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학술논문한국경영과학회지2018.08 발행

전기 저항 용접 강관 제조 공정 분석을 위한 플로우 라인 모형의 응용

Application of Flow Line Model for Electric Resistance Welding Pipe Manufacturing Process Analysis

박예은(한동대학교); 김우성(한동대학교)

43권 3호, 37~50쪽

초록

A flow line model that consists of a two-stage conveyor belt is used to analyze the manufacturing process of electrical resistance welding (ERW) pipes. An ERW pipe is manufactured through two conveyor belts in which manufacturing and inspection are both performed. Various types of pipes are produced in a single line. The speed of the conveyor belt differs depending on the product type, and a preparation period (set-up) is needed when the product type is switched. To prevent production rate loss caused by the preparation period, a finite capacity of the waiting space (buffer) is provided between two conveyor belts. The purpose of this study is to analyze how set-up period and buffer capacity affect the performance measures of the process. A Markov chain model is constructed, and performance measures are analyzed to obtain the study results

Abstract

A flow line model that consists of a two-stage conveyor belt is used to analyze the manufacturing process of electrical resistance welding (ERW) pipes. An ERW pipe is manufactured through two conveyor belts in which manufacturing and inspection are both performed. Various types of pipes are produced in a single line. The speed of the conveyor belt differs depending on the product type, and a preparation period (set-up) is needed when the product type is switched. To prevent production rate loss caused by the preparation period, a finite capacity of the waiting space (buffer) is provided between two conveyor belts. The purpose of this study is to analyze how set-up period and buffer capacity affect the performance measures of the process. A Markov chain model is constructed, and performance measures are analyzed to obtain the study results

발행기관:
한국경영과학회
DOI:
http://dx.doi.org/10.7737/JKORMS.2018.43.3.037
분류:
경영학

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전기 저항 용접 강관 제조 공정 분석을 위한 플로우 라인 모형의 응용 | 한국경영과학회지 2018 | AskLaw | 애스크로 AI