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.

參考文獻
1.A. Dutta and V. M. Nadkarni, “Identifying Critical Process Variable in Poly (Ethylene Terephthalate) Melt Spinning,” Textile Research Journal, Vol. 54, No. 1, pp. 35-42 (1984).
2.吳偉欽，”聚丙烯纖維染色性之探討”，絲織園地,第26期(1998)。
3.T. Kikutani, A. Takaku and J. Shimizu, “Nonisothermal Orientation-Induced Crystallization in Melt Spinning of Polypropylene,” Journal of Applied Polymer Science, Vol. 37, pp. 2686-2697 (1989).
4.J. S. Denton, J. A. Cuculo and P. A. Tucker, “Computer Simulation of High-Speed Spinning of PET,” Journal of Applied Polymer Science, Vol. 57, pp. 939-951 (1995).
5.S. Chen, W. Yu and J. E. Spruiell, “On-Line Studies of Structure Development During Melt Spinning of Poly (Butylenes Telephthalate),” Journal of Applied Polymer Science, Vol. 34, pp. 1477-1492 (1987).
6.K. F. Zieminski and J. Spruiell, “On-Line Studies and Computer Simulation of the Melt Spinning of Nylon-66 Filaments,” Journal of Applied Polymer Science, Vol. 35, pp.2223-2245 (1988).
7.Y. C. Bhuvanesh and V. B. Gupta, “Computer Simulation of Melt Spinning of Polypropylene Fibers Using a Steady-State Model,” Journal of Applied Polymer Science, Vol. 58, pp. 663-674 (1995).
8.鍾清章 校訂，“田口式品質工程導論”，中華民國品質管制學會發行(1989)。
9.R. S. Chen, H. H. Lee and C. Y. Yu, “Application of Taguchi Method on the Optimal Process Design of an Injection Model PC/PBT Automobile Bumper,” Composite Structures, Vol. 39, No. 3-4, pp. 209-214 (1997).
10.Y. S. Zu and S. T. Lin, “Optimizing the Mechanical Properties of Injection Molded W - 4.9% Ni - 2.1% Fe in Debinding,” Journal of Materials Processing Technology, Vol. 71, pp. 337-342 (1997).
11.C. Chen, P. Jen and F. S. Lai, “Optimization of the Coathanger Manifold via Computer Simulation and an Orthogonal Array,” Polymer Engineering and Science, Vol. 37, No. 1, pp. 188-196 (1997).
12.S. J. Liu, C. C. Lai and S. T. Lin, “Optimizing the Impact Strength of Rotationally Molded Parts,” Polymer Engineering and Science, Vol. 40, No. 2, pp. 473-480 (2000).
13.P. S. Wright, “Short-Time Fourier Transforms and Wigner-Ville Distributions Applied to the Calibration of Power Frequency Harmonic Analyzers,” IEEE Transactions on Instrumentation and Measurement, Vol. 48, No. 2, pp. 475-478 (1999).
14.鄭春生，“品質管理”，三民書局，台北 (2000)。
15.A. J. Duncan, “Quality Control and Industrial Statistics,” Irwin Book Company, Illinois (1986).
16.E. S. Page, “Continuous Inspection Schemes,” Biometrika, Vol. 41, pp. 100-115 (1954).
17.D. C. Montgomery, “Introduction to Statistical Quality Control,” 4th ed., John Wiley and Sons, New York (2001).
18.J. M. Lucas, “Combined Shewhart-CUSUM Quality Control Schemes,” Journal of Quality Technology, Vol. 14, pp. 51-59 (1982).
19.J. M. Lucas, and R. B. Crosier, “Fast Initial Response for CUSUM Quality-control Schemes: Give Your CUSUM a Head Start,” Technometrics, Vol. 24, pp.199-205 (1982).
20.S. W. Roberts, “Control Chart Tests Based on Geometric Moving Averages,” Technometrics, Vol. 1 , pp. 239-250 (1959).
21.T. Kohonen, “An Introduction to Neural Computing,” Springer, pp. 3-16 (1988).
22.G. A. Pugh, “Synthetic Neural Networks for Process Control”, Computers and Industrial Engineering, Vol.17, pp. 24–26 (1989).
23.R. S. Guh and J. D. T. Tannock, “Recognition of Control Chart Concurrent Patterns Using a Neural Network Approach,” International Journal of Production Research , Vol. 37 , pp. 1743–1765 (1999).
24.C. C. Chiu., M. K. Chen, and K. M. Lee, “Shifts Recognition in Correlated Process Data Using a Neural Network,” International Journal of Systems Science, Vol. 32, pp. 137–143 (2001).
25.R. S. Guh, “Robustness of the Neural Network Based Control Chart Pattern Recognition System to Non-normality,” International Journal of Quality and Reliability Management, Vol. 19, pp. 97–112 (2002).
26.R. S. Guh, “Integrating Artificial Intelligence Into On-line Statistical Process Control,” Quality and Reliability Engineering International, Vol. 19, pp. 1–20 (2003).
