• Proceedings of the Korean Society of Precision Engineering Conference
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• 2005.10a
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• pp.379-383
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• 2005

This paper proposes a new steel strip surface inspection system. The system acquires bright and dark field images of defects by using a stroboscopic IR LED light and area camera system and the defect images are preprocessed and segmented in real time for feature extraction. 4113 defect samples of cold roll steel strips are used to develop KNN (k-Nearest Neighbor) classifier which classifies the defects into 8 different types. The developed KNN classifier demonstrates about 85% classifying performance which is considered very plausible result.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.31 no.6 s.261
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• pp.651-658
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• 2007

A new tree-structured neural network classifier is proposed for the automatic real-time inspection of cold-rolled steel strip surface defects. The defects are classified into 3 groups such as area type, disk type, area & line type in the first stage of the tree-structured neural network. The defects are classified in more detail into 11 major defect types which are considered as serious defects in the second stage of neural network. The tree-structured neural network classifier consists of 4 different neural networks and optimum features are selected for each neural network classifier by using SFFS algorithm and correlation test. The developed classifier demonstrates very plausible result which is compatible with commercial products having high world-wide market shares.

• Advances in materials Research
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• v.4 no.4
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• pp.215-226
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• 2015

Experiment on roughness and micro pit defects of SUS 430 ferrite stainless steel was investigated in laboratory. The relation between roughness and glossiness with reduction in height, roll surface roughness, emulsion parameters was analyzed. The surface morphology of micro pit defects was observed by SEM, and the effects of micro pit defects on rolling reduction, roll surface roughness, emulsion parameters, lubrication oil in deformation zone and work roll diameter were discussed. With the increasing of reduction ratio strip surface roughness Ra(s), Rp(s) and Rv(s) were decreasing along rolling and width direction, the drop value in rolling direction was faster than that in width direction. The roughness and glossiness were obtained under emulsion concentration 3% and 6%, temperature $55^{\circ}C$ and $63^{\circ}C$, roll surface roughness $Ra(r)=0.5{\mu}m$, $Ra(r)=0.7{\mu}m$ and $Ra(r)=1.0{\mu}m$. The glossiness was declined rapidly when the micro defects ratio was above 23%. With the pass number increasing, the micro pit defects were reduced, uneven peak was decreased and gently along rolling direction. The micro pit defects were increased with the roll surface roughness increase. The defects ratio was declined with larger gradient at pass number 1 to 3, but gentle slope at pass number 4 to 5. When work roll diameter was small, bite angle was increasing, lubrication oil in micro pit of deformation zone was decreased, micro defects were decreased, and glossiness value on the surface of strip was increased.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.27 no.2
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• pp.327-333
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• 2003

Most of shape defects in steel strip are originated from the structure of rolling mill itself. For instance, strip crown occurs when the work roll is deformed by the bending moment induced on roll chocks. To get rids of the shape defects, it is necessary to increase the stiffness of rolling mill. The structure change of back-up roll is one of alternative ways to increase the mill stiffness without facility revamping from 4 high mill to 6 high mill. In this research work, the new back-up roll was developed and can be used in any type of 4 high mill to reduce the strip shape defects. The developed back-up roll consists of sleeve, arbor and phase angle adjusting system for arbor. The circumference of arbor is specially machined to adapt the strip width change during rolling. The experimental cold rolling test was done to prove the effectiveness of newly developed back-up roll. The experimental rolling results show that the new back-up roll has more powerful performance in reducing the shape defects than conventional back-up roll. It was also found that the new back-up roll has higher stability for shape control. In addition to, the only sleeve surface needs to be reground and changed in most cases, so that the maintenance cost can be greatly reduced.

