon gas injection) and Batch 8 (780 • C, 30 torr, 1 h, steel, and 120 min of argon gas injection) were both deemed optimal. The uniformity in the percentage of internal defect area observed in samples from Batch 8 is an indication of stability.

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“…Figure 1 and Eqs. (1) and (2) show that the emitted X-rays are less intense than the incident X-rays (I 0 ), as reported by Cullity and Stock [17]:…”

Section: Fundamental Principles Of X-ray Image Inspectionsupporting

confidence: 52%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Process parameters optimization of magnesium alloy quasi-vacuum casting using X-ray image defect inspection

Huang¹,

Lin²,

Shiou³

et al. 2017

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“…Computer vision methodologies based on X-rays are used to characterize casting and welding and to facilitate the inspection of baggage and cargo. 14 Marrow et al 15 applied digital volume correlation to X-ray computed micro-tomography datasets to measure three-dimensional crack opening displacement and thereby identify the crack opening modes in magnesium alloy. X-ray computed tomography has been used to determine the pore fraction and pore distribution in Al and Mg alloy castings.…”

Section: Fundamental Principles Of X-ray Detection Using a Spectrometermentioning

confidence: 99%

“…X-ray computed tomography has been used to determine the pore fraction and pore distribution in Al and Mg alloy castings. [14][15][16][17] This method is able to detect defects; however, it cannot be used to measure the percentage of internal defect area (PIDA). In this study, we used X-ray image detection to determine the PIDA.…”

Section: Introductionmentioning

confidence: 99%

Mechanical behavior enhancement of AZ31/WS₂ and AZ61/WS₂ magnesium metal matrix nanocomposites

Huang

Lin

Huang

et al. 2018

Advances in Mechanical Engineering

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This study conducted tensile and hardness tests to compare the mechanical properties of Mg alloys and metal matrix composites with and without WS 2 or multi-walled carbon nanotubes. We also examined the microscopic structures to investigate their failure characteristics. Our results indicate that AZ61 presented the best mechanical properties following the addition of WS 2 and solution heat treatment, and increasing the content of WS 2 improved the mechanical properties. This performance of this combination even outperformed that of metal matrix composites containing multiwalled carbon nanotubes. This study developed an image processing method, in which X-ray images of the metal matrix composite specimens undergo filtering, binarization, edge enhancement, and morphological calculations to determine the area of internal defects in Mg metal matrix composites. The results indicated reduced percentage of internal defect area values in AZ31-T4 and AZ61-T4 following the addition of 0.2% WS 2 inorganic nanotube (INT). X-ray diffraction results revealed that the solution heat treatment process causes the WS 2 to dissolve into the Mg base, thereby shifting the peak to the right. This improves solid solution strengthening and enhances the mechanical properties. Further observation of the microscopic structures indicated that the AZ61-T4 possesses finer grains than does the AZ31-T4, which is consistent with the existing literature. KeywordsMg metal matrix composites, percentage of internal defect area, X-ray nondestructive testing, imaging, nanocomposites Date

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Dangerous goods detection based on transfer learning in X-ray images

Wei

Liu

2019

Neural Comput & Applic

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Computer vision technology for X-ray testing

Cited by 20 publications

References 65 publications

Process parameters optimization of magnesium alloy quasi-vacuum casting using X-ray image defect inspection

Process parameters optimization of magnesium alloy quasi-vacuum casting using X-ray image defect inspection

Mechanical behavior enhancement of AZ31/WS₂ and AZ61/WS₂ magnesium metal matrix nanocomposites

Dangerous goods detection based on transfer learning in X-ray images

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Computer vision technology for X-ray testing

Cited by 20 publications

References 65 publications

Process parameters optimization of magnesium alloy quasi-vacuum casting using X-ray image defect inspection

Process parameters optimization of magnesium alloy quasi-vacuum casting using X-ray image defect inspection

Mechanical behavior enhancement of AZ31/WS2 and AZ61/WS2 magnesium metal matrix nanocomposites

Dangerous goods detection based on transfer learning in X-ray images

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Mechanical behavior enhancement of AZ31/WS₂ and AZ61/WS₂ magnesium metal matrix nanocomposites