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Toward Implementation of Production Systems Engineering (PSE) Method for Industry4.0

Title
Toward Implementation of Production Systems Engineering (PSE) Method for Industry4.0
Translated Title
4차 산업혁명을 위한 생산시스템 엔지니어링 (PSE) 방법의 구현을 향하여
Authors
Yuchang Won
DGIST Authors
Yuchang Won; Yongsoon EunKyung-Joon Park
Advisor(s)
은용순
Co-Advisor(s)
Kyung-Joon Park
Issue Date
2022
Available Date
2022-03-08
Degree Date
2022/02
Type
Thesis
Keywords
Productivity Analysis, Smart Factory, Industry 4.0, Production Systems Engineering, Parameter Estimation
Description
Productivity Analysis, Smart Factory, Industry 4.0, Production Systems Engineering, Parameter Estimation
Table Of Contents
I. Introduction 1 1.1 Production Systems Considered in Dissertation 3 1.2 Contributions and Outline of Dissertation 5 1.3 Literature Review 7 II. PSE Model Parameter Estimation 11 2.1 Machine and buffer 11 2.2 Single machine 11 2.3 Production line with two machines and one buffer 13 2.4 Production line with M machines and M − 1 buffers 14 III. Indirect Estimation of Exponential Machine Parameters 17 3.1 Indirect Estimation for Synchronous Exponential Lines 18 3.1.1 Analysis of Performance Measures 18 3.1.2 Two-Machine Lines (M = 2) 21 3.1.3 Lines With More Than Two Machines (M > 2) 22 3.2 Accuracy of Estimation on Synchronous Exponential Lines 28 3.2.1 Model Validation with Estimated Parameters 28 3.2.2 Accuracy in Bottleneck Identification 30 IV. Parameter Estimation based on Periodically Observed Data 33 4.1 Motivating Example and Problem Formulation 33 4.2 Expected Value of Estimated Parameters with Periodically Observed Data 37 4.3 Evaluation of Critical Number for Periodically observed data 39 4.3.1 Validation with simulation 42 4.4 Estimation algorithm of the critical number for periodically observed data 43 4.5 Case Study: Automotive Parts Assembly Machine 48 V. Productivity Analysis based on Data for Fault Monitoring 53 5.1 Automotive parts production line and fault monitoring data 54 5.1.1 Production line 54 5.1.2 Fault monitoring data 56 5.2 Obtaining uptime, downtime, cycle-time from the fault monitoring data 58 5.2.1 Preprocessing stage 59 5.2.2 Estimating Stage 60 5.3 Modeling, Validation, Bottleneck Identification 63 5.3.1 Modeling framework for continuous improvement 63 5.3.2 Structural modeling of production line 64 5.3.3 Machine Reliability Modeling 65 5.3.4 Model validation 66 5.3.5 Bottleneck identification 68 5.4 Continuous Improvement Scenarios 70 5.4.1 Effect of improving the bottleneck 70 5.4.2 Effect of improving multiple machines 70 VI. Productivity Analysis based on Data for Operating LMS 73 6.1 Parameter estimation based on data for operating LMS 74 6.1.1 System considerations and mover position data 74 6.1.2 Parameter estimation method based on mover position-time data 78 6.2 Analysis for buffer capacity of production line with LMS 83 6.2.1 Buffer capacity analysis and production line modeling 83 6.2.2 Model validation with Matlab simulation 87 6.2.3 Productivity analysis according to mover velocity 89 VII. Conclusion 93 A. Proof of Theorem in Chapter 3 95 B. Proof of Proposition in Chapter 4 99 국문초록 113
URI
http://dgist.dcollection.net/common/orgView/200000596422
http://hdl.handle.net/20.500.11750/16342
DOI
10.22677/thesis.200000596422
Degree
Doctor
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
Files:
There are no files associated with this item.
Collection:
Department of Electrical Engineering and Computer ScienceThesesPh.D.


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