臺灣博碩士論文加值系統

隨著近年消費趨勢、加工材料與工法的改變，加工產業已從傳統的大量生產轉向輕薄短小的高品質精密加工，使得生產品質的控管與預測越來越受到重視，更成為近年來各界研究探討建立預測系統的重點，對於品質預測系統的建立方法有傳統的統計預測、模擬建模、回歸分析及個類軟性計算等方式，更有研究加入接觸式的力感測器、聲射感測器與加速規協助數據收集與投入預測補正，且都保有一定的準確率。但是上述所提及的預測模型皆需要大量數據與時間進行建模，加工參數條件一旦改變，就需要重新建模或調整系統，對於目前高精密少量多樣的加工趨勢相當不便；而且加工中的切削液與碎屑對於加裝的接觸式感測器都會形成容易故障的環境。故本研究將採用灰色理論結合非接觸式的聲波訊號感測器，希望透過少數據建模與聲波訊號補正，針對微型鑽孔加工建構一個不受加工參數條件影響的孔徑與刀具壽命即時預測系統。
　　本研究針對微鑽孔孔徑與刀具壽命所開發之灰色理論結合聲波訊號預測系統，利用灰色預測小樣本建模的特性為基礎，透過灰生成，將孔徑及聲波訊號數據輸入並顯著化其趨勢，分別預測出孔徑及聲波訊號值。再透過線性趨勢與線性插值法將聲波訊號補正回孔徑預測值，去預測孔徑與刀具壽命。本研究所使用的方法在各種加工參數與環境下，皆可達到快速建模、即時預測的目標。
　　為證實所提出的方法其可行性與準確性，本研究將設置兩組不同加工參數，進行微鑽孔加工，透過金相式電子顯微鏡與聲波訊號感測器，將孔徑與聲波訊號值投入灰預測系統，再將聲波訊號預測值補正回孔徑預測值，藉此得到更準確的各孔孔徑預測與整體刀具壽命，並探討其準確性。孔徑部分兩組補正值皆比原始預測精準且顯著，刀具壽命部分補正後平均精準度高達96.97%，比原始預測87.795%提升將近10%的準確率，故得以證實本研究所提出之預測系統其可行性與準確性。

As the consumer trends and the change of processing in recent years, production orientation of machining process has evolved from mass production to high quality, small-scale production; therefore, quality control and predict is becoming increasingly important. Various prediction methods such as tradition statistical forecasting, simulation modeling, regression analysis and soft computing, have been proposed to construct prediction systems in these years, and some studies even added contact force sensor, acoustic emission sensor and accelerometers to assist data collection and to increase accuracy. However, these prediction models need huge data and time to create, if processing parameters changed, it has to reboot system, which is unfavorable to high-precision machining and small-scale productions. Cutting fluid and chips are also have bad effect to contact sensors. Due to the inconvenience, in this research will implement an in-process micro drilling diameters and tool life forecasting system which integrate grey theory and non-contact acoustic sensor to correct and without the influence of the changed process setting.
　　The grey theory integrate acoustic signal in micro drilling diameters and tool life forecasting system proposed in this research is develop through grey theory and acoustic signal sensor, a method which emphasizes the input data trend and uses its small sample feature as the fundamental structure for building a real time prediction system. In this research using grey theory to forecasting cutting diameters and acoustic signal, then correction acoustic signal to diameters by linear interpolation. The grey and acoustic signal prediction system can quickly predict micro drilling diameters and tool’s operating life under specific processing settings without considering the type of tool and work part being used, the machining parameters and environment.
　　In order to prove the proposed method is both accurate and reliable, two different sets of machining parameters are used to perform micro drilling. First, an optical instrument is used to measure the diameter of the machined holes and acoustic signal sensor is used to measure the noise .Then using a small amount of the measured data are placed in the grey prediction system for the forecasting of hole sizes and tool life. Based on the information obtained, we can use hypothesis testing and (Mean Absolute Percentage Error , MAPE) test to determine a tool’s operating life and investigate its accuracy to verify the feasibility of the prediction system proposed in this research.

