• 한국정밀공학회지
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• 제23권8호
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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.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2005년도 추계학술대회 논문집
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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.

• 제어로봇시스템학회논문지
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• 제22권4호
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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.

• 대한기계학회논문집A
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• 제31권6호
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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.

• 한국소성가공학회:학술대회논문집
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• 한국소성가공학회 2009년도 제7회 압연 심포지엄
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• pp.73-76
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• 2009

Increase in tensile strength of steel is important for fuel efficiency and $CO_2$ reduction. But the higher the strip strength, the more defect could be generated in hot strip mill. This study focuses on line-type edge scab. One of the causes for the defect is initial edge cracks commonly observed on a slab but their correlation has not been verified yet. Thus, the objective of this report is to verify if the edge crack is exactly the seed for edge scab. For this, we conducted pilot hot rolling test with cracked slab and compared the development of cracks and edge scabs on hot-rolled strip.

• 한국컴퓨터정보학회논문지
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• 제25권3호
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• pp.19-26
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• 2020

일반적으로 품질 관리는 많은 제조 공정, 특히 주조 또는 용접과 관련된 공정의 기본 구성 요소가 된다. 그러나 사람이 일일이 수동으로 품질 관리 절차를 하는 것은 종종 시간이 걸리고 오류가 발생하기 쉽다. 최근 고품질 제품에 대한 요구를 만족시키기 위해 지능형 육안 검사 시스템의 사용이 생산 라인에서 필수적이 되고 있다. 본 논문에서는 이를 위해 딥 러닝 기반의 ShuffleDefectNet 결함 감지 시스템을 제안하고자 한다. 제안된 결함 검출 시스템은 NEU 데이터 세트의 결함 검출에 대한 여러 최신 성능들보다 높은 평균 정확도 99.75% 정도를 얻는다. 이 논문에서 여러 다른 트레이닝 데이터로부터 최상의 성능을 탐지하고 탐지 성능을 관찰하였다. 그 결과 ShuffleDefectNet의 전체 아키텍처를 사용할 때 정확성과 속도가 크게 향상됨을 알 수 있었다.

• 소성∙가공
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• 제11권2호
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• pp.147-154
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• 2002

The tension levelling Process is Performed to elongate the strip plastically In combination of tensile and bending strain so that all longitudinal fibers In the strip have an approximately equal amount ofn length and undesirable strip shapes are corrected to the flat shape. Thus paper is concerned with a simulation of the tension levelling process based on the analysis of tile unit model for the tension leveller. Analysis technique such as the sequential analysis of the unit model is suggested and verified with the assembly analysis of the unit model for the effective arts economic analysis of the full set of the tension leveller. Analysis of the full tension levelling Process using sequential unit models Is carried out for steel strips with the shape defect and provides the effect of the intermesh and optimum amount of the intermesh in tension levelling process.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2006년도 춘계학술대회 논문집
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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.

• 한국정밀공학회지
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• 제24권4호
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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.

• 대한기계학회논문집
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• 제16권4호
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• pp.819-827
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• 1992

본 연구에서는 전술한 바와 같은 반복되는 압연부하에 의한 롤의 피로특성을 규명하기 위해 열연공장의 조압연기중 표본작업 롤의 피로특성을 규명하기 위해 열연 공장의 조압연기중 표본 작업 롤을 선정하고 일정기간의 사용이력을 조사하였으며, 이 를 토대로 롤내부 온도 및 열응력 계산을 수행하여 압연중 발생되는 피로 균열의 크기 를 정량화 시켜보았다. 또 표면 균열관찰 실험 및 롤 표면흠 대응 실험을 통해 피로 균열이 압연판의 표면품질에 어떠한 영향을 미치는지 조사해보았다.