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.

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