27.A. Hassan, M. S. N. Baksh, A. M. Shaharoun and M. Jamaluddin, “Improved SPC Chart Pattern Recognition Using Statistical Features,” International Journal of Production Research, Vol. 41, pp.1587-1603 (2003).
28.A. A. Zeki and M. S. Zakaria, “New Primitive to Reduce the Effect of Noise for Handwritten Features Extraction,” IEEE Tencon Proceedings: Intelligent Systems and Technologies for the New Millennium, pp. 24–27 (2000).
29.H. Utku, “Application of the Feature Selection Method to Discriminate Digitized Wheat Varieties,” Journal of Food Engineering, Vol. 46, pp.211–216 (2000).
30.A. S. Pandya and R. B. Macy, “Pattern Recognition with Neural Network in C++,”CRC, Florida (1996).
31.D. T. Pham and M. A. Wani, “Feature-based Control Chart Pattern Recognition,” International Journal of Production Research, Vol. 35, pp. 1875–1890 (1997).
32.G. Tontini, “Robust Learning and Identification of Patterns in Statistical Process Control Charts Using a Hybrid RBF Fuzzy Artificial Neural Network,” IEEE International Joint Conference on Neural Network Proceedings, Vol. 3, pp.1694–1699 (1998).
33.A. Ziabicki, “Fundamentals of Fiber Formation,” John Wiley, London (1976).
34.Z. Tadmor and C. G. Gogos, “Principle of Polymer Processing,” John, Wiley, New York (1979).
35.T. B. Barker, “Quality Engineering by Design Taguchi’s Philosophy,” Taguchi Methods Applications in World Industry,” Springer-Verlag New York (1989).
36.P. J. Ross, “Parameter Design,” Taguchi Techniques for Quality Engineering , McGraw-Hill (1996).
37.P. J. Ross, “Tolerance Design,” Taguchi Techniques for Quality Engineering , McGraw-Hill (1996).
38.R. N. Kackar, “Off-line Quality Control, Parameter Design,” Quality Control Robust Design, Edited by K. Dehnad ,Wadsworth (1989).
39.R. H. Lochner and J. E. Matar, “Taguchi Inner and Outer Arrays,” Designing for Quality-An Introduction to the Best of Taguchi and Western Methods of Statistical Experimental Design. Kraus Organization Limited. (1990).
40.P. J. Ross, “The Design of Experiment Process,” Taguchi Techniques for Quality Engineering, McGraw-Hill Inc. (1996).
41.W. Y. Fowlkes and D. M. Creveling, “Introduction to Quality Engineering,” Engineering Methods for Robust Product Design-Using Taguchi Methods in Technology and Product Development, Addison-Wesley Publishing Company (1995)
42.G. Box, S. Bisgaard and C. Fung, “An Explanation and Critique of Taguchi’s Contribution to Quality Engineering,” Taguchi Methods Applications in World Industry, Springer-Verlag, New York (1989).
43.P. J. Ross, “Analysis and Interpretation Methods for Experiments,” Taguchi Techniques for Quality Engineering , McGraw-Hill (1996).
44.P. J. Ross, “Orthogonal Array Selection and Utilization,” Taguchi Techniques for Quality Engineering, McGraw-Hill. (1996).
45.王志能，“計量值管制圖之探討與電腦化”，台灣科技大學工業管理研究所碩士論文，(2002)。
46.C. L. Hong, C. Y. Lin and J. Y. Lai, “Improvement of a Dimensional Measurement Process Using Taguchi Robust Designs,” Quality Engineering, Vol. 9, No. 4, pp. 561-573 (1997).
47.官生平，“ 簡易SPC 統計製程管制”，中華民國品質學會，(2000)。
48.D. C. Montgomery, “Introduction to Statistical Quality Control,” John Wiley, New York (2004).
49.Western Electric, “Statistical Quality Control Handbook,” Western Electric Company (1958).
50.房克成、林清風，“ 管制圖與製程管制 ”，中華民國品質學會， (2004)。
51.A. Hassan, M. S. N. Baksh, A. M. Shaharoun and H. Jamaluddin, “Improved SPC Chart Pattern Recognition Using Statisitical Features,” International Journal of Production Research, Vol.41, No.7, pp.1587-1603 (2003).
52.S. K. Gauri and S. Chakraborty, “Feature-Based Recognition of Control Chart Patterns,” Computers and Industrial Engineering (2006).
53.A. Hassan, M. S. Baksh, A. M. Shaharoun and H. Jamaluddin, “Feature Selection for SPC Chart Pattern Recognition Using Fractional Factorial Experimental Design,” Faculty of Mechanical Engineering, University Teknologi Malaysia (2003).
54.D. T. Pham and M. A. Wani, “Feature-Based Control Chart Pattern Recognition,” International Journal of Production Research, Vol. 35, No. 7, pp.1875-1890 (1997).
55.K. Lavangnananda and A. Piyatumrong, “Image Processing Approach to Features Extraction in Classification of Control Chart Patterns,” IEEE Mid-Summer Workshop on Soft Computing in Industrial Applications Helsinki University of Technology, pp. 28-30 (2005).