臺灣博碩士論文加值系統

隨著電腦數值控制(Computer Numerical Control, CNC)的誕生，與材料科技之進步，進而使全球各地的研發精密製造工程與技術不斷精進，且製造工業得以突飛猛進，現今已全面帶動產業邁向工業自動化；然而，CNC設備目前已廣泛運用於各項產業，而各產業多以降低成本、減少浪費以提高生產效率和利潤，來幫助企業達到其目標與願景並增進競爭力，此時在正確的時候做正確的事情以及減少浪費便成了各企業所追求的首要目標。為達成此一目標，品質管理為一不可或缺之方法，對於品質管理中之品質量測，其過程總是耗時費力，為了能減少品質量測之時間，近年來，即時品質量測系統的觀念及研究逐漸被應用與開發。
於CNC加工中，鑽孔加工為一個最基本且被普遍運用的加工方式，卻少有研究探討其品質量測，因目前業界上對鑽孔表面粗糙度的測量多數是採用離線量測及破壞式檢驗，此類檢驗會使成本及時間提高，如果在鑽孔作業時能精確掌握影響粗糙度之因子就能有效控制產品的鑽孔表面粗糙度，再加以發展出一套即時預測系統，可使檢驗之成本大幅下降同時也縮短檢驗之時間；為了能使即時全面品質量測(In-process 100% inspection)系統運用在鑽孔加工中，本研究的目的在於發展結合感測技術與預測系統的CNC鑽孔表面粗糙度即時預測系統。此系統加入機器之加工參數以及力量感測器的訊號作為輸入因子，並經由類神經網路的資料處理來訓練連結權重，並加以比較有無加入感測技術之預測精確度，最後再將上述的預測模型整合成一個精確度較高的鑽孔表面粗糙度即時預測系統。此系統經由不斷的學習與測試，即可達到即時全面品質量測之概念，進一步來協助企業減少時間與金錢之浪費，使其增進產業之競爭力。
研究結果顯示，相關影響因子經由倒傳遞類神經網路訓練後，證明從力量感測器所收集的鑽削力訊號能有效的預測鑽孔表面粗糙度，然後使用田口方法找尋網路參數最佳化的組合，以達到可從力量感測器所呈現的訊號做即時的回饋。

With the invention of the Computer Numerical Control (CNC) and development of the material technology, the engineering of advanced manufacturing is greatly improved. These advantages accelerate the development of manufacturing industry, which becomes a stable foundation of the automation. However, CNC nowadays are widely used in different kinds of industries, which mainly focus on how to minimize the cost and maximize the production and profit. These strategies play important roles in reaching entrepreneur’s goals and visions. At this point, a right decision making at a right time and the reduction of waste are the main benchmarks of many companies. To achieve the benchmarks, quality management is the key factor. However, the inspection of quality control always takes time. To shorten the process time, the idea of “In-process Quality Monitoring System” has been applied and developed.
In CNC operations, drilling is one of the most basic and common operations. However, there are few researches studying the quality measurement of this part. Presently, the manufacturing industry conducts off-line inspection to examine the surface roughness of drilling. The off-line method needed a lot of time with high cost. The surface roughness can be effectively controlled if the influencing factors can be precisely acquired. With a new in-process prediction system, the inspection cost is reduced and so the time is shortened as well. To fit the “In-process 100% inspection” system in the drilling operations, the purpose of this research is to combine the Sensing Technology and the CNC in-process prediction system of surface roughness.
This system inputs the machining parameters and the signal from force sensor as the factors. A neural network is applied to construct the prediction model of the system. Then we compare the accuracy of the system with the other prediction system without sensing technology. With repetitive training and testing, the system can reaches the idea of total quality measurement which can assist the entrepreneurs to reduce the cost and shorten the lead time.
The result indicates that the related influencing factors under Back Propagation Network (BPN) training prove that the cutting signal from the force sensor can be used to effectively predict the surface roughness in drilling operations. This study uses Taguchi method to find the optimal set of the network variables for BPN training, which allows the operator to immediately response via the signal from the sensor.

