本研究為使用鋁合金輔助電極於多晶矽工件進行線放電加工，並觀測使用輔助電極時，多晶矽之材料移除率、表面粗糙度、表面合金化之加工表面。實驗前先以焦耳熱模型計算加工時所需之放電能量，並控制操作參數於區間包含：開路電壓(50-100 V)、脈衝放電時間(30-60 μs)、輔助電極厚度(0-25 mm)以及線電極速度變頻控制(120-180 unit voltage)在符合加工能量之條件下，依田口實驗陣列(L27)進行實驗驗證。數據之分析則使用統計方法之變異數分析(Analysis of variance, ANOVA)及主因子圖(Main effect plots)分析，對操作參數進行檢定，以檢出各參數對觀測指標之影響力及貢獻度，並建議該指標之最佳化參數組合。最後，建立各操作參數對指標之關係模型，在給予各指標均等權重下將方程式進行疊加，以建立多指標最佳化之方程式。實驗結果顯示，線放電加工多晶矽工件使用輔助電極可得到較高之材料移除率(7.62 mg/min)及較低之算術平均面粗糙度(Sa 5.36 μm)，並對工件表面進行改質，在表面鍍附~3.48%之鋁合金。多指標最佳化的結果建議，當操作參數為開路電壓50 V、脈衝放電時間30 μs、變頻控制180單位電壓無使用輔助電極時，多指標之材料移除率值降低2.98 mg/min，表面粗糙度少許上升 Sa 2.12 μm，以及表面改質之鋁元素鍍附率些微地減少0.87 wt. %，為同步得到三指標之優化的結果。

This study presents a state-of-the-art wire electrical discharge machining technology using assisting electrodes shaping polycrystalline silicon workpiece to improve the material removal rate, and surface roughness with surface modification. The experimental work was carried out with the consideration of the Joule heat effect under the operating parameters of open voltage (50-100 V), pulse on-time (30-60 μs), assisting electrode thickness (0-25 mm), and frequency control (120-180 unit) of servo voltage in 10-18 V. Statistical methods such as the main effect plots and ANOVA were used for determining the preferable combinations and the significance of the operating parameters, respectively. For the result showed that high material removal rate (7.62 mg/min) and low surface roughness (Sa 5.36 μm), with the aluminum content of ~3.48% were obtained on the machined surface when assisting electrodes of 15 mm were facilitated in the tests. When the single objective equations were superimposed with the predetermined weightings, the multi-objective function can be established. The preferable combination suggested open voltage of 50 V, pulse on-time of 30 μs, and frequency control of 180 unit without assisting electrode, which resulted in the least error for reduced material removal rate (63%), increasing surface roughness (7%) and reduced surface modification (73%), compared to those in the single objective optimization.

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