• Journal of the Korean Society for Precision Engineering
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• v.13 no.6
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• pp.152-160
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• 1996

This paper focuses on development and evaluation of a performance management algorithm for IEEE 802.4 token bus networks to serve large-scale integrated manufacturing systems. Such factory automation networks have to satisfy delay constraints imposed on time-critical messages while maintaining as much network capacity as possible for non-time-critical messages. This paper presents a network performance manager that adjusts queue capacity as well as timers by using a set of fuzzy rules and fuzzy inference mechanism. The efficacy of the performance management has been demonstrated by a series of simulation experiments.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.20 no.8
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• pp.2436-2448
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• 1996

This paper presents the development of three types of network performance manager for IEEE 802.4 token bus networks that are a part of Manufacturing Automation Protocol(MAP). The performance managers attempt to keep the average data latency below a certain level specified for each priority class. All of the three performance managers are based on a set of fuzzy rules incorporating the knowledge on the relationship between data latency and parameters of the priority mechanism. These Fuzzy Network Performance Managers(FNPMs) have been evaluated via discrete event simulation to demonstrate their efficacy.

• 제어로봇시스템학회:학술대회논문집
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• 1989.10a
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• pp.421-426
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• 1989

Many of semiconductor manufacturing companies persuit automation of wafer fabrication to improve the yields and quality of their products. Development of real-time control system for wafer fabrication and wafer/cassette automatic transfer-system is the most important part to achieve the purpose. In this paper, SECS protocol proposed by SEMI is briefly reviewed and an implementation method of real-time monitoring and control system is suggested as one of the possible ways for wafer fabrication automation. The system consists of process equipments supporting SECS.

• 제어로봇시스템학회:학술대회논문집
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• 1988.10a
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• pp.390-395
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• 1988

The implementation of MAP(Manufacturing Automation Protocol) NIU(network interface uint) is studied and presented in this paper. Hardwares and system softwares for network management, user interface, and medium access control are developed. Also LLC(Logical Link Control) protocol is implemented. Afterwards, The operations of NIU using self-test functions are tested. Compatibility between interfaces according to IEEE 802.4 standard is also tested.

• Journal of the Korean Society for Precision Engineering
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• v.3 no.1
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• pp.13-19
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• 1986

자동제조공정 자동화를 추진하고 있는 GM사는 앞으로 5 년내에 생산 System에 컴퓨터를 활용하는 기술개발에 수백만불을 투입한다고 한다. 생산에서 컴퓨터 황용기술은 종래의 설계소요시간을 단축하는것 외에도 날로 불어나는 설계조건을 쉽게 처리하여 설계의 집적 도를 높이는 효과 그리고 FA의 효율, 기능, 유연성에 있어서도 효과적이라는 것이다. 즉, MAP는 CIM(Computer Integrated Manufacturing)의 구현에의 길목에서 처리하고 지나가야 될 숙명적인 과제라 할 수 있다.

• Journal of the Semiconductor & Display Technology
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• v.12 no.2
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• pp.39-43
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• 2013

The semiconductor equipments generate necessary data through communication networks for the effective manufacturing processes and automation of semiconductor equipments. For transferring data between semiconductor equipments and sending data to monitor equipments, several standards for communication protocols have been proposed. Communication networks in semiconductor manufacturing systems will transmit a lot of data traffic, which can be vulnerable in data delay and network failure. Therefore, it is required that data traffic need to be distributed. To accomplish this objective, we recommend the use of a redundant and valuable communication path which is constructed by a wireless sensor network. In this paper, the directed diffusion method for wireless sensor networking is suggested for networking semiconductor equipments. It is shown that how the directed diffusion is employed to connect semiconductor equipments. Also, we show how to implement the SECS of semiconductor equipments communication protocols based on the directed diffusion.

• Journal of Institute of Control, Robotics and Systems
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• v.6 no.4
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• pp.313-319
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• 2000

As the interest of flexible manufacturing systems and computer integrated manufacturing systems increase, the distribution of centralized control systems using industrial control networks is getting more attention. In this paper, we investigate the rate-based traffic control of industrial control networks to improve the performance regarding the throughput, fairness, and error rates. Especially, we consider the protocol of Lon-$Works^{(TM)}$ which consists of all OSI 7-layers and supports various communication media at a low cost. Basically, the proposed rate-based traffic control system is closed loop by utilizing the feedback channel errors, which shows improved performance when compared with other industrial control networks commonly operated in open loop. To this end, an additional network node called monitoring node is introduced to check the channel status without increasing the channel load. The Proposed control loop is in effect whenever the feedback channel error becomes greater than an admittable value. We demonstrate the improved performance of the controlled network system in view of throughput and fairness measures by implementing the lab-scale network system based on LonWorks and through the experimentation upon it.

• Journal of Institute of Control, Robotics and Systems
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• v.22 no.12
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• pp.1027-1033
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• 2016

There is an increasing need for manufacturing systems to produce batches in small quantities. Such manufacturing systems are significantly difficult to develop with conventional automation equipment. Recently, several research groups have applied industrial dual-arm robots to cell production lines. A synchronization method for robots is necessary for the cell production process when robots work in a shared workspace. Conventional automation factories do not need this method because the main control system operates all of the machines or robots. However, our intended application for the developed robot is in small manufacturing environments that cannot install an expensive main control system. We propose an inexpensive and high-performance method with a simple digital in/out channel using a real-time communication protocol. The developed method was validated in a pilot production line for cellular phone packing.

• Journal of the Semiconductor & Display Technology
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• v.11 no.4
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• pp.13-18
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• 2012

The semiconductor equipments generate a lot of data traffic via communication channels for the effective manufacturing processes of semiconductors and for the automation of semiconductor equipments. These data should be sent to a host computer in order to keep in a safe. The IEEE 802.11e specification gives differentiated and distributed access to a wireless communication medium with four access categories. These four access categories use different priority parameters which lead to quality of service in data traffic. In this paper, it is shown that the throughput of data depends upon the priority parameters provided by IEEE802.11e. We implement the usage of priority parameters in the SECS protocol for semiconductor equipments. It is necessary that the data from semiconductor equipments should be classified with certain priority for the effective use of our proposed method. Therefore, our scheme will contribute to improving the performance of the manufacturing processes of semiconductors.

• International Journal of Internet, Broadcasting and Communication
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• v.13 no.4
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• pp.121-128
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• 2021

In order to design a real time big data collection and analysis system of manufacturing data in a smart factory, it is important to establish an appropriate wired/wireless communication system and protocol. This paper introduces the latest communication protocol, OPC-UA (Open Platform Communication Unified Architecture) based client/server function, applied user interface technology to configure a network for real-time data collection through IoT Integration. Then, Database is designed in MES (Manufacturing Execution System) based on the analysis table that reflects the user's requirements among the data extracted from the new cutting process automation process, bush inner diameter indentation measurement system and tool monitoring/inspection system. In summary, big data analysis system introduced in this paper performs SPC (statistical Process Control) analysis and visualization analysis with interface of OPC-UA-based wired/wireless communication. Through AI learning modeling with XGBoost (eXtream Gradient Boosting) and LR (Linear Regression) algorithm, quality and visualization analysis is carried out the storage and connection to the cloud.