跳到主要內容

簡易檢索 / 詳目顯示

研究生: 辛維朗
Wei-Lang Hsin
論文名稱: 結合反應曲面法與蟻群演算法探討尼龍6之纖維複合材料射出成型縫合線強度最佳化
Optimization of weld line strength for injection molding process parameters of Nylon 6 fiber composites using response surface methodology coupled with ant colony optimization
指導教授: 鍾禎元
Chen-Yuan Chung
口試委員:
學位類別: 碩士
Master
系所名稱: 工學院 - 機械工程學系
Department of Mechanical Engineering
論文出版年: 2023
畢業學年度: 111
語文別: 中文
論文頁數: 80
中文關鍵詞: 縫合線反應曲面法蟻群演算法
外文關鍵詞: Weld line, Response surface methodology, Ant colony optimization
相關次數: 點閱:12下載:0
分享至:
查詢本校圖書館目錄 查詢臺灣博碩士論文知識加值系統 勘誤回報
  • 本研究透過反應曲面法結合蟻群演算法,建立製程參數與品質特性之
    回歸模型並進行最佳化搜索,以獲得最佳製程參數,提升材料之縫合線強度。以15%短碳纖維含量之尼龍6 作為射出材料,並使用Moldex3D 與
    SolidWorks 進行射出成型模具設計,再來使用Box-Behnken 設計法規劃實驗並建構數學模型,並將其作為蟻群演算法之適應函數,最後各別以反應曲面法與蟻群演算法求解製程最佳化問題,探討尼龍6 纖維複合材料於射出成型製程中,不同的製程參數對於其縫合線強度之影響,並比較反應曲面法與蟻群演算法兩種最佳化方法之優劣。
    實驗結果顯示,縫合線強度受到熔膠溫度的影響最為顯著,熔膠溫度越高,縫合線之結合性越好,使其抗拉強度有明顯的提升。反應曲面法最佳化之預測與實驗誤差為1.53%,縫合線強度改善率為3.89%;另一方面,蟻群演算法最佳化之預測與實驗誤差為0.68%,縫合線強度改善率為4.54%,根據上述結果表示,蟻群演算法之預測誤差較小,且縫合線強度改善率較高,代表其最佳化製程參數之能力較佳,但綜觀結果來看,反應曲面法與蟻群演算法都能夠有效地最佳化製程參數,提高縫合線強度。
    本研究成功地利用CAD 與CAE 工具完成模具設計,有效降低試模成
    本,並成功地結合反應曲面法與蟻群演算法,找出最佳的製程參數組合,提
    升尼龍6 纖維複合材料之縫合線強度,降低實驗成本並節省時間,提供技
    術人員另一種有效的製程最佳化方法。


    This study combines response surface methodology (RSM) and ant colony optimization (ACO) to establish the regression model between process parameters and specimen quality. This combination performs optimization to obtain the optimal process parameters and improve the weld line strength of specimen. First,nylon 6 containing 15% short carbon fiber was used as the injection material, and Moldex3D and SolidWorks were used for mold design. Next, the Box-Behnken design (BBD) was employed to conduct the experiment and establish amathematical model which was the fitness function of the ACO. Finally, RSM and ACO were used to solve the optimization problem. The influence of different process parameters on the weld line strength of the specimen was discussed. The optimization results of RSM and ACO were compared with each other.
    The experimental results show that the weld line strength is significantly affected by the melt temperature. The higher the melt temperature, the better the bonding of the weld line which significantly improves its tensile strength. The prediction error of weld line strength using RSM is 1.53%, and its improvement rate is 3.89%. Moreover, the prediction error of weld line strength using ACO is
    0.68%, and its improvement rate is 4.54%. According to the present results, it shows that ACO has a smaller prediction error and a higher improvement rate of weld line strength. This study indicates that ACO has better ability to optimize
    process parameters. In conclusion, both RSM and ACO can effectively optimize process parameters and improve weld line strength, which provide technicians with another effective method for process optimization.