目錄
摘 要 I
Abstract II
致 謝 III
目錄 IV
圖目錄 VII
表目錄 VIII
第一章 緒論 1
1.1 研究背景與動機 1
1.2 研究目的 3
1.3 研究範圍與限制 3
1.4 研究假設 4
1.5 研究架構 5
第二章 文獻探討 7
2.1 CNC鑽孔加工 7
2.1.1 電腦數值控制(CNC) 7
2.1.2 鑽孔加工與微型鑽孔 8
2.1.3 尺寸公差 9
2.2 刀具磨耗與壽命 11
2.2.1 刀具磨耗 11
2.2.2 刀具壽命 12
2.3 刀具監測感測技術 13
2.4 刀具壽命預測模型 15
2.4.1 泰勒刀具壽命方程式 (Tayler Tool Life Equation) 15
2.4.2 刀具壽命預測系統 15
2.5 灰色系統理論 16
2.5.1 灰色系統理論概述 16
2.5.2 灰關聯分析 18
2.5.3 AGO灰色累加生成基本原理 21
2.5.4 灰色預測模型GM(1,1)文獻探討 23
2.5.5 灰色預測與灰色建模 24
2.6 聲波訊號轉換法 26
第三章 實驗設置與研究方法 29
3.1 實驗設備與原始數據收集 30
3.1.1 硬體設備 30
3.1.2 軟體設備 32
3.1.3 原始數據收集 33
3.2 灰關聯分析 35
3.3 灰色預測系統GM(1,1)設置 36
3.3.1 AGO生成序列 37
3.3.2 灰色預測建模 38
3.3.3 預測精度滾動檢驗 39
3.3.4 刀具壽命灰色預測系統 40
3.4 線性回歸趨勢線 41
3.5 聲波訊號補正法 41
第四章 實驗結果與分析 44
4.1 實驗設定與捨棄投入筆數分析 44
4.1.1 實驗設定 44
4.1.2 捨棄投入筆數分析 45
4.2 第一組實驗結果 46
4.2.1 孔徑部分實驗結果 46
4.2.2 聲波訊號部分實驗結果 48
4.3 第二組實驗結果 51
4.3.1 孔徑部分實驗結果 51
4.3.2 聲波訊號部分實驗結果 54
4.4 線性插值法補正結果 57
4.5 準確度分析 58
4.5.1 各孔孔徑預測補正準確度分析 58
4.5.2 整體刀具壽命預測補正準確度分析 59
第五章 結論與未來研究方向 61
5.1 研究結論 61
5.2 未來研究方向 62
參考文獻 63
附錄A 第一組實驗實際孔徑值21-120孔 (單位：mm) 67
附錄B 第一組實驗灰預測孔徑值21-95孔 (單位：mm) 68
附錄C第一組實驗實際頻率值21-120孔 (單位：Hz) 69
附錄D第一組實驗實際噪音峰值21-120孔 (單位：Pa) 70
附錄E第一組實驗實際噪音衝量值21-120孔 (單位：N*s) 71
附錄F第一組實驗頻率之灰預測值21-120孔(單位：Hz) 72
附錄G第二組實驗實際孔徑值21-145孔 (單位：mm) 73
附錄H第二組實驗灰預測孔徑值21-145孔 (單位：mm) 74
附錄I第二組實驗實際頻率值21-145孔 (單位：Hz) 75
附錄J第二組實驗實際噪音峰值21-145孔 (單位：Pa) 76
附錄K第二組實驗實際噪音衝量值21-145孔 (單位：N*s) 77
附錄L第二組實驗頻率之灰預測值21-145孔(單位：Hz) 78
附錄M第一組實驗孔徑補正預測值 (單位：mm) 79
附錄N第二組實驗孔徑補正預測值 (單位：mm) 80
附錄O 第一組實驗原始、灰預測、補正值孔徑比較表 81
附錄P 第二組實驗原始、灰預測、補正值孔徑比較表 82

圖目錄
圖1 研究流程圖 6
圖2 鑽孔加工示意圖 8
圖3 刀具磨耗與時間關係圖 12
圖4 實驗架構圖 29
圖5 鑽孔加工實驗與聲波感測器配置圖 31
圖6 孔徑測量實驗配置圖 31
圖7 電子顯微鏡與軟體操作介面 34
圖8 聲波訊號感測器軟體操作介面 34
圖9 刀具壽命灰色預測系統流程圖 40
圖10 系統流程圖 43

表目錄
表1 ISO標準公差表 10
表2 「灰色系統理論」、「機率統計論」、「模糊理論」差異表 17
表3 實驗加工參數設置表 33
表4 第一組灰關聯分析原始數據 35
表5 第二組灰關聯分析原始數據 36
表6 實驗設定表 44
表7 第一組實驗捨棄投入筆數分析 45
表8 第二組實驗捨棄投入筆數分析 45
表9 第一組實驗實際孔徑值21-100孔 (單位：mm) 46
表10 第一組實驗灰預測孔徑值21-85孔 (單位：mm) 46
表11 第一組孔徑灰預測滾動檢驗 47
表12 第一組孔徑灰預測滾動檢驗精準度表 47
表13 第一組實驗實際頻率值21-40孔 (單位：Hz) 48
表14 第一組實驗實際噪音峰值21-40孔 (單位：Pa) 49
表15 第一組實驗實際噪音衝量值21-40孔 (單位：N*s) 49
表16 第一組實驗聲波訊號灰關聯分析 49
表17 第一組實驗頻率之灰預測值21-140孔(單位：Hz) 50
表18 第一組高因子聲波訊號灰預測滾動檢驗 50
表19 第一組高因子聲波訊號灰預測滾動檢驗精準度表 51
表20 第二組實驗實際孔徑值21-135孔 (單位：mm) 52
表21 第二組實驗灰預測孔徑值21-145孔 (單位：mm) 52
表22 第二組孔徑灰預測滾動檢驗 53
表23 第二組孔徑灰預測滾動檢驗精準度表 53
表24 第二組實驗實際頻率值21-145孔 (單位：Hz) 54
表25 第二組實驗實際噪音峰值21-145孔 (單位：Pa) 54
表26 第二組實驗實際噪音衝量值21-145孔 (單位：N*s) 55
表27 第二組實驗聲波訊號灰關聯分析 55
表28 第二組實驗頻率之灰預測值21-145孔(單位：Hz) 55
表29 第二組高因子聲波訊號灰預測滾動檢驗 56
表30 第二組高因子聲波訊號灰預測滾動檢驗精準度表 56
表31 第一組實驗孔徑補正預測值 (單位：mm) 57
表32 第二組實驗孔徑補正預測值 (單位：mm) 57
表33 成對樣本t檢定結果 58
表34 MAPE評估表 59
表35 加工壽命誤差表 59
表36 補正前後MAPE比較 60

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