本論文主要是尋求線性滑軌設計參數最佳化，利用田口實驗設計法來規劃實驗，再藉由S/N比、灰色關聯分析法、和變異數分析來找出最佳參數組合。實驗設計使用田口方法，選擇預壓等級、潤滑油黏性、鋼珠精密等級及有無鏈帶有無法蘭型式為控制因子，其品質則是選擇滑座順暢度、水平組合精度、行走精度、推力值、噪音值、滑軌表面粗度等為評估的指標，根據線性滑軌使用者所輸出的性能參數期望值，利用現有的測試所得之資料，將線性滑軌設計參數輸入和性能參數輸出的特性，使用田口方法暨灰關聯分析找出最佳參數組合，由於一般實務的要求都會希望達到多目標品質特性最佳化，但就田口方法難以直接處理多重品質特性的情況，因此選擇利用灰色關聯分析來處理多重品質特性的問題，藉以瞭解同時考慮多重品質特性時的最佳線性滑軌製程條件，同樣以變異數分析來瞭解控制因子的貢獻度。另外建立線性滑軌類神經網路預測建模，此方法具有準確預測能力，再結合類神經網路暨遺傳基因演算法找出最佳設計參數，期待優於田口方法暨灰關聯分析而能符合線性滑軌使用者所要求之較佳品質特性，得到由線性滑軌不同設計參數組合，使得整個線性滑軌的製程，在精度、噪音值、滑軌表面粗度等方面皆能提昇。

The main purpose of this paper is to optimize the design parameters of linear motion guide. The experiment is planned via the Taguchi experiment design method. Through the S/N ratio, grey-based Taguchi methods and analysis of variance, the optimal parameter combination is identified. The design of experiment employs the Taguchi method. Control factors include the preload grade, lubricant viscosity, ball precision grade, and with/without ball cage and with/without flange types. Its quality evaluation indicators comprise base smoothness, horizontal combination error, movement error, thrust value, noise value and motion guide surface roughness. Based on the quality parameters expectation value outputted by the linear motion guide user, current test data is utilized in conjunction with linear motion guide design parameters input and quality parameters output for identification of the optimal parameter combination through the grey-based Taguchi methods. In practice optimization of multiple quality characteristics is generally expected. Due to the fact that the Taguchi method is unable to process multiple quality characteristics directly, the grey relation analysis is employed to deal with multiple quality issues in order to understand what the optimal linear motion guide process condition will be when multiple quality characteristics are taken into consideration simultaneously. The analysis of variance is then conducted to identify the contribution of control factor. Moreover we established the neural network prediction modeling of the linear motion guide. This method has the accurate forecast ability. The analytical method incorporates Genetic Algorithm and neural network in order to find the optimal design parameters. We anticipated the optimal quality characteristics that can better meet the requirement of the linear motion guide user that what have been identified through the grey-based Taguchi methods. This analysis follows to equip it with the ability to predict more accurately. A better linear motion guide design parameters combination can be acquired as a result to enhance the entire linear motion guide process in terms of precision, noise and motion guide surface roughness.

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