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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 吳政鴻(Cheng-Hung Wu) | |
dc.contributor.author | Soon-Tat Ong | en |
dc.contributor.author | 王順達 | zh_TW |
dc.date.accessioned | 2021-06-08T03:50:55Z | - |
dc.date.copyright | 2018-11-07 | |
dc.date.issued | 2018 | |
dc.date.submitted | 2018-09-17 | |
dc.identifier.citation | [1]Abburi, N. and U. Dixit (2007). 'Multi-objective optimization of multipass turning processes.' The International Journal of Advanced Manufacturing Technology 32(9-10): 902-910.
[2]Atan, M. O. and M. Selim Akturk (2008). 'Single CNC machine scheduling with controllable processing times and multiple due dates.' International Journal of Production Research 46(21): 6087-6111. [3]Balin, S. (2011). 'Non-identical parallel machine scheduling using genetic algorithm.' Expert Systems with Applications 38(6): 6814-6821. [4]Chen, M.-C. and D.-M. Tsai (1996). 'A simulated annealing approach for optimization of multi-pass turning operations.' International Journal of Production Research 34(10): 2803-2825. [5]Coromant, S. (1994). Modern metal cutting, Sandvik Coromant. [6]Gilbert, W. (1950). 'Economics of machining.' Machining-Theory and Practice: 465-485. [7]Groover, M. P. (2007). Fundamentals of modern manufacturing: materials processes, and systems, John Wiley & Sons. [8]Gupta, R., et al. (1995). 'Determination of optimal subdivision of depth of cut in multipass turning with constraints.' International Journal of Production Research 33(9): 2555-2565. [9]Gurel, S. and M. S. Akturk (2007). 'Scheduling parallel CNC machines with time/cost trade-off considerations.' Computers & Operations Research 34(9): 2774-2789. [10]Gutowski, T., et al. (2006). Electrical energy requirements for manufacturing processes. 13th CIRP international conference on life cycle engineering, CIRP International Leuven, Belgium. [11]Holland, J. (1975). 'Adaptation in natural and artificial systems: an introductory analysis with application to biology.' Control and artificial intelligence. [12]Huang, L. and J. C. Chen (2001). 'A multiple regression model to predict in-process surface roughness in turning operation via accelerometer.' Journal of Industrial technology 17(2): 1-8. [13]Jabri, A., et al. (2013). 'Multi-objective optimization using genetic algorithms of multi-pass turning process.' Engineering 5(07): 601. [14]Jabri, A., et al. (2017). 'Multipass Turning Operation Process Optimization Using Hybrid Genetic Simulated Annealing Algorithm.' Modelling and Simulation in Engineering 2017. [15]Kayan, R. K. and M. S. Akturk (2005). 'A new bounding mechanism for the CNC machine scheduling problems with controllable processing times.' European Journal of Operational Research 167(3): 624-643. [16]Lin, W., et al. (2015). 'A multi-objective teaching− learning-based optimization algorithm to scheduling in turning processes for minimizing makespan and carbon footprint.' Journal of Cleaner Production 101: 337-347. [17]Lu, K., et al. (2013). 'Optimization of sequential subdivision of depth of cut in turning operations using dynamic programming.' The International Journal of Advanced Manufacturing Technology 68(5-8): 1733-1744. [18]Moradnazhad, M. and H. O. Unver (2017). 'Energy efficiency of machining operations: A review.' Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture 231(11): 1871-1889. [19]Peng, T. and X. Xu (2017). 'An interoperable energy consumption analysis system for CNC machining.' Journal of Cleaner Production 140: 1828-1841. [20]Satishkumar, S., et al. (2006). 'Optimization of depth of cut in multi-pass turning using nontraditional optimization techniques.' The International Journal of Advanced Manufacturing Technology 29(3-4): 230-238. [21]Senthilkumar, P. and P. Shahabudeen (2006). 'GA based heuristic for the open job shop scheduling problem.' The International Journal of Advanced Manufacturing Technology 30(3-4): 297-301. [22]Shabtay, D. and G. Steiner (2007). 'A survey of scheduling with controllable processing times.' Discrete Applied Mathematics 155(13): 1643-1666. [23]Shin, Y. and Y. Joo (1992). 'Optimization of machining conditions with practical constraints.' The International Journal of Production Research 30(12): 2907-2919. [24]Srinivas, J., et al. (2009). 'Optimization of multi-pass turning using particle swarm intelligence.' The International Journal of Advanced Manufacturing Technology 40(1-2): 56-66. [25]Turkcan, A., et al. (2003). 'Non-identical parallel CNC machine scheduling.' International Journal of Production Research 41(10): 2143-2168. [26]Wang, X. and C. Feng (2002). 'Development of empirical models for surface roughness prediction in finish turning.' The International Journal of Advanced Manufacturing Technology 20(5): 348-356. [27]Woldman, N. and R. Gibbons Machinability and Machining of Metals. [28] Electricity Price Knowledge Zone: 2016’s International Electricity Price www.taipower.com.tw/tc/page.aspx?mid=213&cid=351&cchk=1b3221ee-37c3-4811-9d4d-a1bb215f33c8 [29]Basic Salary Enactment and Adjustment Process Record www.mol.gov.tw/topic/3067/5990/13171/19154/ [30]Mistubishi’s Metal Ceramic NC Blade TNGG160402R/160404L-F NX2525 item.taobao.com/item.htm?id=523157271181 | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/21875 | - |
