Analysis of the Surface Defects in a Hot-Rolled Low-Carbon C–Mn Steel Plate

Sarkar, Partha Pratim; Dhua, S. K.; Thakur, Suman Kant; Rath, Sushant

doi:10.1007/s11668-017-0281-8

Cited by 21 publications

(7 citation statements)

References 14 publications

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“…The other characteristic feature is that cracks end up in a deer-horn-type branching at the end. This kind of crack branching is reported to be due to the deformation during hot rolling [8]. Observed cracks in most cases consist of two distinctive features; firstly, cracks are all nucleated on V-notches.…”

Section: Microstructure Of Heavy Platesmentioning

confidence: 95%

“…Thus, one of the most important objectives of a hot rolling plant is minimizing heavy plates with surface defects and discontinuities. There are different types of surface defects [5][6][7][8][9][10][11][12][13][14], with many of them having to do with the poor quality of steel during continuous casting [15][16][17]. Other important stages following continuous casting are reheating and hot rolling.…”

Section: Introductionmentioning

confidence: 99%

“…There is always a chance that surface defects after casting are either very small or not detected. These defects will extend and show up in the later stages, i.e., during reheating or rolling [8]. Complete eradication of surface defects in heavy plates necessitates a complete understanding of root causes of all kinds of surface defects.…”

Section: Introductionmentioning

confidence: 99%

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Root Cause Analysis of Surface Cracks in Heavy Steel Plates during the Hot Rolling Process

Bahrami

Khouzani

Mokhtari

et al. 2019

Metals

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This paper investigates the root cause of the formation of surface cracks on hot-rolled C–Mn constructional steel heavy plates. Cracks are rather evenly distributed over the surface in the form of colonies of cracks. Samples were cut from the heavy plate. The microstructure of samples in the as-cast and hot-rolled states were studied using optical and electron microscopes as well as energy dispersive X-ray spectroscopy (EDS). Results show that cracks are heavily oxidized. De-carburized areas are also seen alongside cracks. The crack tip is in the form of a deer-horn, indicating that crack branching has taken place during deformation. The crack initiation sites are V-shaped grooves on the surface of as-cast slabs. Correlations between microstructures, processing parameters, and crack formation are discussed.

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Section: Microstructure Of Heavy Platesmentioning

confidence: 95%

Section: Introductionmentioning

confidence: 99%

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Root Cause Analysis of Surface Cracks in Heavy Steel Plates during the Hot Rolling Process

Bahrami

Khouzani

Mokhtari

et al. 2019

Metals

View full text Add to dashboard Cite

show abstract

“…e point is the information on the surface of the strip can be used. Literature [12] uses the edge of the defect to detect the defect target and analyzes the texture feature through the PHOTO algorithm to determine the defect location, which improves the detection accuracy. However, the system has many detection characteristics and the system response time is slow.…”

Section: Related Workmentioning

confidence: 99%

Steel Plate Defect Recognition of Deep Neural Network Recognition Based on Space-Time Constraints

Zhang

Liu

Wang

2022

Advances in Multimedia

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In order to improve the effect of real-time defect recognition in steel plate online production, this paper studies the method of steel plate defect recognition based on the deep neural network algorithm based on space-time constraints. Moreover, this paper improves the space-time constraint algorithm, optimizes the encryption structure of the traditional ABE scheme, and obtains a neural network feature recognition method based on space-time constraints. In order to process the massive image data stream generated instantaneously and ensure the real-time performance, accuracy, and stability of the detection system, this paper constructs a distributed parallel computing system structure based on the client/server (CC/S) model to obtain an intelligent recognition system. Through experimental research, it can be seen that the deep neural network recognition system based on space-time constraints proposed in this paper has a good effect in the recognition of steel plate defects.

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“…During rolled steel production, hard processes such as cold rolling, etching, annealing and deposition may introduce defects to the steel surface [1][2][3]. Six common types of defects are rolled-in scale (RS), patches (Pa), crazing (Cr), pitted surface (PS), inclusion (In) and scratches (Sc) as shown in figure 1 [4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%

Steel surface defect classification using multi-resolution empirical mode decomposition and LBP

Samsudin

Arof

Harun

et al. 2020

Meas. Sci. Technol.

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In this work we introduce Multi-Resolution Empirical Mode Decomposition (MREMD) as an image decomposition method that simplifies the implementation of Empirical Mode Decomposition (EMD) for bidimensional data. The proposed method is used in conjunction with the local binary pattern (LBP) to classify the images of six types of defects that can be found on the surface of rolled steel. The process starts by performing MREMD on the training images to obtain the first bidimensional intrinsic mode function (BIMF). Then features are extracted from the images and their first BIMF using the LBP. These features are used to train an artificial neural network (ANN) classifier. After training, given an unknown test image containing a defect, MREMD is applied on it to obtain its first BIMF. Next, LBP features are extracted from the image and its first BIMF and these features are fed to the trained ANN classifier to assign the image to one of the six defect classes. The classification process is carried out on 900 test images of the NEU database of six types of surface defects. The approach achieves an overall accuracy that is better than the result obtained using the LBP features alone. The main contribution of this paper is the introduction of multi resolution envelope interpolation using downsampling and upsampling with a fixed window size that reduces the execution time and decrease the sensitivity of the resulting BIMFs to the positions and number of extrema in the input image.

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Analysis of the Surface Defects in a Hot-Rolled Low-Carbon C–Mn Steel Plate

Cited by 21 publications

References 14 publications

Root Cause Analysis of Surface Cracks in Heavy Steel Plates during the Hot Rolling Process

Root Cause Analysis of Surface Cracks in Heavy Steel Plates during the Hot Rolling Process

Steel Plate Defect Recognition of Deep Neural Network Recognition Based on Space-Time Constraints

Steel surface defect classification using multi-resolution empirical mode decomposition and LBP

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