English  |  正體中文  |  简体中文  |  Items with full text/Total items : 58243/91820 (63%)
Visitors : 13792507      Online Users : 56
RC Version 7.0 © Powered By DSPACE, MIT. Enhanced by NTU Library & TKU Library IR team.
Scope Tips:
  • please add "double quotation mark" for query phrases to get precise results
  • please goto advance search for comprehansive author search
  • Adv. Search
    HomeLoginUploadHelpAboutAdminister Goto mobile version
    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/118166


    Title: 應用類神經網路與基因演算法於鎂合金與銅異種金屬銲接參數最佳化之研究
    Authors: 張志平;羅惠瓊;朱佳荷
    Keywords: ;鎂合金;氬銲;類神經網路;基因演算法;copper;magnesium alloy;TIG;artificial neural network;genetic algorithm
    Date: 2019-12-30
    Issue Date: 2020-03-05 12:10:51 (UTC+8)
    Publisher: 品質學報
    Abstract: 由於質量輕高強度的鎂合金與高導電的銅金屬具有不同優越特性,導致兩者的接合在現今工業應用上非常普及且重要,而氬銲則是非常方便普及的接合技術。然而,在銲接過程中,鎂合金與銅的熔點分別為1,083℃與649℃差異甚大,且其可銲性之條件範圍狹窄會有接合介面和融熔銲道形成硬而脆的介金屬化合物的困難點。此外,一般對於銲接參數設定並沒有公式可循,完全憑藉專家過去的知識和經驗來設定,一旦超出專家經驗範圍,便無法有效設定最佳參數,容易造成銲接品質不佳。有鑑於此,本研究將發展一套經濟且有效之多品質特性田口實驗設計方法,解決大量連續型參數及水準之多重品質特性實驗設計問題,並利用理想解類似度順序偏好法(technique for order preference by similarity to ideal solution, TOPSIS)與類神經網路(artificial neural network, ANN)訓練最佳化參數設計函數架構,結合基因演算法(genetic algorithm, GA)的柔性演算法(soft computing, SC)搜尋最佳參數組合,找出銅與鎂合金異種金屬板材銲接最佳化參數組合。
    Relation: 品質學報 26(6),頁381-394
    DOI: 10.6220/joq.201912_26(6).0003
    Appears in Collections:[Graduate Institute & Department of Business Administration] Journal Article

    Files in This Item:

    File Description SizeFormat
    index.html0KbHTML64View/Open

    All items in 機構典藏 are protected by copyright, with all rights reserved.


    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library & TKU Library IR teams. Copyright ©   - Feedback