臺灣博碩士論文加值系統

摘要
本論文將對熔融紡織機台去診斷張力異常作研究，採用聚丙烯作為機台材料，機台加工參數共有十個因子，分別是螺桿三段加熱溫度、齒輪幫浦溫度、模頭溫度、紡嘴溫度、螺桿轉速、齒輪幫浦轉速、冷卻速度與捲取速度。實驗首先透過田口實驗規劃法和變異數分析，在張力值變異值望小特性設定下，經過計算得到該機台之張力變異最小之最佳參數條件，之後量取每個因子在不同的加工參數條件設定下之張力值，利用相同加工參數條件重覆作十組實驗，觀察每個因子張力變異情況，在十組中該因子張力變異值若超過偏離最佳參數之變異範圍三次，則判定該因子張力值異常，否則該因子張力值屬於在正常範圍內。在分類器方面，將利用倒傳遞類神經來做分類，並經過觀察統計製程管制圖，選用三個特徵值來做分析，分別是平均距離(Average distance, RDIST)、偏態係數(Skewness)和ALSLSC(Area between the pattern and least square line per Least square line crossover expressed in terms of standard deviation)，因此以此三種特徵值當作類神經網路之輸入層，而加工參數中的七個顯著因子作為輸出層，其辨識成功率能達到百分之百。為了達到熔融紡織機台異常診斷的完整性，我們運用單因子分類結果和倒傳遞類神經網路建構一套雙因子分類流程，經由多次使用倒傳遞類神經，並在分類時排除已調整過之因子，辨識熔融紡織機台之異常雙因子，實驗結果證實，我們所提出的方法可以成功辨識各種異常狀況。

Abstract
This thesis forces on diagnosing the fault tension in the melting spinning processing. We use the material of polypropylene (PP) in experiments and there are ten processing parameters, including three section extruder barrel temperatures, die temperature, metering pump temperature, spinning temperature, metering pump speed, the formation speed, cooling air speed and take-up velocity. First we find the best processing parameters to give the smaller tension variance by using the Taguchi method and the analysis of variance (ANOVA). Then, we measure the spinline tension of every factor in different processing parameter conditions, ten times for each condition. If the tension variance of spinline have three times over the normal range in ten experiments, we consider this tension is abnormal; Otherwise it is normal. In addition, we use statistical processing control (SPC) to choose three feature values and the back-propagation neural network (BPNN) to classify the fault processing parameters. The features include the average distance (RDIST), ALSLSC (Area between the pattern and least square line per least square line crossover expressed in terms of standard deviation) and skewness. The output layer has seven significance factors. The recognition rate can reach 100%. In order to complete the diagnosis system, we present procedures for classifying two factors in abnormal conditions we identify one of the two factors using the classifier for single factor, and locate the other with elimination of the identified factor from the neural network. The experiment results show that the proposed method can successfully classify the fault processing parameters in the melt spinning machines.

目次
摘要 I
ABSTRACT III
致謝 IV
目次 V
表目錄 VIII
圖目錄 IX
第1章 緒論 1
1.1 研究動機和目的 2
1.2 文獻回顧 3
1.3 論文架構 10
第2章 熔融紡絲機台 12
2.1 機台和硬體架構 13
2.2 紡絲加工延伸過程 15
第3章 研究方法 16
3.1 田口實驗規劃法 16
3.1.1 品質設計三階段 17
3.1.2 實驗參數種類 20
3.1.3 田口實驗步驟 23
3.1.4 直交表 26
3.1.5 信號雜訊比(Signal to noise ration) 28
3.1.6 變異係數分析(ANOVA) 29
3.1.7 確認實驗 31
3.2 統計製程管制 33
3.2.1 共同原因和特殊原因 35
3.2.2 管制圖(Control of charts) 36
3.2.3 管制圖圖形趨勢的種類 42
3.2.4 管制圖辨識方法 43
3.3 類神經網路 47
3.3.1 類神經網路特性 48
3.3.2 類神經網路分類 49
3.3.3 非線性轉換函數 53
3.3.4 倒傳遞類神經網路 57
第4章 實驗結果與分析 65
4.1 田口實驗結果分析 66
4.1.1 分析因子張力值之變異 75
4.2 選用管制圖特徵值 78
4.2.1 單因子分析 80
4.2.2 雙因子分析 82
4.3 倒傳遞類神經網路 84
4.3.1 單因子分群 84
4.3.2 雙因子分類流程 88
4.3.3 分類結果與討論 93
第5章 結論 99
5.1 研究結果討論 99
5.2 未來發展 99
參考文獻 101

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