• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.06a
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• pp.208-212
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• 1998

This paper suggests the method to recognize the various defect patterns of cold mill strip using binary decision tree constructed by genetic algorithm automatically. In case of classifying the complex the complex patterns with high similarity like the defect patterns of cold mill strip, the selection of the optimal feature set and the structure of recognizer is important for high recognition rate. In this paper genetic algorithm is used to select a subset of the suitable features at each node in binary decision tree. The feature subset of maximum fitness is chosen and the patterns are classified into two classes by linear decision function. After this process is repeated at each node until all the patterns are classified respectively into individual classes. In this way , binary decision tree classifier is constructed automatically. After construction binary decision tree, the final recognizer is accomplished by the learning process of neural network using a set of standard p tterns at each node. In this paper, binary decision tree classifier is applied to recognition of the defect patterns of cold mill strip and the experimental results are given to show the usefulness of the proposed scheme.

• Proceedings of the KIEE Conference
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• 2000.07d
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• pp.3056-3059
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• 2000

This paper describes a fast image labeling algorithm for the feature extraction of connected components. Labeling the connected regions of a digitized image is a fundamental computation in image analysis and machine vision, with a large number of application that can be found in various literature. This algorithm is designed for the surface defect inspection of Cold Mill Strip. The labeling algorithm permits to separate all of the connected components appearing on the Cold Mill Strip.

• Journal of Institute of Control, Robotics and Systems
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• v.12 no.11
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• pp.1156-1161
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• 2006

This paper describes a fast image labeling algorithm for the feature extraction of connected components. Labeling the connected regions of a digitized image is a fundamental computation in image analysis and machine vision, with a large number of application that can be found in various literature. This algorithm is designed for the surface defect inspection of Cold Mill Strip. The labeling algorithm permits to separate all of the connected components appearing on the Cold Mill Strip.

• Proceedings of the Korean Society for Technology of Plasticity Conference
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• 1999.08a
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• pp.142-149
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• 1999

Strip profile and shape control is one of the most important technologies in cold mill, especially for ultra-thin and wide cold strip. The 6-high mills, both of HCMW and UCMW mill, are known to be very effective for the shape controllability. The optimized values of these factors for set-up scheduling were analyzed and found that excellent strip control would be possible by controlling the combination of the influencing factors according to hot coil profile. The important considerations for operation were discussed for individual stand.

• Journal of Institute of Control, Robotics and Systems
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• v.6 no.1
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• pp.98-103
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• 2000

This paper suggests a method to recognize the various defect patterns of a cold mill strip using a binary decision tree automatically constructed by a genetic algorithm(GA). In classifying complex patterns with high similarity like the defect patterns of a cold mill stirp, the selection of an optimal feature set and an appropriate recognizer is important to achieve high recognition rate. In this paper a GA is used to select a subset of the suitable features at each node in the binary decision tree. The feature subset with maximum fitness is chosen and the patterns are classified into two classes using a linear decision function. This process is repeated at each node until all the patterns are classified into individual classes. In this way, the classifier using the binary decision tree is constructed automatically. After constructing the binary decision tree, the final recognizer is accomplished by having neural network learning sits of standard patterns at each node. In this paper, the classifier using the binary decision tree is applied to the recognition of defect patterns of a cold mill strip, and the experimental results are given to demonstrate the usefulness of the proposed scheme.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2009.06a
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• pp.471-472
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• 2009

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.1083-1086
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• 1996

Cold rolling mill process in steel works uses stands of rolls to flatten a strip to a desired thickness. At cold rolling mill process, precalculation determines the mill settings before a strip actually enters the mill and is done by an outdated mathematical model. A corrective neural network model is proposed to improve the accuracy of the roll force prediction. Additional variables to be fed to the network include the chemical composition of the coil, its coiling temperature and the aggregated amount of processed strips of each roll. The network was trained using a standard backpropagation with 4,944 process data collected from no.1 cold rolling mill process from March 1995 through December 1995, then was tested on the unseen 1,586 data from Jan 1996 through April 1996. The combined model reduced the prediction error by 32.8% on average.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.220-220
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• 2000

In this paper, some non-interaction problems in tandem cold rolling mill are considered, which are required to control effectively a thickness of the cold .oiled strip. First, Interstand non-interactive compensate. (Interstand NIC) is designed for decoupling the interaction between stands. Next, a L2 optimization based pre-compensator is designed for decoupling the interaction between roll gap and strip tension in the stand. Finally, the effectness of the proposed decoupling methods is demonstrated via simulations.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.10 no.5
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• pp.96-103
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• 2001

The quality requirements for thickness accuracy in cold rolling continue to become more stringent, particularly in response to exacting design specification from automotive customers. One of the major impacts from the tighter tolerance level is more unusable product on the head end and tail end of tandem mill coils when the mill is in transition to or from steady state rolling condition. A strip thickness control system for a tandem cold steel rolling mills is composed with blocked non-interacting controller and controllers for strip thickness and tension control of each rolling stands. An intelligent mathematical model included an elastic deformation of strip has been developed and applied to the field in order to predict the rolling force. The simulated results showed that the effect of elastic recovery should be included the model, even if the effect of elastic compression was not important.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.39-39
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• 2000

In this paper, a decoupler of tandem cold rolling mill is designed. Before designing the decoupler, this paper improved conventional linear model by considering friction and yield stress of rolling strip. In a stand, the decoupler let an output be controlled by an input. And even if states of other stands should be changed, current stand takes no interference from those changes. In addition, with the same method, a feedforward controller is designed for an input strip thickness error. Finally, performance of controllers above is shown with nonlinear simulation.