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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 張培仁(Pei-Zen Chang) | |
| dc.contributor.author | Ping-Jung Chen | en |
| dc.contributor.author | 陳品蓉 | zh_TW |
| dc.date.accessioned | 2021-07-11T15:20:34Z | - |
| dc.date.available | 2023-02-12 | |
| dc.date.copyright | 2019-03-15 | |
| dc.date.issued | 2019 | |
| dc.date.submitted | 2019-02-19 | |
| dc.identifier.citation | Reference
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/78804 | - |
| dc.description.abstract | 主軸的內部溫度是預防保養的重要指標，但是其內部並無足夠空間可置入溫度感測器，而將主軸鑽孔則會降低其機械剛度，故難以直接量測主軸內部的溫度，因此建立主軸的熱特徵模型並據以預測其內部溫度成為重要的研究。本文提出了兩種系統識別(System Identification，SID)方法，分別為灰箱系統識別（Grey-box System Identification，GSID）和黑箱系統識別（Black-box System Identification，BSID），用來建立外部驅動式主軸的熱特徵模型。本研究包含溫度感測及無線傳輸模組（Temperature Sensing and Wireless Transmission Module，TSWTM）的自製硬體和用於系統識別（SID）的軟體; TSWTM用來蒐集SID所需的溫度訓練數據，以識別主軸熱特徵模型的參數。
在GSID中，主軸的熱特性被描述為以熱阻(thermal resistance)，熱容(thermal capacity)和熱源(heat source)所組成的熱網絡模型（Thermal Network Model，TNM），該TNM的參數則由MATLAB的System ID Toolbox提供的命令“greyest”識別出來。TNM可以預測出各特徵點在不同轉速下的溫升曲線，在主軸的工作模式和自然冷卻模式下，其預測溫度和測量數據的一致性分別達到71.83%和93.14％。在BSID中，主軸的熱特性由線性非時變狀態空間模型建模，BSID整合由MATLAB的System ID Toolbox提供的命令“n4sid”和機器學習中的K-fold交叉驗證方法，將模型的參數識別出來，所產生的熱特徵模型能夠在操作期間從其表面溫度準確地預測出主軸的內部溫度或者以轉速預測主軸的內外部溫度，其驗證準確率高於98.5％。BSID可以有效地最佳化模型的複雜度，在模型的偏差和變異之間取得平衡，避免擬合不足或是過度擬合的情況。本文比較了兩種用於建立主軸熱特徵模型的系統辨識方法，並說明了利用BSID算法從其表面溫度（可直接檢測）精確計算出主軸內部溫度（無法直接檢測）的可行性。 | zh_TW |
| dc.description.abstract | The internal temperature is an important index for the prevention and maintenance of a spindle. However, the temperature inside the spindle is undetectable directly because there is no space to embed temperature sensor and drilling holes will reduce its mechanical stiffness. Therefore, it is worthwhile to understand the thermal-feature of a spindle and predict the internal temperature variation of a spindle from its thermal-feature model. This article presents two method of system identification (SID), namely grey-box system identification (GSID) and black-cox system identification (BSID) to identify the thermal-feature model of an externally driven spindle. The works contains a self-made hardware of the temperature sensing and wireless transmission module (TSWTM) and a software for the SID; TSWTM is to acquire the temperature training data while SID to identify the parameters of the thermal-feature model of spindle. For GSID, the thermal-feature model of a spindle is modeled as a thermal network model (TNM) composed of equivalent thermal resistances, thermal capacity and heat source, and the parameters of the TNM are identified by the command “greyest” provided by the System ID Toolbox of MATLAB. The resulting TNM is used to figure out the temperature variation inside and outside the spindle via rotational speeds and the consistence with measurement data reaches to 71.83% and 93.14% during the operation and natural-cooling modes of the spindle respectively. For BSID, the thermal-feature of a spindle is modeled by a linearly time-invariant state-space model whose parameters are identified by BSID which integrates the command “n4sid” provided by the System ID Toolbox of MATLAB and the k-fold cross-validation that is common in machine learning. The present BSID can effectively strike a balance between the bias and variance of model, such that both under-fitting and over-fitting can be avoided. The resulting thermal-feature model can not only predict the temperature of spindle rotating at various speeds but also figure out the internal temperature of spindle from its surface temperature. Its validation accuracy is higher than 98.5%. This article compares two kind of system identification in thermal-feature modeling, and illustrates the feasibility of accurately figuring out the internal temperature (undetectable directly) of spindle from its surface temperature (detectable directly) with BSID. | en |
| dc.description.provenance | Made available in DSpace on 2021-07-11T15:20:34Z (GMT). No. of bitstreams: 1 ntu-108-R05543089-1.pdf: 10511356 bytes, checksum: dc5aebb6863b63dc9ea390eecef76984 (MD5) Previous issue date: 2019 | en |
| dc.description.tableofcontents | CONTENTS