    中文摘要 ................................................................................................................ I ABSTRACT ........................................................................................................ II 誌謝 .................................................................................................................... III 目錄 ..................................................................................................................... IV 圖目錄 ............................................................................................................... VII 表目錄 ................................................................................................................. IX 第一章、緒論 ....................................................................................................... 1 1-1 前言 ............................................................................................................ 1 1-2 文獻回顧 .................................................................................................... 3 1-2-1 纖維複合材料文獻 ............................................................................ 3 1-2-2 實驗設計法文獻 ................................................................................ 4 1-2-3 蟻群演算法文獻 ................................................................................ 4 1-3 研究動機與目的 ........................................................................................ 5 1-4 研究流程 .................................................................................................... 6 第二章、基本原理與理論模式 ........................................................................... 8 2-1 射出成型原理 ............................................................................................ 8 2-1-1 充填階段 ............................................................................................ 8 2-1-2 保壓階段 ............................................................................................ 9 2-1-3 冷卻階段 ............................................................................................ 9 2-1-4 頂出脫模階段 .................................................................................... 9 2-2 縫合線 ...................................................................................................... 10 V 2-3 拉伸試驗 .................................................................................................. 11 2-3-1 彈性限與比例限 .............................................................................. 11 2-3-2 楊氏係數 .......................................................................................... 11 2-3-3 降伏點與降伏強度 .......................................................................... 12 2-3-4 抗拉強度 .......................................................................................... 12 2-3-5 延性 .................................................................................................. 12 2-4 實驗設計法 .............................................................................................. 13 2-4-1 反應曲面法 ...................................................................................... 13 2-4-2 Box-Behnken 設計 ............................................................................ 14 2-5 蟻群演算法 .............................................................................................. 15 2-5-1 馬爾可夫鏈蒙特卡羅法 .................................................................. 16 2-5-2 轉移機率 .......................................................................................... 17 2-5-3 狀態更新之依據準則 ...................................................................... 17 2-5-4 費洛蒙強度更新之依據準則 .......................................................... 18 第三章、研究方法 ............................................................................................. 20 3-1 實驗材料 .................................................................................................. 20 3-2 試片設計 .................................................................................................. 22 3-3 實驗設備 .................................................................................................. 22 3-4 實驗設計軟體 .......................................................................................... 28 3-5 Z-SCORE 標準化方法 ................................................................................ 28 第四章、結果與討論 ......................................................................................... 29 4-1 模具設計 .................................................................................................. 29 4-1-1 流道設計 .......................................................................................... 29 VI 4-1-2 澆口設計 .......................................................................................... 32 4-1-3 冷卻水路設計 .................................................................................. 34 4-2 射出成型實驗 .......................................................................................... 35 4-2-1 製程參數規劃 .................................................................................. 35 4-2-2 實驗設計 .......................................................................................... 37 4-3 拉伸試驗結果 .......................................................................................... 39 4-4 反應曲面法最佳化過程與結果 .............................................................. 43 4-4-1 模型適當性檢驗 .............................................................................. 43 4-4-2 變異數分析 ...................................................................................... 46 4-4-3 預測結果 .......................................................................................... 48 4-5 蟻群演算法最佳化過程與結果 .............................................................. 48 4-5-1 蟻群參數設定與測試 ...................................................................... 49 4-5-2 蟻群最佳化預測結果 ...................................................................... 56 4-6 製程參數最佳化結果比較 ...................................................................... 57 第五章、結論與未來展望 ................................................................................. 60 5-1 結論 .......................................................................................................... 60 5-2 未來展望 .................................................................................................. 62 附錄一 ................................................................................................................. 63 參考文獻 ............................................................................................................. 64

    [1] D. K. Rajak, D. D. Pagar, P. L. Menezes, and E. Linul, "Fiber-reinforced polymer
    composites: Manufacturing, properties, and applications," Polymers, vol. 11, no. 10, p.
    1667, 2019.
    [2] C.-T. C. Huang, H.-C. Tseng, M.-C. Chen, and J. Vlcek, "Numerical Simulation for
    Screw Geometry Design and Performance Effects on Fiber Breakage Study."
    [3] C.-T. Huang, X.-W. Chen, and W.-W. Fu, "Investigation on the fiber orientation
    distributions and their influence on the mechanical property of the co-injection molding
    products," Polymers, vol. 12, no. 1, p. 24, 2019.
    [4] H.-C. Tseng, R.-Y. Chang, and C.-H. Hsu, "Phenomenological improvements to
    predictive models of fiber orientation in concentrated suspensions," Journal of Rheology,
    vol. 57, no. 6, pp. 1597-1631, 2013.
    [5] S.-Y. Fu, B. Lauke, E. Mäder, C.-Y. Yue, and X. Hu, "Tensile properties of short-glassfiber-
    and short-carbon-fiber-reinforced polypropylene composites," Composites Part A:
    Applied Science and Manufacturing, vol. 31, no. 10, pp. 1117-1125, 2000.
    [6] A. Hassan, R. Yahya, A. Yahaya, A. Tahir, and P. Hornsby, "Tensile, impact and fiber
    length properties of injection-molded short and long glass fiber-reinforced polyamide 6,
    6 composites," Journal of reinforced plastics and composites, vol. 23, no. 9, pp. 969-
    986, 2004.
