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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 張培仁(Pei-Zen Chang) | |
dc.contributor.author | Yuan-Chieh Lo | en |
dc.contributor.author | 羅元玠 | zh_TW |
dc.date.accessioned | 2021-07-10T22:18:50Z | - |
dc.date.available | 2021-07-10T22:18:50Z | - |
dc.date.copyright | 2017-08-11 | |
dc.date.issued | 2017 | |
dc.date.submitted | 2017-08-07 | |
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dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/77737 | - |
dc.description.abstract | 高速切削技術之發展過程中,工具機主軸的熱特徵為提升加工精度之重要參數。本研究以熱網路分析模型為基礎,採用系統識別法建構工具機主軸之暫態和穩態熱預測模型。硬體層面自行研發具高穩定度與高精度的溫度感測無線傳輸模組,以及適用於實驗量測之三種溫度探頭:(1)磁吸式、(2)螺紋式、(3)探棒式。模組測試結果已達到:精準度 ±(0.1+0.0029|t|)℃、解析度0.00489℃、功率為7 mW、模組尺寸Ø40×27mm3。內嵌數個微型溫度感測探頭於主軸之溫度特徵點(如:軸承外環、心軸、主軸表面、…等),設計於不同操作轉速及初始條件下量測溫升降曲線。軟體層面歸納主軸熱傳導特性,依據理論及經驗公式推導簡化與轉速相關之領導項,建構參數化熱模型。續搭配灰箱參數估測與實驗量測結果,實現主軸熱預測模型。經實驗證實此一熱模型識別法適用於工具機主軸溫度預測,即使在不同操作轉速以及初始溫度條件下,熱預測模型皆與實驗溫度數據吻合,吻合度達94.2%以上。同時應用系統降階法將主軸熱預測模型簡化,大幅減少數據運算量,以符合微處理器之運算能力,企圖實現邊緣運算(edge computing)之藍圖。 | zh_TW |
dc.description.abstract | Thermal characteristic analysis is essential for machine tool spindles because sudden catastrophic failures may occur due to the unexpected thermal issue. This article presents a lumped-parameter thermal network model and parameter identification scheme, including hardware and software, to characterize both the steady-state and transient thermal behavior of machine tool spindles. For the hardware, the author develops a Bluetooth temperature sensor module which accompanying with three types of temperature-sensing probes (magnetic, screw, and probe). Its specification, through experimental test, achieves to the precision ±(0.1+0.0029|t|) ℃, resolution 0.00489 ℃, power consumption 7 mW, and size Ø40×27 mm3. For the software, the heat transfer characteristics of the machine tool spindle correlative the rotating speed are derived based on the theory of heat transfer and empirical formula. The predictive thermal network model of spindles was developed with the grey-box estimation and experimental results. Even under such complicated operating conditions as various speeds and different initial conditions, the experiments validate that the present modeling methodology provides a robust and reliable tool for the temperature prediction with 94.2% agreement, and the present approach is transferable to the other spindles with similar structure. For realizing the edge computing in smart manufacturing, a reduced order thermal network model is constructed by model order reduction technique and implemented into the real-time embedded system. | en |
dc.description.provenance | Made available in DSpace on 2021-07-10T22:18:50Z (GMT). No. of bitstreams: 1 ntu-106-R04543023-1.pdf: 78624736 bytes, checksum: 113e7ff59918936f728bef1b1407a7f1 (MD5) Previous issue date: 2017 | en |
dc.description.tableofcontents | 口試委員會審定書 i
誌謝 ii 中文摘要 iii ABSTRACT iv CONTENTS v LIST OF FIGURES viii LIST OF TABLES xi NOMENCLATURE xii SUBSCRIPTS xiv Chapter 1 Introduction 1 1.1 Motivation 1 1.2 Literature Survey 2 1.2.1 Review on the modeling of machine tool spindle system 2 1.2.2 Review on the system identification applications 4 1.2.3 Overview of temperature sensor modules 5 1.2.4 Available products 6 1.3 Thesis Organization 7 Chapter 2 Thermal Characteristic of Machine Tool Spindle 9 2.1 Heat Generation 9 2.2 Conduction 12 2.2.1 Conduction 12 2.2.2 Thermal contact resistance 12 2.3 Convection 14 2.3.1 Free convection 14 2.3.2 Forced convection around moving surface 15 2.3.3 Forced convection near annulus 16 2.4 Radiation 17 2.4.1 Radiation 17 2.5 Heat Capacity 17 Chapter 3 System Identification of Thermal Network Model and Experimental Validation 19 3.1 Thermal Network Model for Spindle 19 3.1.1 Parameterization Strategy 19 3.1.2 TNM at operating mode 23 3.1.3 TNM at steady state 26 3.1.4 TNM at natural cooling mode 28 3.2 Experimental Setup 29 3.2.1 Experimental setup 29 3.2.2 Experimental result 31 3.3 Theory of System Identification 33 3.3.1 System identification process 33 3.3.2 Nonlinear least square for parameter estimation 35 3.3.3 Grey-box estimation for parameter estimation 36 3.4 TNM Validation 38 3.4.1 Parameter estimation 38 3.4.2 Steady state self-validation 41 3.4.3 Transient state self-validation 43 3.4.4 External validation 48 3.4.5 Model order reduction 49 3.4.6 Short circuit time constant 51 3.5 Summary 52 Chapter 4 Bluetooth Temperature Sensor Module 53 4.1 Module Design and Characterization 53 4.1.1 Specifications 53 4.1.2 Signal flow 54 4.1.3 Hardware 55 4.1.4 Software 55 4.1.5 GUI 57 4.1.6 Circuit design 58 4.2 Diagnose of the Error 60 4.2.1 Lead wire resistance induced error 61 4.2.2 Self-heating effect 62 4.2.3 Reference resistor variation induced error 62 4.2.4 ADC performance 63 4.3 Fabrication of Temperature Probe 64 4.3.1 Fabrication process 64 4.3.2 Types of temperature probe 65 4.4 Performance and Summary 65 4.4.1 Wireless temperature sensor module 65 4.4.2 Experimental setup 66 4.4.3 Accuracy 67 4.4.4 Temperature variation of the reference resistor induced error 68 4.4.5 Power consumption 69 4.4.6 Summary 69 Chapter 5 Conclusions and Future Works 70 5.1 Conclusions 70 5.2 Future Works 71 Reference 72 | |
dc.language.iso | en | |
dc.title | 工具機主軸之熱網路系統參數識別與藍牙溫度感測模組 | zh_TW |
dc.title | Parameter Estimation of the Thermal Network Model of a Machine Tool Spindle by Bluetooth Temperature Sensor Module | en |
dc.type | Thesis | |
dc.date.schoolyear | 105-2 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 胡毓忠(Yuh-Chung Hu),王啟昌(Chi-Chang Wang),李尉彰(Wei-Chang Li) | |
dc.subject.keyword | 熱網路模型,系統識別,工具機主軸,參數估計,熱特徵預測,藍牙溫度感測模組, | zh_TW |
dc.subject.keyword | Thermal network model,System identification,Machine tool spindle,Parameter estimation,Predictive thermal characteristic,Bluetooth temperature sensor module, | en |
dc.relation.page | 77 | |
dc.identifier.doi | 10.6342/NTU201702645 | |
dc.rights.note | 未授權 | |
dc.date.accepted | 2017-08-08 | |
dc.contributor.author-college | 工學院 | zh_TW |
dc.contributor.author-dept | 應用力學研究所 | zh_TW |
顯示於系所單位: | 應用力學研究所 |
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