dc.description.abstract | 本研究以最小化製造成本與加權延誤成本為目的,探討平行車削機台之排程問題。車削作業(turning operation)為製造產業中常見之一機械製造作業。通過改變其作業參數,作業員能夠操控車削作業的工作時間。在實際工作環境中,一工件之車削作業往往需要多次車削才能完工。此類車削作業我們稱之為多層車削作業(multi-pass turning operation)。多層車削作業本身為一混整數非線性規劃問題(Mixed Integer Nonlinear Programming; MINLP)。由於車削作業的排程本身為可控時間排程問題(controllable processing time scheduling),這使得考慮多層車削作業的排程問題變得不易求解。本研究根據車削作業本身的特性,提出一二階段方法來求解此問題。第一階段先求解多層車削作業的最佳化參數,並在第二階段時以基因演算法(Genetic Algorithm)完成其排程演算。根據模擬結果顯示,本研究所提出之二階段方法能在合理的時間內求得比相關研究更為佳的解。 | zh_TW |
dc.description.abstract | This paper studies the scheduling problem of turning process in identical parallel machine environment. Scheduling of turning machine is a case of controllable processing time scheduling since the processing time of turning operation can be adjusted by alter the cutting speed, feed rate and depth of cut. The determination of subdivision of depth of cut is another decision problem to be solve in turning operation. Since the determination of subdivision of depth of cut is a MINLP problem itself, the combination of the two problem make the scheduling problem of turning machine very hard to solve. Our research objective is to build an effective approach to solve the minimization of the manufacturing cost and weighted tardiness cost in scheduling of parallel turning machine with multi-pass turning. The robustness of the methodology and computationally efficient are guaranteed under different scenario settings. | en |
dc.description.provenance | Made available in DSpace on 2021-06-08T03:50:55Z (GMT). No. of bitstreams: 1 ntu-107-R05546035-1.pdf: 2276950 bytes, checksum: b7217e49a9b45d4e7346028b1bd53298 (MD5) Previous issue date: 2018 | en |
dc.description.tableofcontents | 誌謝 II
ABSTRACT IV CONTENTS V LIST OF FIGURES VII LIST OF TABLES X Chapter 1 Introduction 1 1.1 Research Background 1 1.1.1 Optimization of Turning Operation 1 1.1.2 Determination of Subdivision of depth of cut 3 1.1.3 Scheduling of Turning Machine 4 1.2 Research Objective 4 1.3 Organization of the Thesis 5 Chapter 2 Literature Review 6 2.1 Optimization of multi-pass turning operation 6 2.2 Parallel Turning Machine Scheduling 7 Chapter 3 Problem Formulation and Methodology 8 3.1 Effect of Cutting Parameter on Cost Component in Turning Operation 8 3.1.1 Machining Time Property 9 3.1.2 Tool life Property 9 3.1.3 Power Consumption Property 13 3.2 Parallel Machine Scheduling Model for Multi-pass Turning Operation 18 3.2.1 Nomenclature and assumptions 18 Chapter 4 Multi-pass Turning Operation Scheduling with Machining Conditions’ Solution Set Feedback System 25 4.1 First-stage: Optimization of Turning Operation 25 4.1.1 Production Cost Minimization Model 25 4.1.2 Processing Time Minimization Model 26 4.2 Optimality of minimum number of cut 27 4.2.1 Validation of the methodology 29 4.2.2 Effectiveness Comparison of the methodology 35 4.3 Building of Machining Conditions’ Solution Set Feedback System 37 4.3.1 Scheduling Model 37 4.3.2 Comparison of Solution Sets 38 4.4 Construction of Genetic Algorithm 39 4.4.1 Chromosome Design 40 4.4.2 Operations and Parameters Setting 40 Chapter 5 Experiment Result and Analysis 44 5.1 Experiment Settings 44 5.1.1 Methodology for Comparison 44 5.1.2 Experimental Factors Design 45 5.1.3 Parameters Settings 47 5.2 Experimental Result for Single pass case 48 5.3 Experimental Result for Multi-pass case 53 Chapter 6 Conclusion and Future Research 60 6.1 Conclusion 60 6.2 Future Research Direction 60 Reference 61 Appendix A: pseudo code for GA 64 | |
dc.language.iso | en | |
dc.title | 考慮成本與交期之切削參數與排程最佳化 | zh_TW |
dc.title | Scheduling and Cutting Parameters Optimization for Parallel Turning Machines with Energy Consumption and Tool Life Considerations | en |
dc.type | Thesis | |
dc.date.schoolyear | 107-1 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 洪一薰(I-Hsuan Hong),陳文智(Wen-Chih Chen) | |
dc.subject.keyword | 可控作業時間,多層車削作業,平行機台排程,基因演算法,加權延誤成本, | zh_TW |
dc.subject.keyword | Controllable processing time,multi-pass turning operation,parallel machine scheduling,Genetic Algorithm,weighted tardiness cost, | en |
dc.relation.page | 69 | |
dc.identifier.doi | 10.6342/NTU201804127 | |
dc.rights.note | 未授權 | |
dc.date.accepted | 2018-09-18 | |
dc.contributor.author-college | 工學院 | zh_TW |
dc.contributor.author-dept | 工業工程學研究所 | zh_TW |
顯示於系所單位: | 工業工程學研究所 |
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