目錄
摘要 I
Abstract . II
誌謝 III
目錄 IV
圖目錄 VI
表目錄 . VII
第一章 緒論 . 1
1.1 研究背景 .. 1
1.2 研究動機 .. 2
1.3 研究目的 .. 3
1.4 研究限制 .. 4
1.5 研究假設 .. 4
1.6 研究架構 .. 5
第二章 文獻探討 .. 7
2.1 鑽削理論 .. 7
2.1.1 鑽削原理 7
2.1.2 鑽削加工文獻回顧 .. 8
2.2 表面粗糙度 . 9
2.2.1 表面粗糙度的定義 10
2.2.2 影響表面粗糙度之重要因子 .. 12
2.3 感測技術 14
2.4 類神經網路 .. 16
2.4.1 類神經網路簡介 . 16
2.4.2 類神經網路模式 . 20
2.5 田口方法 22
2.5.1 田口方法原理 .. 22
第三章 研究方法 24
3.1 鑽削力 . 24
3.1.1 鑽削力之介紹 .. 24
3.1.2 鑽削力分析 25
3.1.3 力量感測器之分析 25
3.2 倒傳遞類神經網路 .. 27
3.3 田口方法 30
3.3.1 損失函數 . 30
3.3.2 品質特性值的種類 31
3.3.3 直交表 .. 31
3.3.4 設計水準表及變異數分析 32
3.4 定義目標函數與品質特性 .. 33
第四章 預測系統之建置及實驗結果 . 34
4.1 實驗設備建置 . 35
4.1.1 硬體設備 . 35
4.1.2 軟體設備 . 36
4.2 原始數據蒐集 . 37
4.3 相關係數分析 . 38
4.4 倒傳遞類神經網路預測系統 . 39
4.4.1 倒傳遞類神經網路資料的前處理 40
4.4.2 倒傳遞類神經網路的建構 41
4.4.2.1 預測系統之網路結構選擇 . 41
4.4.2.2 預測系統之有無鑽削力因子比較 . 47
第五章 結論與未來研究方向 . 51
5.1 研究結論 51
5.2 未來研究方向 . 51
參考文獻 53
附錄A 類神經網路100 筆原始數據表 .. 60
附錄B 相關分析15 筆原始數據表 .. 63
附錄C 隨機50 筆測試資料表 . 64

圖目錄
圖1-1 研究流程圖 . 6
圖2-1 鑽孔加工示意圖 .. 7
圖2-2 表面輪廓包含了粗糙度曲線與波浪起伏的曲線 .. 10
圖2-3 表面粗糙度測量方式 . 12
圖2-4 生物神經元 .. 17
圖2-5 人工類神經模型 17
圖2-6 前饋式類神經網路架構：(a)前饋式、(b)回饋式 . 21
圖2-7 學習演算法的流程說明圖 .. 21
圖3-1 刀具作用於工件的力 . 24
圖3-2 鑽削力之分析 . 24
圖3-3 鑽孔鑽削力之實際狀況示意圖 26
圖3-4 倒傳遞網路架構圖 .. 28
圖3-5 倒傳遞網路學習示意圖 29
圖4-1 預測系統實驗架構圖 . 34
圖4-2 本研究之實驗建置 .. 36
圖4-3 轉換函數：(a)非線性、(b)線性 .. 42
圖4-4 未含鑽削力因子之田口實驗平均值回應圖 . 45
圖4-5 未含鑽削力因子之田口實驗SN 值回應圖 .. 45
圖4-6 含鑽削力因子之田口實驗平均值回應圖 .. 46
圖4-7 含鑽削力因子之田口實驗SN 值回應圖 47
圖4-8 鑽孔表面粗糙度預測系統之網路結構 49

表目錄
表2-1 鑽孔加工相關研究與應用 . 8
表2-2 各種表面粗糙度的定義 10
表2-3 表面粗糙度相關研究與應用 . 13
表2-4 感測技術相關研究與應用 .. 15
表2-5 類神經網路相關研究與應用 . 19
表2-6 田口方法相關研究與應用 .. 22
表4-1 10.0(mm)HSS 鑽頭之鑽削條件 37
表4-2 12.0(mm)HSS 鑽頭之鑽削條件 37
表4-3 鑽孔加工條件之範圍 . 37
表4-4 部分原始資料(詳細資料請參閱附錄A) . 38
表4-5 相關分析原始數據(詳細資料請參閱附錄B) .. 38
表4-6 相關分析正規化後數據 39
表4-7 因子與Ra 之相關係數排序表 .. 39
表4-8 機器加工參數表 40
表4-9 類神經網路正規化前之數據範例 .. 40
表4-10 類神經網路正規化後之數據範例 .. 41
表4-11 田口實驗之因子與水準設定表 43
表4-12 L9(32)直交表 . 43
表4-13 未含鑽削力因子之網路訓練結果及SN 值 .. 44
表4-14 未含鑽削力因子之實驗水準對應SN 值 45
表4-15 含鑽削力因子之網路訓練結果及SN 值 46
表4-16 含鑽削力因子之實驗水準對應SN 值 . 47
表4-17 含鑽削力因子與未含鑽削力因子之兩樣本假設檢定 48
表4-18 輸入層與第一層隱藏層之權重與偏權值 .. 50
表4-19 第一層與第二層隱藏層之權重與偏權值 .. 50
表4-20 第二層隱藏層與輸出層之權重與偏權值 .. 50

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