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Data-driven modeling and performance analysis of production system : A case study of the auto lever production line

Title
Data-driven modeling and performance analysis of production system : A case study of the auto lever production line
Translated Title
데이터 기반 생산시스템 모델링 및 성능 분석 : 신형 오토 레버 생산라인 사례 연구
Authors
Jihwan Lee
DGIST Authors
Lee, Jihwan; Eun, YongsoonPark, Kyung-Joon
Advisor(s)
박경준
Co-Advisor(s)
Yongsoon Eun
Issue Date
2020
Available Date
2020-06-23
Degree Date
2020-02
Type
Thesis
Description
생산 시스템 공학, 데이터 전처리, 모델링, 병목 분석, 생산성 향상
Abstract
In this paper, we suggest data preprocessing method to model production system and analyze a performance of a serial line having identical machines that work in turn. A four type of data for modeling are defined and several data preprocessing algorithms based on defined data have been developed to calculate cycletime, downtime and buffer capacity. it is shown that there are the five kind of different production type which has different cycle-time distribution and production type 3 occupies more than half of total productivity. we confirmed that this line can also be applied to modeling based on aggregation method in production system engineering by analyzing production rate, blockage and starvation through the simulation. Finally, an asynchronous exponential model is used for modeling of production system, and an estimation of throughput is obtained from model. The difference with actual TP for product type 3 was about 7.74% which is typically acceptable error. For a bottleneck identification using arrow method with calculated parameter, the bottleneck machine of production type 3 is identified as m18. Finally, two kinds of improvements method are suggested through the bottleneck mitigation.
Table Of Contents
I. Introduction 10 II. Data definition required for modeling 11 III. Line description 12 IV. System modeling 14 4.1 Structure modeling 14 4.2 Data process 15 4.3 Identical machine analysis 20 4.3.1 Parameter error in two machine line 20 4.3.2 Parameter error in six machine line 22 V. Parameter identification and performance analysis 24 5.1 Parameter identification 24 5.2 Performance analysis 26 VI. Conclusions 31
URI
http://dgist.dcollection.net/common/orgView/200000286760
http://hdl.handle.net/20.500.11750/12018
DOI
10.22677/Theses.200000286760
Degree
Master
Department
Information and Communication Engineering
University
DGIST
Related Researcher
  • Author Eun, Yongsoon DSC Lab(Dynamic Systems and Control Laboratory)
  • Research Interests Resilient control systems; Control systems with nonlinear sensors and actuators; Quasi-linear control systems; Intelligent transportation systems; Networked control systems
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Collection:
Department of Information and Communication EngineeringThesesMaster


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