臺灣博碩士論文加值系統

SUS304不鏽鋼具有優異的耐腐蝕性、耐熱性及機械性能，因此被應用於許多領域中，像是食品加工業、化學工業、醫療器械…等。SUS304不鏽鋼在高速精車削中，因加工中不易散熱、容易產生刀具積屑瘤…等問題。本研究藉由對製程的優化，不只減少刀具積屑瘤產生，也提高成品表面品質、降低切削過程的切削振動及減少刀具磨耗深度。
切削加工中，幾乎所有操作都存在著振動，且這些振動與成品和刀具間有著一定的關係。刀具和工件之間產生大幅度的切削振動時，將會導致成品表面品質變差及加速刀具磨損的現象發生。
本研究透過田口品質工程之實驗設計結合L27 (313)直交表，並利用反應曲面法對轉速、進給率及單邊切削深度對振動加速度訊號、平均表面粗糙度及平均刀腹磨耗深度，進行相關數學預測模型建立後，透過對模型進行模型決定係數(R2)計算確認模型擬合度，進而進行多目標參數優化，藉由望想函數法將多品質轉換為單一特性指標後，再進行最佳化運算，最終獲得最佳參數組合。經過實驗與量測後，模型的R2值在振動加速度訊號為0.96、平均表面粗糙度為0.90和平均刀腹磨耗深度為0.70；多目標優化模型則在皆為望小的情況，在轉速為2000 rpm、進給率0.2 mm/rev和單邊每刀切深0.08mm時，所得的望想值為84.18%的品質水準。藉由實驗比對之結果與誤差，與實際值相符合，證明此優化對於製程有所改善。

SUS304 stainless steel has excellent corrosion resistance, heat resistance and mechanical properties, so it is used in many fields, such as food processing industry, chemical industry, medical equipment...etc. In high-speed fine turning of SUS304 stainless steel, it is not easy to dissipate heat during processing, and it is easy to produce tool built-up edge...etc. By optimizing the manufacturing process, this study not only reduces the build-up edge of the tool, but also improves the surface quality of the finished product, reduces the cutting vibration during the cutting process, and reduces the depth of tool wear.
In cutting processing, vibration exists in almost all operations, and these vibrations have a certain relationship with the finished product and the cutting tool. When there is a large cutting vibration between the tool and the workpiece, it will lead to the deterioration of the surface quality of the finished product and the phenomenon of accelerated tool wear.
This study employed the Taguchi quality engineering approach with L27 (33) orthogonal array design and utilized response surface methodology to establish mathematical predictive models for vibration signal, average surface roughness, and average flank wear depth in relation to cutting parameters such as cutting speed, feed rate, and single-pass cutting depth. The model determination coefficient (R2) was calculated to confirm the fitting degree of the models. Subsequently, a multi-objective parameter optimization was conducted using the desirability function method to transform multiple quality characteristics into a single performance index, followed by the optimization process to obtain the optimal parameter combination. After conducting experiments and measurements, the R2 values of the models were found to be 0.96 for vibration signal, 0.90 for average surface roughness, and 0.70 for average flank wear depth. The multi-objective optimization model resulted in desired values for all criteria. The optimal parameter combination with a desirability value of 84.18% was achieved at a cutting speed of 2000 rpm, a feed rate of 0.2 mm/rev, and a single-pass cutting depth of 0.08 mm. The comparison between experimental results and predicted values demonstrated the effectiveness of this optimization approach in improving the machining process.

摘要 I
ABSTRACT III
誌 謝 V
目 錄 VI
第一章 緒論 1
1.1前言 1
1.2研究動機與目的 1
1.3論文架構 2
第二章 文獻回顧 3
2.1高速精車削加工 3
2.2表面粗糙度 5
2.3反應曲面法 7
第三章 研究方法 9
3.1研究架構 9
3.2研究流程 11
3.3實驗材料及刀具 12
3.4實驗設備 15
3.4.1 CNC車床 15
3.4.2振動加速度感測器及擷取卡 16
3.4.3形狀量測雷射顯微鏡 17
3.4.4表面粗度測定儀 18
3.5實驗設計 19
3.6田口/反應曲面法 23
3.6.1參數設計 23
3.6.2直交表 24
3.6.3訊號雜訊比(Signal-to-Noise ratio) 26
3.6.4變異數分析(Analysis of Variance) 27
3.6.5反應曲面法(Response Surface Methodology) 29
3.6.6回歸預測模型評估方法 30
3.6.7 D-optimal最佳設計方法 31
第四章 數據量測與分析方法 33
4.1振動加速度訊號量測及分析 33
4.2表面粗糙度量測及分析 35
4.3刀具磨耗量測及分析 36
第五章 研究結果與討論 39
5.1 高速精切削加工實驗結果 39
5.1.2平均表面粗糙度 42
5.1.3平均刀腹磨耗深度 43
5.2 反應曲面法及變異數分析結果 44
5.2.1振動加速度訊號之數學模型 45
5.2.2振動加速度訊號單目標優化 47
5.2.3平均表面粗糙度之數學模型 49
5.2.4平均表面粗糙度單目標優化 50
5.2.5平均刀腹磨耗深度之數學模型 52
5.2.6平均刀腹磨耗深度單目標優化 53
5.2.7多目標優化 55
5.3 D-optimal最佳設計方法優化 58
5.3.1 單目標優化 59
5.3.2 多目標優化 60
5.4最佳參數組合驗證實驗 61
5.4.1單目標優化驗證實驗 61
5.4.2多目標優化驗證實驗 63
第六章 結論與未來發展性 65
6.1結論 65
6.2未來發展性 66
參考文獻 67

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