• 제어로봇시스템학회논문지
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• 제15권10호
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• pp.1062-1070
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• 2009

The strip shape for the stainless steel process has made an issue of the strip quality, and hence the shape control method is developed at the Sendzimir rolling mill (ZRM). ZRM is a stainless cold rolling mill and has actuators for the shape control. They are first intermediate rolls and top crown rolls, which are controlled horizontally and vertically, respectively. The shape control of the stainless steel rolling process has difficulty in obtaining the symmetrical shape. The objective of the shape control is to minimize the shape deviation and to maintain stable state, which keeps symmetrical shape pattern in the lateral direction. The method of the shape recognition employs a least squares method and neural network. The shape deviation is the difference between the target shape and actual shape and is controlled by the fuzzy shape control. The fuzzy shape control using operator's informative knowledge is proposed in this paper. The experiments are carried out online for various stainless materials and sizes. The productivity of the rolling process has increased from 9.0 to 9.4 tons per hour.

• 한국공작기계학회:학술대회논문집
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• 한국공작기계학회 2003년도 추계학술대회
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• pp.368-373
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• 2003

A major concern at present is the simultaneous control of transverse thickness profile and flatness in the finishing stages of hot rolling process. The mathematical modeling of hot rolling process has long been recognized to be a desirable approach to investigate rolling operating practice and the design of mill equipment to improve productivity and quality. However, many factors make the mathematical analysis of the rolling process very complex and time-consuming. In order to overcome these problems and to obtain an accurate rolling force, the predicted model of rolling force using neural networks has widely been employed. In this paper, Radial Basis Function Network(RBFN) is applied to improve the accuracy of rolling force prediction in hot rolling mill. In order to verify and analysis the performance of applied neural network, the comparison with the measured rolling force and the predicted results using two different neural networks - RBFN, MLP, has respectively been carried out. The results obtained using RBFN neural network are much more accurate those obtained the MLP.

• 한국공작기계학회논문집
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• 제13권6호
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• pp.29-33
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• 2004

A major concern at present is the simultaneous control of transverse thickness profile and flatness in the finishing stages of hot rolling process. The mathematical modeling of hot rolling process has long been recognized to be a desirable approach to investigate rolling operating practice and the design of mill equipment to improve productivity and quality. However, many factors make the mathematical analysis of the rolling process very complex and time-consuming. In order to overcome these problems and to obtain an accurate rolling force, the predicted model of rolling force using neural networks has widely been employed. In this paper, Radial Basis Function Network(RBFN) is applied to improve the accuracy of rolling force prediction in hot rolling mill. In order to verify and analyze the performance of applied neural network the comparison with the measured rolling force and the predicted results using two different neural networks-RBFN, MLP, has respectively been carried out. The results obtained using RBFN neural network are much more accurate those obtained the MLP.

• 한국소성가공학회:학술대회논문집
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• 한국소성가공학회 2004년도 제5회 압연심포지엄 신 시장 개척을 위한 압연기술
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• pp.109-118
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• 2004

No.2 PCM (Pickling and tandem cold rolling mill) at Pohang works was revamped in 2003. The purpose of this project is to produce carbon and stainless steel using conventional carbon production process, rolling and annealing. This paper introduces the applied facilities and technologies of PCM which are used in production of carbon and stainless steel. To realize the main purpose of this project, POSCO have developed laser weld technology in normal carbon and special steel (stainless, high carbon and high silicon). And this report describes the method which is developed to get down the surface defect of stainless 400 series. After revamping, No.2 PCM can have competitive power in this field and can supply the special steel using carbon rolling process.

• 제어로봇시스템학회논문지
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• 제16권10호
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• pp.1006-1012
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• 2010

The hot rolling process ranks the highest position for production in steel making process. The hot strip manufacturing processes consist of the reheating furnace, roughing and finishing mill and coiler. The reheating furnace heats the slab. The roughing and finishing mill produce the hot strip from slab. The hot strip quality mainly depends on finishing mill, which consists of 4-high 7 stands. The looper is installed between stands and is used for controlling the strip tension by the looper angle for better material flow. It is difficult to control the strip tension with the coupled looper system from interaction between the looper angle and strip tension. Too much deviation of strip tension severely affects the poor width quality of the hot strip. It is important to control simultaneously both the looper angle and strip tension with each of their target values. This paper proposes the fuzzy tension control, which is developed to minimize the width deviation of the hot strip by maintaining the proper strip tension between stands and to achieve the stable operation of the coupled looper system. The fuzzy tension control performance is compared with the conventional PID control by experimental results.