誌謝 II 中文摘要 III ABSTRACT IV CONTENTS VI LIST OF FIGURES VIII LIST OF TABLES XII Chapter 1 Introduction 1 1.1 Motivation 1 1.2 Literature Survey 1 1.3 Thesis Organization 2 Chapter 2 Hardware 4 2.1 Temperature sensor and wireless transmission module (TSWTM) 4 2.1.1 Circuit board design process 7 2.1.2 Schematic design 8 2.1.3 PCB layout design 11 2.1.4 Hardware component integration 13 2.2 Experiment setup 15 2.2.1 Spindle run-in system 15 2.2.2 Performance and components of TSWTM 16 Chapter 3 Thermal-feature model of spindle 21 3.1 Grey-box system identification (GSID) for the thermal-feature model of spindle 21 3.1.1 Parameterization strategy of thermal network model 21 3.1.2 Thermal network model in operating mode 24 3.1.3 Thermal network model at steady state 28 3.1.4 Thermal network model in nature cooling mode 29 3.1.5 Algorithm of GSID 30 3.2 Black-box system identification (BSID) for the thermal-feature model of spindle 33 3.2.1 Data preparation stage 34 3.2.2 Structure determination stage 38 3.2.3 Parameter identification stage 40 Chapter 4 Results and Discussions 45 4.1 Validation for thermal-feature model established with grey-box system identification 45 4.1.1 Parameter estimation result 45 4.1.2 Self-validation and external-validation 45 4.2 Validation for thermal-feature model established with black-box system identification 50 4.2.1 Speed-dependence of the thermal-feature model of spindle 50 4.2.2 Using the thermal-feature model to predict the temperature variation of spindle at various speeds 52 4.2.3 Predicting internal temperature of spindle from its surface temperature 55 Chapter 5 Conclusion and future works 61 5.1.1 Conclusion 61 5.1.2 Future works 63 Reference 65 LIST OF FIGURES Figure 2.1.1 Functional block diagram of the TSWTM 5 Figure 2.1.2 (a) Schematic diagram of the current flow path for an analog input channel in parallel; (b) Signal transform firmware flow chart. 5 Figure 2.1.3 Circuit board design process diagram 8 Figure 2.1.4 Analog-to-digital circuit schematic of TS-WTM 10 Figure 2.1.5 ADG1607 functional block diagram [12] 10 Figure 2.1.6 Bluno Beetle board MCU&I/O part circuit schematic [13] 11 Figure 2.1.7 Analog-to-digital circuit PCB layout of TSWTM 12 Figure 2.1.8 (a) Top layer (b) Bottom layer of the TSWTM PCB layout 13 Figure 2.1.9 Analog-to-digital circuit component position of TSWTM 14 Figure 2.2.1 Experiment setup schematic 15 Figure 2.2.2 Sensor location and characteristic points on spindle. 16 Figure 2.2.3 The sensing error of TSWTM at various temperature: (a) CH1; (b) CH2; (c) CH3; (d) CH4; (e) CH5. 17 Figure 2.2.4 Temperature measurement of TSWTM elapsing a time-period at constant temperature: (a) 35℃ (b) 40℃ (c) 45℃ (d) 55℃. 18 Figure 2.2.5 Introduction of all components used in the TSWTM. 20 Figure 3.1.6 TSWTM assembly process 20 Figure 3.1.1 Sensor location and TNM representation on spindle 23 Figure 3.1.2 Thermal system block diagram 23 Figure 3.1.3 TNM in operating mode 24 Figure 3.1.4 TNM at steady state 28 Figure 3.1.5 TNM in nature cooling mode 29 Figure 3.1.6 The algorithm flowchart of GSID 32 Figure 3.1.7 Graphical user interface for GSID. 32 Figure 3.2.1 Procedure of BSID. 34 Figure 3.2.2 Raw data: (a) The time-series of temperatures measured by TSWTM; (b) the time-series of input rotational speed of spindle. Given the case of 4,000 rpm as an example. 36 Figure 3.2.3 Resampled data with the same sampling rate of 1 Hz: (a) The time-series of temperatures resampled from the raw data; (b) The time-series of the input rotational speed of spindle resample from the raw data. Given the case of 4,000 rpm as an example. 37 Figure 3.2.4 The final dataset for system identification. Given the case of 4,000 rpm as an example. 38 Figure 3.2.5 The temperature variations of seven spindle’s characteristic points at the rotational speeds same to that of Figure 3.2.2 (b) but measure on different dates: (a) rear bearing A; (b) rear bearing B; (c) front bearing C; (d) front bearing D; (e) inner housing mid; (f) rear outer housing; (g) front outer housing. 40 Figure 3.2.6 Select the model order by the command “n4sid” in the System ID Toolbox of MATLAB. 41 Figure 3.2.7 Model order determination via 2-fold cross validation: (a) The training and validation accuracies by MNRMSE at different model orders, wherein the best model order of 25 is indicated by an asterisk; (b) Zoom-in to the range near order 25. 