Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/1534
DC FieldValueLanguage
dc.contributor.advisor范光堯zh_TW
dc.contributor.advisorKJ Fannen_US
dc.contributor.author楊景欽zh_TW
dc.contributor.authoryang, jimchingen_US
dc.date2002zh_TW
dc.date.accessioned2014-06-05T11:41:04Z-
dc.date.available2014-06-05T11:41:04Z-
dc.identifier.urihttp://hdl.handle.net/11455/1534-
dc.description.abstract本研究採用類神經網路之誤差逆傳遞法,配合有限元素模擬分析結果,建立型材矯直預測系統,以達到只須於矯直前量測型材平直度就可藉型材矯直預測系統,預測得知矯直位置、方向、衝頭下壓量,並於矯直後達到一定平直度精度。此方法是先以有限元素模擬矯直過程並將分析結果做為類神經網路訓練樣本,以建立類神經網路型材矯直預測系統。本研究中實際將型材矯直預測系統運用於半自動化壓機矯直機,以驗證說明本研究所提出類神經網路運用於型材矯直預測可行性。本研究所嘗試使用類神經網路運用於型材矯直預測,其結果應可提供業界在型材矯直應用開發上的參考。zh_TW
dc.description.abstractThe application of artificial neural networks of error-back-propagation method and finite element method for simulation results of straightening process are to build prediction system of profile materials straighten. This system used to measure straightness of profile materials can obtain the straightening position, direction, and punch travel and profile materials finally reach required straightness. This system serves results of finite element method simulated straighten process as training patterns for neural networks and build prediction system of profile materials straighten. Actually use this system on straighten machine of automation to straighten profile materials and verify mention of neural networks that could be available for predict process of profile materials straighten. This study attempted to apply neural networks to predict process of profile materials straighten and the results can be provide the reference to develop of profile materials straighten in industry.en_US
dc.description.tableofcontents目錄 中文摘要 I 英文摘要 II 目錄 III 圖目錄 VI 表目錄 IX 符號說明 X 1前言 1 2文獻回顧 2 2.1型材矯直的必要性 2 2.2矯直方法 3 2.2.1輥輪矯直法 3 2.2.2回轉矯直法 5 2.2.2.1兩滾輪式 6 2.2.2.2多滾輪式 7 2.2.3壓機矯直法 8 2.3矯正的規範 10 2.4相關研究 12 3類神經網路 15 3.1類神經網路理論 16 3.1.1類神經網路定義 16 3.1.2類神經網路分類 17 3.2類神經網路基本架構 18 3.2.1處理單元 18 3.2.2層 20 3.2.3網路 21 3.3誤差逆傳遞類神經網路演算法 22 3.3.1網路學習過程 22 3.3.2網路預測過程 25 3.3.3網路參數的設定 27 3.3.3.1隱藏層層數 27 3.3.3.2隱藏層處理單元個數 27 3.3.3.3學習速率 28 4研究目的與方法 29 5矯直系統與機構的建立 30 5.1硬體設備 32 5.2軟體 36 5.3有限元素分析 36 5.4矯直預測系統建立 41 6實例 44 6.1實驗流程 44 6.2矯直機剛性曲線量測 46 6.3 16×16mm SUS304彎曲矯直 49 6.4 16×12mm SUS304彎曲矯直 53 6.5 16×14mm SUS304彎曲矯直 56 6.6 重覆矯直之影響 60 7結論與未來展望 62 8參考文獻 63 附錄A 矯直機構設計 66 附錄B 雷射位移計原理與規格 72 附錄C有限元素分析驗證 76 誌謝zh_TW
dc.language.isoen_USzh_TW
dc.publisher機械工程學系zh_TW
dc.subjectstraightenen_US
dc.subject矯直zh_TW
dc.subjectneural networksen_US
dc.subjecterror-back-propagation methoden_US
dc.subjectfinite element methoden_US
dc.subject類神經網路zh_TW
dc.subject誤差逆傳遞法zh_TW
dc.subject有限元素法zh_TW
dc.title類神經網路運用於型材矯直之研究zh_TW
dc.titleA Study of Profiles Material Straightness by Applying Artificial Neural Networksen_US
dc.typeThesis and Dissertationzh_TW
item.fulltextno fulltext-
item.languageiso639-1en_US-
item.openairetypeThesis and Dissertation-
item.cerifentitytypePublications-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.grantfulltextnone-
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