博碩士論文 953203016 完整後設資料紀錄

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DC.contributor機械工程學系zh_TW
DC.creator洪嘉維zh_TW
DC.creatorJia-Wei Hongen_US
dc.date.accessioned2008-6-29T07:39:07Z
dc.date.available2008-6-29T07:39:07Z
dc.date.issued2008
dc.identifier.urihttp://ir.lib.ncu.edu.tw:444/thesis/view_etd.asp?URN=953203016
dc.contributor.department機械工程學系zh_TW
DC.description國立中央大學zh_TW
DC.descriptionNational Central Universityen_US
dc.description.abstract本研究以添加15wt.%玻璃纖維的強化聚縮醛(POM/15wt.%GF)工程塑膠為對象,探討其射出成型製品之準靜態拉伸性質、磨耗性質及衝擊性質等三項,首先利用田口實驗設計配合變異數分析法及類神經網路法,針對單一品質特性進行製程最佳化分析,其次,利用田口方法結合主成份分析及類神經網路分別來進行多重品質特性考量下之最佳化製程條件預測,並比較兩方法預測結果之差異。 重要研究結果如下:在單一品質特性的考量下,準靜態拉伸性質的最大抗拉強度之最佳成型條件為充填時間1.5 s、熔膠溫度200℃、模具溫度60℃及保壓壓力55 MPa;而對其影響最為顯著的製程條件為熔膠溫度(貢獻度達81.37%)。而在同時考量到抗拉強度、摩擦係數及衝擊吸收能量等多重品質特性下,其最佳化製程條件為充填時間1 s、熔膠溫度200℃、模具溫度75℃及保壓壓力55 MPa;而對其影響最為顯著的製程條件為熔膠溫度(貢獻度達42.9%)。zh_TW
dc.description.abstractThis study investigates the influence of injection molding process parameters on the tensile properties, wear properties and impact toughness of 15wt.% Glass Fiber Reinforced Polyoxymethylene (POM/15wt.%GF). The author combined the Taguchi experimental design, analysis of variance (ANOVA) and artificial neural network to optimize the manufacturing process. Then, the Taguchi experimental design, principal component analysis (PCA) and Artificial Neural Network were utilized to evaluate a comprehensive index for multi-response cases. The results of Taguchi experiment design and ANOVA showed that the optimum manufacturing parameters was filling time 1.5 s, melting temperature 200℃, mold temperature 60℃ and packing pressure 55 MPa for tensile properties. Melting temperature was the most significant parameter among them. When we consider the multi-response of tensile strength, friction coefficient and impact absorb energy, the analytical results of Taguchi experiment design and PCA showed that the optimum manufacturing parameters was filling time 1 s, melting temperature 200℃,mold temperature 75℃ and packing pressure 55 MPa. Also, melting temperature was the most significant parameter.en_US
DC.subject類神經網路zh_TW
DC.subject射出成型zh_TW
DC.subject玻璃纖維強化聚縮醛zh_TW
DC.subject機械性質zh_TW
DC.subject製程最佳化zh_TW
DC.subjectInjection Moldingen_US
DC.subjectMechanical Propertyen_US
DC.subjectGlass Fiber Reinforced Polyoxymethyleneen_US
DC.subjectArtificial Neural Networken_US
DC.subjectManufacturing Process Optimizationen_US
DC.title短玻璃纖維強化聚縮醛射出成型條件最佳化與機械性質之研究zh_TW
dc.language.isozh-TWzh-TW
DC.titleOptimizing Injection Molding Conditions and Mechanical Properties of Glass Fiber Reinforced Polyoxymethyleneen_US
DC.type博碩士論文zh_TW
DC.typethesisen_US
DC.publisherNational Central Universityen_US

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