本文探討複合式節流器之設計原理與方法，藉由理論分析複合式節流器的工作原理，並利用程式模擬複合式節流器與軸承系統中不同流阻比之性能表現，再以現有液靜壓實驗平台進行實驗驗證，探討其不同參數之間的關係。首先使用基本方法驗證、作圖，接著使用預處理過的數據來訓練類神經網路，並輸出預測結果。文中基於實驗結果，利用類神經網路所建構出的數學模型，來輔助冗長、非線性、難以驗證的物理、數學推導，試圖為液靜壓軸承節流器最佳化之設計與分析提出新的方法與工具；且可利用類神經網路之模型與物理模型針對設計參數與機械性能交互比對、驗證兩者之正確性。
本研究利用類神經網路模型，不僅能夠達到預測液靜壓軸承系統搭配不同節流器時，隨著負載、供油壓力、油溫等參數變化時其他相應參數的可能變化，並依此結果得到節流器幾何之新設計值；有別於目前工業大數據中機器學習等工具，皆大多應用於監測機器、機台之健康狀態，預估保養時程及元件耗損、更換時間等，期待本論文研究之結果能向上推展，實際應用在精密機械的設計階段，支援CPS智能決策系統，並提供智慧化機台的更多可能性，以期達成工業4.0之願景。

The flow restrictor, one of the critical elements of hydrostatic bearings, provides the self-adjusting capability of pocket pressure and stiffness. With the increasing usage of hydrostatic bearings, reliability requirements for flow restrictors have become more crucial.
The purpose of this thesis is to provide a useful method to analyze and optimize the dimension, preload, stiffness and various design parameters of flow restrictors. The method to carry out this study is using artificial neural network (ANN) models. By using feedforward neural network, the ANNs are capable of estimating any function by massively connecting appropriate number of neurons and different kinds of layers. After data preprocessing and cross-validation, the network is more stable and workable for our system with non-linear characteristic.
In this thesis the function of fluid restrictors affecting the performance of hydrostatic bearings is investigated both theoretically and experimentally. An apparatus is constructed, which integrates a sliding hydrostatic table with several flow restrictor connectors. With these connectors, various kinds of restrictors such as capillary or membrane type restrictor are assembled and installed due to individual requirements. Furthermore, the pressure, flow and temperature of each pad are measured on-line simultaneously.
Since limited experimental measurements are available to illustrate the properties and interaction between various parameters, and additionally, the theoretical derivation is complicated, non-linear and hard to be verified, the proposed neural network structure is demonstrated very suitable to model the flow restrictor design problem. By means of a well-trained network, the design parameters and the data with the uncertainty of the derivation had been successfully correlated.

摘要 II
ABSTRACT III
致謝辭 V
目錄 VI
符號表 X
第一章 導論 1
1-1 研究背景 1
1-2 液靜壓軸承與節流器文獻回顧 3
1-2-1 固定式節流器之研究 3
1-2-2 自補償節流器之研究 5
1-2-3 複合型節流器之研究 6
1-3 類神經網路發展回顧 8
1-4 研究動機與本文內容 11
第二章 液靜壓軸承與節流器基本理論推導 13
2-1 毛細管節流器 14
2-2 溝槽型節流器 18
2-3 油墊與主動式節流器封油面流阻計算 22
2-3-1 兩平行板間流場分析 22
2-3-2 環形封油面之流場分析 24
2-3-3 方形與環形封油面流阻公式整理 26
第三章 液靜壓軸承流阻網路法分析 29
3-1 單向墊流阻網路法分析 30
3-1-1 固定式節流器搭配單向墊系統 31
3-1-2 自補償節流器搭配單向墊系統 33
3-1-3 複合式節流器搭配單向墊系統 35
3-2 對向墊流阻網路法分析 36
3-2-1 固定式節流器搭配對向墊系統 36
3-2-2 自補償節流器搭配對向墊系統 38
3-2-3 複合式節流器搭配對向墊系統 42
第四章 溝槽式節流器實驗研究 44
4-1 液靜壓節流器實驗架構 44
4-1-1 液靜壓軸承小型實驗平台與節流器整合座 44
4-1-2 實驗與量測設備 46
4-1-3 實驗架設 49
4-2 實驗方法與步驟 51
4-3 固定式(溝槽型)節流器單向墊實驗結果 53
第五章 複合式節流器模擬與分析 58
5-1 實驗室研究成果整理 58
5-2 過往模擬回顧 60
5-3 流阻比例無因次化模擬 63
5-4 自補償效果定義 67
第六章 複合式節流器實驗研究 69
6-1 盤型彈簧剛性實驗 69
6-2 複合式節流器不同彈簧配置實驗 75
6-3 複合式節流器預壓調整實驗 78
6-4 複合式節流器預壓比較實驗 80
6-5 複合式節流器實驗-連續紀錄動態反應 84
第七章 類神經網路架構與應用實作 95
7-1 監督式學習與類神經網路簡介 97
7-1-1 監督式學習 98
7-1-2 類神經元與類神經網路 99
7-1-3 訓練類神經網路 103
7-2 建立類神經網路模型 106
7-2-1 資料前處理 107
7-2-2 網路架構 109
7-2-3 訓練演算法選用 109
7-2-4 網路驗證 110
7-3 固定式(溝槽型)節流器實驗數據訓練與預測結果 112
7-4 如何將類神經網路作為設計工具 118
第八章 結論與未來工作 121
8-1 結論 121
8-2 未來工作 122
參考文獻 126

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