    [7] F. Hacioglu, U. Tayfun, T. Ozdemir, and T. Tincer, "Characterization of carbon fiber and
    glass fiber reinforced polycarbonate composites and their behavior under gamma
    irradiation," Progress in Nuclear Energy, vol. 134, p. 103665, 2021.
    [8] J. Denault, T. Vu‐Khanh, and B. Foster, "Tensile properties of injection molded long
    fiber thermoplastic composites," Polymer composites, vol. 10, no. 5, pp. 313-321, 1989.
    [9] J. Li and C. Cai, "The carbon fiber surface treatment and addition of PA6 on tensile
    properties of ABS composites," Current Applied Physics, vol. 11, no. 1, pp. 50-54, 2011.
    [10] S. Mortazavian and A. Fatemi, "Effects of fiber orientation and anisotropy on tensile
    strength and elastic modulus of short fiber reinforced polymer composites," Composites
    part B: engineering, vol. 72, pp. 116-129, 2015.
    [11] P. H. Foss, H. C. Tseng, J. Snawerdt, Y. J. Chang, W. H. Yang, and C. H. Hsu, "Prediction
    of fiber orientation distribution in injection molded parts using Moldex3D simulation,"
    Polymer Composites, vol. 35, no. 4, pp. 671-680, 2014.
    [12] C.-Y. Chung, C.-C. Yeh, and C.-M. Lin, "Injection Molding Study on the Strength of
    Weld Line for Polycarbonate and Carbon Fiber Composite Material," in IOP Conference
    Series: Materials Science and Engineering, 2020, vol. 842, no. 1: IOP Publishing, p.
    65
    012018.
    [13] R. Srinivasan, T. Pridhar, L. Ramprasath, N. S. Charan, and W. Ruban, "Prediction of
    tensile strength in FDM printed ABS parts using response surface methodology (RSM),"
    Materials today: proceedings, vol. 27, pp. 1827-1832, 2020.
    [14] E. Agung, S. Sapuan, M. Hamdan, H. Zaman, and U. Mustofa, "Optimization of the
    mechanical properties of abaca fibre-reinforced high impact polystyrene (HIPS)
    composites using box-behnken design of experiments," Polymers and Polymer
    Composites, vol. 19, no. 8, pp. 697-710, 2011.
    [15] W.-C. Chen and D. Kurniawan, "Process parameters optimization for multiple quality
    characteristics in plastic injection molding using Taguchi method, BPNN, GA, and
    hybrid PSO-GA," International Journal of Precision Engineering and Manufacturing,
    vol. 15, no. 8, pp. 1583-1593, 2014, doi: 10.1007/s12541-014-0507-6.
    [16] M. Dorigo, V. Maniezzo, and A. Colorni, "Ant system: optimization by a colony of
    cooperating agents," IEEE Transactions on Systems, Man, and Cybernetics, Part B
    (Cybernetics), vol. 26, no. 1, pp. 29-41, 1996.
    [17] M. Dorigo and L. M. Gambardella, "Ant colony system: a cooperative learning
    approach to the traveling salesman problem," IEEE Transactions on evolutionary
    computation, vol. 1, no. 1, pp. 53-66, 1997.
    [18] 許芳峻, "應用類神經螞蟻演算法於50 奈米接觸洞微影製程參數最佳化之研究,"
    碩士, 工業工程與管理研究所, 明新科技大學, 新竹縣, 2009. [Online]. Available:
    https://hdl.handle.net/11296/2w7wa7
    [19] K. Yang, Y. Wang, and G. Wang, "Research on the Injection Mold Design and Molding
    Process Parameter Optimization of a Car Door Inner Panel," Advances in Materials
    Science and Engineering, vol. 2022, 2022.
    [20] A. Ghazali, M. Shirani, A. Semnani, V. Zare-Shahabadi, and M. Nekoeinia,
    "Optimization of crystal violet adsorption onto Date palm leaves as a potent biosorbent
    from aqueous solutions using response surface methodology and ant colony," Journal
    of environmental chemical engineering, vol. 6, no. 4, pp. 3942-3950, 2018.
    [21] 羅壬成, "模流分析與射出成型控制參數的優化," 碩士, 工學院碩士在職專班精密
    與自動化工程學程, 國立交通大學, 新竹市, 2006. [Online]. Available:
    https://hdl.handle.net/11296/57td3d
    [22] 龍成塑膠. " 射出成型穩定的關鍵 : 射速篇."
    https://www.lcpf.com.tw/tw/knowledge/injection-moldingtechnology/
    003?page=2&rtnKind=list (accessed.
    [23] 林炫良, "厚件產品保壓過程對於收縮率與殘留應力影響之研究," 碩士, 機械工程
    研究所, 中原大學, 桃園縣, 2003. [Online]. Available:
    66
    https://hdl.handle.net/11296/4e398x
    [24] E. Bociąga and W. Skoneczny, "Characteristics of injection molded parts with the areas
    of weld lines," Polimery, vol. 65, no. 5, pp. 337-345, 2020.