• 대한기계학회논문집A
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• 제20권9호
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• pp.2901-2907
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• 1996

Flatness of cold rolled strip is vital to the quality of the product and productivity of the mill. Therefore, in recent year requirement for flatness control in strip rolling have become increasingly severe. The necessity for more accurate automatic gauge control and automatic flatness control(AFC) has increased by customer's requirement for cold rolled steel sheets with thinner gauge and better flatness quality. In this paper, the performance and functions of AFC system installed on the 4 hi-reversing mill has been investigated under actual conditions. The test results are as follows : The more strip thickness is thick, the smaller the I-value. The I-value is a strain measured by stressometer. Complex distributions of strip tension are controlled to remove not only a quarter buckle but also a simple center wave and edge wave. The defects caused by poor flatness have been drastically decreased. And a proper coolant temperature for work roll cooling system on the AFC system is about $50~55^{\circ}C$.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2000년도 제15차 학술회의논문집
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• pp.180-180
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• 2000

The shape of cold strip for the stainless process has been become issue in quality recently, and hence POSCO (Pohang Iron & Steel Co., Ltd) developed an automatic control system for strip shape in the sendzimir mill. The strip shape is measured by an outward measuring roll and is controlled by As_U roll and first intermediate roll. As_U roll consists of 8 saddles, which are controlled vertically. The fist intermediate rolls, which are controlled horizontally, consist of two pairs of rolls up and down. A developed shape control system is applied to real plant by using fuzzy logic and neural network method to control actuators; As_U roll and first intermediate roll. This system composes mainly of three parts as a real-time system, input to output conditioner board, and man-machine interface. The actual shape is recognized by neural network and converted into symmetric shape. The fuzzy controller, based on the shape from neural network and sensor, controls positions of the As_U roll and first intermediate roll. This paper verifies the shape controller performance. The experiments are made on line for the sendzimir mill. The shape control performance shows very efficient for the target tracking, shape symmetry, and fluctuation of shape.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1998년도 하계학술대회 논문집 G
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• pp.2330-2332
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• 1998

This paper suggests a method to recognize the various defect patterns of cold mill strip using binary decision tree automatically constructed by genetic algorithm. The genetic algorithm and K-means algorithm were used to select a subset of the suitable features at each node in binary decision tree. The feature subset with maximum fitness is chosen and the patterns are classified into two classes by a linear decision boundary. This process was repeated at each node until all the patterns are classified into individual classes. The final recognizer is accomplished by neural network learning of a set of standard patterns at each node. Binary decision tree classifier was applied to the recognition of the defect patterns of cold mill strip and the experimental results were given to demonstrate the usefulness of the proposed scheme.

• Journal of Mechanical Science and Technology
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• 제18권7호
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• pp.1074-1085
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• 2004

There are many intermediate web guides in cold rolling mills process such as CRM (cold rolling mill), CGL (continuous galvanizing line), EGL (electrical galvanizing line) and so on. The main functions of the web guides are to adjust the center line of the web (strip) to the center line of the steel process. So they are called CPC (center position control). Rapid process speed cause large deviation between the center position of the strip and the process line. Too much deviation is not desirable. So the difference between the center position of the strip and the process line should be compensated. In general, the center position control of the web is obtained by the hydraulic driver and electrical controller. In this paper, we propose modelling and several controller designs for web-guide systems. We model the web and guide by using geometrical relations of the guide ignored the mass and stiffness of the web. To control the systems, we propose PID controllers with their gains tuned by the Ziegler-Nichols method, the H$\_$$\infty$/ controller model-matching method, and the coefficient diagram method (CDM). CDM is modified for high order systems. The results are verified by computer simulations.

• 소성∙가공
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• 제28권4호
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• pp.183-189
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• 2019

Cylindrical worm reducers are generally used in various fields and forms throughout the industry, and demand is increasing due to their role as an integral part of the industry. Market trends require high-load, high-precision components, and small-sized reducers with large loads. When using a cylindrical worm reducer, a reducer designed with a reduced center distance while maintaining the same output torque results in gear wear. To overcome this difficulty, an enveloping worm gear reducer is introduced and studied. In this paper, three types of end mill tools are used to evaluate the tooth profile accuracy for each tool shape during machining of the tooth profile for a non-developed surface worm thread. The effect of the endmill shape on the accuracy of the tooth profile was analyzed by performing 3D modeling of the surrounding worm tooth profile based on the Hindley method. In this study, we analyzed tooth profile accuracy, tooth surface roughness, and tooth surface machining time, etc. Through the study, efficient machining conditions for the enveloping worm gears and the influence of parameters on the process were presented.