Image Processing Algorithm for Real-Time Crack Inspection in Hole Expansion Test

Choi, Seung-Ho; Kim, Kwangyoon; Lee, Jae-Ho; Park, Sung Hyuk; Lee, Hye-Jin; Yoon, Jonghun

doi:10.1007/s12541-019-00101-4

Cited by 14 publications

(9 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The initial and final inner diameter were measured in the first and the last images in a series of hole expansion tests, and the value of the HER was automatically calculated by a connected computer. An image processing algorithm [16] is utilized for precisely measuring the HER, as illustrated in Figure 6. It consists of image binarization, blob detection, background deletion, ROI selection, image linearization, and crack identification.…”

Section: Machine Vision Analysis For Determining Hermentioning

confidence: 99%

“…However, both measurement results show discrepancies, with difficulty in determining the HER accurately. Choi et al [16] developed a real-time crack detection system with an image processing algorithm in the hole expansion test to precisely capture the images of crack identification. They utilized a vision camera and light-emitting diode (LED) light source to enhance the resolution and proposed a versatile crack inspection algorithm.…”

Section: Introductionmentioning

confidence: 99%

“…Choi et al [16] focused on developing the machine vision system and detecting algorithm for automatic crack detection in hole expansion tests, but it is limited to analyzing the error of incorrect crack identification when the light reflection of the tested material is changed, since the accuracy of the machine vision system is sensitive to changes of light reflection. To overcome this limitation, this paper newly integrated a punch load analysis system into a machine vision system, which makes it possible to detect crack occurrence, although the machine vision system cannot precisely detect the crack due to the excessive light deflection, etc.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Integrated Machine Vision System for Evaluating Hole Expansion Ratio of Advanced High-Strength Steels

et al. 2022

Self Cite

View full text Add to dashboard Cite

In this paper, we propose a new method to estimate the hole expansion ratio (HER) using an integrated analysis system. To precisely measure the HER, three kinds of analysis methods (computer vision, punch load, and acoustic emission) were utilized to detect edge cracks during a hole expansion test. Cracks can be recognized by employing both computer vision and a punch load analysis system to determine the moment of crack initiation. However, the acoustic emission analysis system has difficulty detecting the instant of crack appearance since the magnitude of the audio signal is drowned out by noise from the press, which interrupts the differentiation of crack configuration. To enhance the accuracy for determining the HER, an integrated analysis system that combines computer vision with punch load analysis, and improves on the shortcomings of each analysis system, is newly suggested.

show abstract

Section: Machine Vision Analysis For Determining Hermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Integrated Machine Vision System for Evaluating Hole Expansion Ratio of Advanced High-Strength Steels

et al. 2022

Self Cite

View full text Add to dashboard Cite

show abstract

“…Under these circumstances, it is highly required to fully consider the hardness profile along the sheared edge to with 5M pixels is utilized to precisely evaluate the through-thickness cracks [28].…”

Section: Materials Hardening Datamentioning

confidence: 99%

Artificial Neural Network for Predicting Edge Stretchability in Hole Expansion Test With Gpa-Grade Steel

2020

Self Cite

View full text Add to dashboard Cite

This paper mainly proposes an artificial neural network (ANN) model for predicting edge stretchability of GPa-grade steels, which is substantially difficult to predict due to the complex nonlinear relation among the numerous sheared edge qualities. We newly suggest the physically characterized parameters, such as material properties, deformed shape, and work hardening of sheared edge, to predict the various materials and punching methods, simultaneously. The proposed parameters are trained with the predamage strain which is calculated by inherent fracture strain and experimental results in terms of hole expansion ratio. To prevent the overfitting issues, cross validation method with additional datasets from a different kind of edge stretchability test such as sheared edge tensioning test are utilized. Experimental validations have been conducted with various GPa-grade steels and sheared edge conditions, which are compared with the proposed ANN model and numerical simulation. The proposed ANN model exhibits remarkable performance in the prediction of hole expansion ratio having a mean absolute error of 1.5% when compared to the previous studies such as numerical simulation and ANN model with utilizing the maximum hardness measured at the sheared edge. INDEX TERMS Artificial neural network, edge cracking, edge stretchability, GPa-grade steels, sheared edge quality.

show abstract

“…Manual crack detection methods are utterly dependent on the knowledge and experience of the inspection personnel, and these methods are very subjective, time-consuming, and labor-intensive. In addition, there are other crack detection techniques based on traditional image algorithms, such as edge detection [ 2 , 3 ] and image processing [ 4 , 5 , 6 ]. Although the crack detection methods based on traditional image algorithms have achieved better results than manual crack detection methods, they didn’t consider complex noise, and have shortcomings of low detection accuracy and detection efficiency.…”

Section: Introductionmentioning

confidence: 99%

Automatic Pixel-Level Pavement Crack Recognition Using a Deep Feature Aggregation Segmentation Network with a scSE Attention Mechanism Module

Qiao

Liu

et al. 2021

Sensors

View full text Add to dashboard Cite

Pavement crack detection is essential for safe driving. The traditional manual crack detection method is highly subjective and time-consuming. Hence, an automatic pavement crack detection system is needed to facilitate this progress. However, this is still a challenging task due to the complex topology and large noise interference of crack images. Recently, although deep learning-based technologies have achieved breakthrough progress in crack detection, there are still some challenges, such as large parameters and low detection efficiency. Besides, most deep learning-based crack detection algorithms find it difficult to establish good balance between detection accuracy and detection speed. Inspired by the latest deep learning technology in the field of image processing, this paper proposes a novel crack detection algorithm based on the deep feature aggregation network with the spatial-channel squeeze & excitation (scSE) attention mechanism module, which calls CrackDFANet. Firstly, we cut the collected crack images into 512 × 512 pixel image blocks to establish a crack dataset. Then through iterative optimization on the training and validation sets, we obtained a crack detection model with good robustness. Finally, the CrackDFANet model verified on a total of 3516 images in five datasets with different sizes and containing different noise interferences. Experimental results show that the trained CrackDFANet has strong anti-interference ability, and has better robustness and generalization ability under the interference of light interference, parking line, water stains, plant disturbance, oil stains, and shadow conditions. Furthermore, the CrackDFANet is found to be better than other state-of-the-art algorithms with more accurate detection effect and faster detection speed. Meanwhile, our algorithm model parameters and error rates are significantly reduced.

show abstract

Image Processing Algorithm for Real-Time Crack Inspection in Hole Expansion Test

Cited by 14 publications

References 18 publications

Integrated Machine Vision System for Evaluating Hole Expansion Ratio of Advanced High-Strength Steels

Integrated Machine Vision System for Evaluating Hole Expansion Ratio of Advanced High-Strength Steels

Artificial Neural Network for Predicting Edge Stretchability in Hole Expansion Test With Gpa-Grade Steel

Automatic Pixel-Level Pavement Crack Recognition Using a Deep Feature Aggregation Segmentation Network with a scSE Attention Mechanism Module

Contact Info

Product

Resources

About