    [25] S. C. Ferreira et al., "Box-Behnken design: An alternative for the optimization of
    analytical methods," Analytica chimica acta, vol. 597, no. 2, pp. 179-186, 2007.
    [26] M. Dorigo and L. M. Gambardella, "Ant colonies for the travelling salesman problem,"
    biosystems, vol. 43, no. 2, pp. 73-81, 1997.
    [27] A. Grasas, A. A. Juan, J. Faulin, J. De Armas, and H. Ramalhinho, "Biased
    randomization of heuristics using skewed probability distributions: A survey and some
    applications," Computers & Industrial Engineering, vol. 110, pp. 216-228, 2017.
    [28] 龍成塑膠. " 塑膠材質大補帖 : 基本概念."
    https://www.lcpf.com.tw/tw/knowledge/plasticmaterial/
    003?page=1&rtnKind=plastic-material (accessed.
    [29] 集盛科技股份有限公司. "尼龍複合材料詳細規格." http://www.zigsheng.com/zigsheng-
    products-zh/nylon-compound-material-zh/pa6-zh/ (accessed.
    [30] 龍成塑膠. " 塑膠材質大補帖 : 尼龍(PA)."
    https://www.lcpf.com.tw/tw/knowledge/plastic-material/007?page=1&rtnKind=list
    (accessed.
    [31] 三菱化学株式会社. " 碳纖維短切纖維產品資訊." https://www.mchemical.
    co.jp/carbon-fiber/cn/product/mid/ (accessed.
    [32] 台中精機. " 台中精機射出成型機規格資訊 "
    https://www.victortaichung.com/injection-machines/tw/vsp.htm (accessed.
    [33] 潤輝科技有限公司. " 油式溫度控制機AO 系列規格表." http://www.a1-
    max.com.tw/product/detail1.html (accessed.
    [34] 晏邦電機工業有限公司. " 料斗乾燥機 (HD/IHD/DHD) 規格表."
    https://www.yannbang.com/hopper-dryer-tw (accessed.
    [35] C. Cheadle, M. P. Vawter, W. J. Freed, and K. G. Becker, "Analysis of microarray data
    using Z score transformation," The Journal of molecular diagnostics, vol. 5, no. 2, pp.
    73-81, 2003.
    [36] 許嘉翔、張榮語、王茂齡, 模流分析理論與實務 Molding Simulation Theory and
    Practice. p. 201.
    [37] 林霹泯, "淺談熱塑性高分子長纖維複材射出成型技術," 財團法人塑膠工業技術
    發展中心技術/研發部.
    [38] 許嘉翔、張榮語、王茂齡, 模流分析理論與實務 Molding Simulation Theory and
    Practice. p. 203.
    [39] B. P. Chang, H. M. Akil, M. G. Affendy, A. Khan, and R. B. M. Nasir, "Comparative
    67
    study of wear performance of particulate and fiber-reinforced nano-ZnO/ultra-high
    molecular weight polyethylene hybrid composites using response surface
    methodology," Materials & Design, vol. 63, pp. 805-819, 2014.
    [40] C.-S. Chen, T.-J. Chen, R.-D. Chien, and S.-C. Chen, "Investigation on the weldline
    strength of thin-wall injection molded ABS parts," International Communications in
    Heat and Mass Transfer, vol. 34, no. 4, pp. 448-455, 2007.
    [41] C. H. Wu and W. J. Liang, "Effects of geometry and injection‐molding parameters on
    weld‐line strength," Polymer Engineering & Science, vol. 45, no. 7, pp. 1021-1030,
    2005.
    [42] A. A. Dzulkipli and M. Azuddin, "Study of the effects of injection molding parameter
    on weld line formation," Procedia engineering, vol. 184, pp. 663-672, 2017.
    [43] B. Ozcelik, "Optimization of injection parameters for mechanical properties of
    specimens with weld line of polypropylene using Taguchi method," International
    Communications in Heat and Mass Transfer, vol. 38, no. 8, pp. 1067-1072, 2011.
    [44] J. P. Kleijnen, "Response surface methodology," in Handbook of simulation
    optimization: Springer, 2014, pp. 81-104.
    [45] S. Ruder, "An overview of gradient descent optimization algorithms," arXiv preprint
    arXiv:1609.04747, 2016.
    [46] Y.-M. Huang, W.-R. Jong, and S.-C. Chen, "Transfer learning applied to characteristic
    prediction of injection molded products," Polymers, vol. 13, no. 22, p. 3874, 2021.

    QR CODE
    :::