• Proceedings of the Korean Society for Technology of Plasticity Conference
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• 1997.10a
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• pp.115-118
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• 1997

Recently, hot rolling oil lubrication technology is required to face with the new environments such as the rapid introduction of high wear resistent high speed steel roll the development of continuous hot rolling technology. In the hot strip mill, according to rolling and quality required conditions are constrict, Roll material of hot rolling finishing stand is changing Hi-Cr Roll to High Speed Steel [HSS] Roll. The problem of HSS Roll of roll force and strip scale defects are increasing in hot strip mill, So we have tested HSS Roll in hot rolling simulator as rolling condition, rolling speed, draft, hot oil concentration. To reduce roll force and prevent scale defects. We get some merit rolling force, rolling torque, roll wear reduction, roll and strip surface roughness and hot rolling critical oil concentration 0.4%. Finally we are going to investigate the effect of hot rolling oil of on rolling with HSS Roll.

• Journal of the Korean Society for Precision Engineering
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• v.23 no.8 s.185
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• pp.80-88
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• 2006

This paper proposes a new steel strip surface inspection system. The system acquires bright and dark field images of defects by using a stroboscopic IR LED illuminator and area camera system and the defect images are preprocessed and segmented in real time for feature extraction. 4113 defect samples of hot rolled steel strip are used to develop KNN (k- Nearest Neighbor) classifier which classifies the defects into 8 different types. The developed KNN classifier demonstrates about 85% classifying performance which is considered very plausible result.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2006.05a
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• pp.61-62
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• 2006

A new neural network classifier is proposed for the automatic real-time surface inspection of high-speed cold steel strips having 11 different types of defects. 46 geometrical and gray-level features are extracted for the defect classification. 3241 samples of Posco's Kwangyang steel factory are used for training and testing the neural network classifier. The developed classifier produces plausible 15% error rate which is much better than 20-30% error rate of human vision inspection adopted in most of domestic steel factories.

• Journal of the Korean Society for Precision Engineering
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• v.24 no.4 s.193
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• pp.76-83
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• 2007

A new neural network classifier is proposed for the automatic real-time surface inspection of high-speed cold steel strips having 11 different types of defects. 46 geometrical and gray-level features are extracted for the defect classification. 3241 samples of Posco's Kwangyang steel factory are used for training and testing the neural network classifier. The developed classifier produces plausible 15% error rate which is much better than 20-30% error rate of human vision inspection adopted in most of domestic steel factories.

• Journal of the Korean institute of surface engineering
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• v.34 no.4
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• pp.327-336
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• 2001

The skinpassing conditions such as roll type, roll force and roll roughness of the work roll were evaluated to give the surface properties of the galvanized steel sheets that were required for automotive and to get rid of the surface defects that caused with the bad control of galvanized coating process parameters. The surface defects of the galvanized steel sheets such as the ripple mark and the scratch were completely removed as the roll force of SPM work roll was increased and the amount of the transfer of roll surface texture to the strip was also gained a lot. The image clarity of electro discharge textured (EDT) coated steel sheets before and after painting was higher than that of the bright (BRT) and shot blasted (SBT) coated steel sheets because of higher PPI value, lower waviness and uniform surface pattern. Since micro-craters transferred on the surface of the galvanized steel sheets played a role of nucleation sites of chromate reaction, Increase of micro-craters was bring to better corrosion resistance with the increase of the roll force and the use of EDT roll at the skin pass mill.

• Journal of Institute of Control, Robotics and Systems
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• v.22 no.4
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• pp.247-252
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• 2016

In this paper, we propose a new defect detection technology for micro-defect on the surface of steel products. Due to depth and size of microscopic defect, slop of surface and vibration of strip, the conventional optical method cannot guarantee the detection performance. To solve the above-mentioned problems and increase signal to noise ratio, a novel retro-schlieren method that consists of retro reflector and knife edge is proposed. Moreover dual switching lighting method is also applied to distinguish uneven micro defects and surface noise. In proposed method, defective regions are represented by a black and white pattern. This pattern is detected by a defect detection algorithm with Gabor filter. Experimental results by simulator for sample defects of cold rolled steel show that the proposed method is effective.