44 Figure 4.1.1 Model self-validations of: (a) front bearing D (T1); (b) inner housing mid (T2); (c) rear bearing A (T3); (d) shaft mid (T4); (e) rear outer housing (T5); (f) front outer housing (T6) in operating mode 47 Figure 4.1.2 Model self-validations of: (a) front bearing D (T1); (b) inner housing mid (T2); (c) rear bearing A (T3); (d) shaft mid (T4); (e) rear outer housing (T5); (f) front outer housing (T6) in nature cooling mode 48 Figure 4.1.3 Model external-validations of: (a) front bearing D (T1); (b) inner housing mid (T2); (c) rear bearing A (T3); (d) shaft mid (T4); (e) rear outer housing (T5); (f) front outer housing (T6) in operating mode 49 Figure 4.1.4 Model external-validations of: (a) front bearing D (T1); (b) inner housing mid (T2); (c) rear bearing A (T3); (d) shaft mid (T4); (e) rear outer housing (T5); (f) front outer housing (T6) in nature cooling mode 50 Figure 4.2.1 The percentage VA of 8-order model: (a) The matrix chart; (b) The bar chart, wherein each group of bars corresponds to a row of the matrix chart. 52 Figure 4.2.2 The percentage VA of 25-order model: (a) The matrix chart; (b) The bar chart, wherein each group of bars corresponds to a row of the matrix chart. 52 Figure 4.2.3 Using the thermal-feature model of spindle to predict the temperature variation at: (a) rear bearing A; (b) rear bearing B; (c) front bearing C; (d) front bearing D; (e) inner housing mid; (f) rear outer housing, (g) front outer housing. 54 Figure 4.2.4 Using the 3I5O thermal-feature model (Table 4.2.1) to predict the internal temperature of spindle at: (a) rear bearing A; (b) rear bearing B; (c) front bearing C; (d) front bearing D; (e) inner housing mid. 58 Figure 4.2.5 Using the 2I5O thermal-feature model (Table 4.2.1) to predict the internal temperature of spindle at: (a) rear bearing A; (b) rear bearing B; (c) front bearing C; (d) front bearing D; (e) inner housing mid. 59 Figure 4.2.6 Using the 1I5O thermal-feature model (Table 4.2.1) to predict the internal temperature of spindle at: (a) rear bearing A; (b) rear bearing B; (c) front bearing C; (d) front bearing D; (e) inner housing mid. 59 Figure 4.2.7 The accuracies of temperature prediction by the thermal-feature models listed in Table 4.2.1. 60 LIST OF TABLES Table 2.1.1 ADG1607 truth table [12] 6 Table 2.1.2 IC component integration table 13 Table 2.1.3 Analog-to-digital circuit TSWTM Bill of Material (BOM) 14 Table 2.2.1 Specification of TSWTM 19 Table 2.2.2 Specifications of the temperature sensing probe 19 Table 2.2.3 Commercial temperature module specification comparison table 19 Table 3.2.1 The data descriptions for thermal-feature model identification, all data are recorded in year 2018. The superscript of data name indicates the fold number and the subscript indicates the input spindle speed. The format of record date is month/day. Note that each data contains 1 time-series of input spindle speed and 7 time-series of temperature variation corresponding to 7 characteristic temperature points of the spindle. 36 Table 3.2.2 Results of 2-fold cross validation at a given rotational speeds. 44 Table 4.1.1 Estimated thermal parameters of TNM 45 Table 4.2.1 The thermal-feature models and their input/output data. 56 | |
| dc.language.iso | en | |
| dc.subject | 熱特徵模型 | zh_TW |
| dc.subject | 溫度感測器 | zh_TW |
| dc.subject | 系統識別 | zh_TW |
| dc.subject | 工具機主軸 | zh_TW |
| dc.subject | 機器學習 | zh_TW |
| dc.subject | Thermal feature model | en |
| dc.subject | Machine learning | en |
| dc.subject | Machine tool spindle | en |
| dc.subject | System identification | en |
| dc.subject | Temperature sensor | en |
| dc.title | 工具機主軸之熱特徵系統辨識 | zh_TW |
| dc.title | Thermal-Feature System Identification for a Machine
Tool Spindle | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 107-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.coadvisor | 胡毓忠(Yuh-Chung Hu) | |
| dc.contributor.oralexamcommittee | 張智星(Jyh-Shing Jang),王啟昌(Chi-Chang Wang) | |
| dc.subject.keyword | 機器學習,工具機主軸,系統識別,溫度感測器,熱特徵模型, | zh_TW |
| dc.subject.keyword | Machine learning,Machine tool spindle,System identification,Temperature sensor,Thermal feature model, | en |
| dc.relation.page | 66 | |
| dc.identifier.doi | 10.6342/NTU201800972 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2019-02-19 | |
| 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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