本論文利用微型混煉機探討低密度聚乙烯與球形二氧化矽兩種材料混煉之最佳均勻度加工參數，並藉由混煉時之扭力、黏度及剪力三數據辨識正常與異常參數。經由多項文獻得知，二氧化矽顆粒對於塑料有增強性能之作用，且顆粒在塑料中之均勻度越佳時機械性質有越優良的表現，而現今研究人員在探討材料之均勻度時，皆僅以平均粒徑或SEM分析其均勻度，主觀給予其結論，能有公正與客觀的給予定義是非常重要的議題。研究為了解決此問題，將均勻度分為分布度及分散度兩項品質做分析，藉由EDS之檢測並利用變異係數評估分布度；藉由SEM之圖譜以表面效應及比表面公式評估分散度，再以田口方法之直交表設計實驗組，經由變異分析獲得單品質最佳加工參數，最後利用層級分析法獲得多品質最佳參數組，經過信賴區間測試及最後多品質加工參數之結果，可以驗證本研究中所使用的分布度及分散度公式具有成效。最後將多品質最佳參數做為標準參數做單項因子上的水準變動，經由支撐向量機辨識異常參數與最佳參數，最終能從其直接辨識加工參數有無異常，而省去檢測品質之成本。

This thesis investigates optimal parameters of comprehensive quality of mixing of Low Density Polyethylene (LDPE) and Spherical Silica Powder (SiO2) in a micro compounder, and to Identify the faults of parameter by the melt viscosity, screw torque and shear stress data during mixing. Through a number of literatures, it is known that SiO2 have an effect of enhancing mechanical performance of plastics, and the better the uniformity of the SiO2 particles in plastics, the better the mechanical performance. Nowadays, when investigating the uniformity of materials, researchers only analyze the uniformity by average particle size or SEM. Nowadays, when investigating the uniformity of materials, researchers only analyze the uniformity by average particle size or SEM. Researchers subjectively give their conclusions. It is very important to have a fair and objective definition. In this thesis, the uniformity is divided into two categories: distribution and dispersion. Distribution is evaluated by coefficient of variation and the data of EDS. Dispersion is evaluated by specific surface formula and the photos of SEM. Firstly, using the Taguchi's orthogonal arrays to design experiment group, the best quality parameter combination is obtained through the Analysis of Variance (ANOVA) and F-test. Secondly, founding the optimal parameters of comprehensive quality by using the Analytic Hierarchy Process (AHP). After the confidence interval test result and the final uniformity performance, it can be known that the distribution and dispersion formula used in this thesis is effective. Thirdly, changing of the single factor from optimal parameters of comprehensive quality, and investigating the relationship between online data during the mixing and the parameters. Finally, the abnormal parameters are distinguished from the normal parameters by the Support Vector Machine (SVM). From the result, we can directly exclude the defective products of abnormality of the processing parameters and omit the cost of detecting the quality.

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