Determination of BGA structural defects and solder joint defects by 3D X-ray laminography

Moore, Thomas; Vanderstraeten, Daniel; Forssell, Pirkko

doi:10.1109/ipfa.2001.941474

Cited by 12 publications

(2 citation statements)

References 10 publications

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“…s(x, y) = σ xy + c 3 σ x σ y + c 3 (16) l(x,y) is the brightness component, c(x,y) is a contrast component, s(x,y) is the structural component. µ x and µ y represent the means of x, y, σ x and σ y represent the standard deviation of x, y, respectively.…”

Section: Reconstruction Quality Assessmentmentioning

confidence: 99%

A novel design of industrial real-time CT system based on sparse-view reconstruction and deep-learning image enhancement

Zheng¹,

Wang²,

Yuan³

et al. 2022

Preprint

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Industrial CT is useful for defect detection, dimensional inspection and geometric analysis. While it does not meet the needs of industrial mass production, because of its time-consuming imaging procedure. This article proposes a novel stationary real-time CT system with multiple X-ray sources and detectors, which is able to refresh the CT reconstructed slices to the detector frame frequency. This kind of structure avoids the movement of the X-ray sources and detectors. Projections from different angles can be acquired with the objects' translation, which makes it easier to be integrated into pipeline. All the detectors are arranged along the conveyor, and observe the objects in different angle of view. With the translation of objects, their X-ray projections are obtained for CT reconstruction. To decrease the mechanical size and reduce the number of X-ray sources and detectors, the FBP reconstruction algorithm was combined with deep-learning image enhancement. Medical CT images were applied to train the deep-learning network for its quantity advantage in comparison with industrial ones. It is the first time to adopt this source-detector layout strategy. Data augmentation and regularization were used to elevate the generalization of the network. Time consumption of the CT imaging process was also calculated to prove its high efficiency. It is an innovative design for the 4th industrial revolution, providing an intelligent quality inspection solution for digital production. Keywords non-destructive testing • defect inspection • deep-learning • automated production line • real-time CT • parallel computing

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Section: Reconstruction Quality Assessmentmentioning

confidence: 99%

A novel design of industrial real-time CT system based on sparse-view reconstruction and deep-learning image enhancement

Zheng¹,

Wang²,

Yuan³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…X-ray tomography has been a typical technology in the non-destructive testing area. Detailed studies of classical tomography development can be found elsewhere [17][18][19][20][21][22]. Over the past half century, with further developments in electronics and computers, X-ray computed tomography (CT) achieved rapid development.…”

Section: Introductionmentioning

confidence: 99%

Novel Design of Industrial Real-Time CT System Based on Sparse-View Reconstruction and Deep-Learning Image Enhancement

Zheng

Wang

2023

Electronics

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Industrial CT is useful for defect detection, dimensional inspection and geometric analysis, while it does not meet the needs of industrial mass production because of its time-consuming imaging procedure. This article proposes a novel stationary real-time CT system, which is able to refresh the CT-reconstructed slices to the detector frame frequency. This structure avoids the movement of the X-ray sources and detectors. Projections from different angles can be acquired with the objects’ translation, making it easier to be integrated into production line. All the detectors are arranged along the conveyor and observe the objects in different angles of view. With the translation of objects, their X-ray projections are obtained for CT reconstruction. To decrease the mechanical size and reduce the number of X-ray sources and detectors, the FBP reconstruction algorithm was combined with deep-learning image enhancement. Medical CT images were applied to train the deep-learning network for its quantity advantage in comparison with industrial ones. It is the first time this source-detector layout strategy has been adopted. Data augmentation and regularization were used to elevate the generalization of the network. Time consumption of the CT imaging process was also calculated to prove its high efficiency. Our experiment shows that the reconstruction resulting in undersampled projections is highly enhanced using a deep-learning neural network which meets the demand of non-destructive testing. Meanwhile, our proposed system structure can perform quick scans and reconstructions on larger objects. It solves the pain points of limited scan size and slow scanning speed of existing industrial CT scans.

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A novel defect detection technique using active transient thermography for high density package and interconnections

Chai

Wongi²,

Bai³

et al.

53rd Electronic Components and Technology Conference, 2003. Proceedings.

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Determination of BGA structural defects and solder joint defects by 3D X-ray laminography

Cited by 12 publications

References 10 publications

A novel design of industrial real-time CT system based on sparse-view reconstruction and deep-learning image enhancement

A novel design of industrial real-time CT system based on sparse-view reconstruction and deep-learning image enhancement

Novel Design of Industrial Real-Time CT System Based on Sparse-View Reconstruction and Deep-Learning Image Enhancement

A novel defect detection technique using active transient thermography for high density package and interconnections

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