English  |  正體中文  |  简体中文  |  Items with full text/Total items : 62830/95882 (66%)
Visitors : 4057486      Online Users : 757
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: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/125024


    Title: Study on Machine identification and its effect on the RSM Optimization in Injection Molding.
    Authors: Xu, Rui-ting;Wang, Tsung-han;Huang, Chao-tsai;Chen, Po-hsuan;Jong, Wen-ren;Chen, Shia-chung;Chang, Rong-yeu;Hsu, David
    Keywords: Injection molding;machine identification;optimization
    Date: 2022-06-14
    Issue Date: 2024-01-30 12:05:34 (UTC+8)
    Publisher: Society of Plastic Engineers (SPE)
    Abstract: Different optimization methods or strategies have been proposed and utilized to enhance the quality of injected products for many years. However, what is the machine characteristics to influence the efficiency of the optimization method? It is not fully understood yet. In this study, the injection machine characteristics has been identified using numerical simulation (Moldex3D) based on a round plate system. The response surface method (RSM) was further utilized for both simulation prediction and experimental conduction to discuss the efficiency of the optimization for operation parameters in injection molding. Results showed that before the machine identification and calibration, the quality of injected part can be improved by 75% theoretically. At the same time, the real experimental system demonstrated worse result. However, the difference between simulation and experiment has the same amount no matter the system has been optimized or not through RSM method. Moreover, after the machine identified and calibrated, the difference between simulation prediction and experimental observation has been improved by 71.4%. Also, the accuracy of the RSM optimization in the real experiment has been enhanced by 50% (from -0.06 mm to 0.03 mm). Obviously, it showed that the machine identification for the real capability is very important.
    Relation: SPE Technical Papers, ANTEC2022
    Appears in Collections:[Graduate Institute & Department of Chemical and Materials Engineering] Proceeding

    Files in This Item:

    There are no files associated with this item.

    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