本論文以聚酯及聚丙烯纖維為原料，使用羅拉式梳棉機、摺疊機及針軋機來製造針軋非織物，品質特性為非織物之拉伸強力(縱、橫向)及撕裂強力(縱、橫向)，文中利用田口實驗計劃法做參數設計，針對會影響拉伸強力及撕裂強力結果之刺針排列方式、纖維種類、纖維喂入量、摺疊機擺動速度、摺疊機輸送速度、針軋深度、針軋密度、刺針型號，作為實驗的控制因子，並選用L18(21×37)直交表進行實驗，同時配合變異數分析法來找出顯著因子及最佳製程條件。本實驗希望非織物的拉伸強力及撕裂強力愈大愈好，所以選用望大特性作為目標特性，並以灰色關聯度分析法結合田口實驗計劃法以獲得多重品質特性之最佳製程參數。再以確認實驗之計算來檢驗實驗之再現性。實驗結果顯示，本文所得到的最佳條件的非織物拉伸及撕裂強力皆符合95%的信賴區間，具有可再現性。最後，將控制因子設為倒傳遞類神經網路之輸入參數，而拉伸強力及撕裂強力設為輸出參數，以建構針軋非織物之預測系統，其預測誤差率在5% 以內，表示本預測系統有良好的預測能力。

In this thesis polyester fiber and polypropylene fiber are adopted as the materials. The roller card, cross-lapper machine, and needle punching machine are used to manufacture the needle punching nonwoven fabrics. The characteristics are the tensile strength (longitudinal and transverse) and tear strength (longitudinal and transverse), and utilize the Taguchi experimental method in the thesis. These parameters include arrangement of needles in the needle board, the sort of fiber material, the amount of fiber feed, the swing speed of the cross-lapper machine, the conveyor speed of the cross-lapper machine, penetration depth, needling density, and the size of the needles. They are considered as the control factors, which can affect the results of the tensile strength and tear strength. The L18(21×37) orthogonal array together with the Analysis of Variance (ANOVA) approach are employed to find the significant parameters and the optimum process conditions. In the experiment, the maximum tensile strength and tear strength of the nonwoven fabrics, the better. Therefore the larger-the-better target characteristic is chosen. Grey relationship analysis combined with Taguchi experimental design method is applied to get the optimal processing parameter of multiple quality characteristics, and the five confirmation experiments are performed. The tensile strength and tear strength of the nonwoven fabrics in optimum conditions are corresponded with 95% confidence interval. This reveals that experiments are reproducible. Finally, the control factors are taken as the inputs of Artificial Neural Network (ANN) and the tensile strength and tear strength are taken as the outputs of ANN for implementing the prediction system of needle punching nonwoven fabrics. The percentage errors of prediction are all within 5%. This indicates that the prediction system constructed has precision forecast capability.

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