(3.237.234.213) 您好!臺灣時間:2021/03/09 11:28
字體大小: 字級放大   字級縮小   預設字形  
回查詢結果

詳目顯示:::

我願授權國圖
: 
twitterline
研究生:許育閔
研究生(外文):HSU,YU-MIN
論文名稱:不同粒度碳化鎢多道次放電加工之智能排程
論文名稱(外文):Applying intelligent scheduling for multipass electrical discharge machining process on tungsten carbide with different grain size
指導教授:戴子堯戴子堯引用關係
指導教授(外文):TAI,TZU-YAO
口試委員:郭瑞昭許富銓
口試委員(外文):KUO,JUI-CHAOHSU,FU-CHUAN
口試日期:2020-07-23
學位類別:碩士
校院名稱:南臺科技大學
系所名稱:機械工程系
學門:工程學門
學類:機械工程學類
論文種類:學術論文
論文出版年:2020
畢業學年度:108
語文別:中文
論文頁數:129
中文關鍵詞:碳化鎢EDM多道次加工最佳化粒度
外文關鍵詞:Tungsten carbideEDMMultipass processingOptimizingGranularity
相關次數:
  • 被引用被引用:0
  • 點閱點閱:23
  • 評分評分:系統版面圖檔系統版面圖檔系統版面圖檔系統版面圖檔系統版面圖檔
  • 下載下載:3
  • 收藏至我的研究室書目清單書目收藏:0
摘要
本實驗選用的材料為KE13、VA70、CF10碳化鎢化合物(15mm×15mm),加工電極則選用石墨(10mm×10mm)為電極材料,共進行96組全因素實驗,獲得並探討材料移除率、電極消耗率、表面粗糙度、再鑄層厚度,並使用線性規劃建立模型,搭配Python和Gurobi求解器將加工後的數據運用於多道次加工之優化上。
實驗結果顯示,材料移除率會隨著低壓電流和放電持續時間的增大而變大,在31μs之後開始下降,提升電壓也會使得材料移除率提升,粒度越細材料移除率越高,對於電極消耗率,會隨著低壓電流的增大而變大,隨著放電持續時間在15.7μs到達峰值隨後大幅度下降,粒度的改變可能造成電極消耗率提升,表面粗糙度會因為低壓電流和放電持續時間的增大有惡化的趨勢,粒度會影響低壓電流的表面粗糙度,粒度越細越小的低壓電流會使表面粗糙增大,再鑄層厚度則主要受到低壓電流和放電持續時間的影響。爲了達到多道次加工優化之效果,本實驗程式選用材料移除率,表面粗糙度和再鑄層厚度作為輸入參數,因實驗結果得知影響這些結果的主要參數為低壓電流和放電持續時間,改變低壓電流和放電持續時間的96組參數作為資料庫。程式結果顯示,不固定每道次之步進和最後道次參數,最佳加工時間為加工道次為3,得出的參數組合編號為28、7、3,輸出結果12649.12s,也可根據不同參數和品質要求,改變步進、道次之設定,輸出最佳的參數組合與加工時間。
對於製程的優化,在最短的時間獲得較佳的加工品質,一直是業界追求的目標。本實驗建立之模式可以根據使用者輸入不同的資料庫,再根據不同的加工品質,計算出符合品質要求下最佳的加工時間和參數選擇,可以利用程式計算來代替繁瑣複雜的實驗測試,以達到對於多道次放電加工之智能優化的效果。

Abstract
This research used KE13, VA70, CF10 tungsten carbide compound (15mm × 15mm) and graphite (10mm × 10mm ) to perform full factorial experiment. Material removal rate, electrode wear rate, surface roughness and the thickness of the recast layer have been discussed by using linear programming model with Python and Gurobi solver, the processed data are applied to the optimization of multipass processing.
The results show that the material removal rate will increase with the increase of low-voltage current and discharge duration, and start to decrease after 31μs. Increasing the voltage will also increase the material removal rate. The finer the particle size, the higher the material removal rate. For the electrode consumption rate, it will increase with the increase of the low voltage current. As the discharge duration reaches the peak at 15.7μs and then greatly decreases, the change in particle size may cause the electrode consumption rate to increase, and the surface roughness will be due to the low voltage current With the increase of the discharge duration, there is a tendency to deteriorate. The particle size will affect the surface roughness of the low-voltage current. The finer the particle size, the smaller the low-voltage current will increase the surface roughness. The thickness of the cast layer is mainly affected by the low-voltage current and the discharge duration. Impact. In order to achieve the effect of multi-pass processing optimization, this experimental program selects material removal rate, surface roughness and recast layer thickness as input parameters. Because of the experimental results, it is known that the main parameters affecting these results are low-voltage current and discharge duration. 96 sets of parameters for changing low voltage current and discharge duration are used as a database. The program result shows that the stepping and last pass parameters of each pass are not fixed. The best processing time is 3 for the processing pass. The parameter combination numbers obtained are 28, 7, 3, and the output result is 12649.12s. Different parameters and quality requirements, change the setting of step and pass, output the best parameter combination and processing time.
Achieving the best processing time has always been the goal of pursuit.This research can calculate the best processing time and parameter selection according to different databases and processing quality requirements.Program calculation can be used to achieve the effect of intelligent optimization for multipass EDM.

總目錄
摘要………………………………………………………………………...…………….I
Abstract……………………………………………………………………...…………..II
致謝……………………………………………………………………………..……...III
總目錄……………………………………..………………………………………...…IV
表目錄……………………………………………………………………………...….VII
圖目錄………………………………………………………………………………..VIII
第一章 前言 1
第二章 文獻回顧 3
2.1 放電加工的始源 3
2.2 放電加工之材料移除率 3
2.3 放電加工之電極消耗率 6
2.4 放電加工之表面粗糙度 9
2.5 放電加工之再鑄層厚度與裂紋 11
2.6 放電加工之碳化鎢的影響 13
第三章 理論說明 15
3.1 放電加工起源 15
3.2 放電加工之相關參數 16
3.3 放電加工之過程 18
3.4 放電加工表面形貌 20
3.5 放電加工之再鑄層特徵 26
3.6 最佳化理論 28
第四章 實驗方法 30
4.1 實驗流程 30
4.2 實驗規劃 32
4.3 實驗工件材料電極 38
4.4 實驗設備介紹 39
4.5 材料移除率 45
4.6 電極消耗率 45
4.7 再鑄層平均厚度量測方式 46
4.8 模型問題描述 46
4.9 模型架構 47
第五章 實驗結果與討論 51
5.1 材料移除率 51
5.2 電極消耗率 59
5.3 表面粗糙度 68
5.4 再鑄層厚度 80
5.5 程式結果 87
第六章 結論 111
第七章 未來與展望 114
第八章 參考文獻 115


1. Yan M.T., Lai Y.P.,“Surface quality improvement of wire-EDM using a fine-finish power supply”,International Journal of Machine Tools and Manufacture,Vol.47, PP.1686-1694, 2007.
2.Debabrata Mandal, Pal S.K. and Saha P.,“Back propagation neural network based modeling of multi-responses of an electrical discharge machining process”, International Journal of Knowledge-based and Intelligent Engineering Systems,Vol.11, PP.381-390, 2007.
3.A.R. Ahamed, P. Asokan, S. Aravindan, “EDM of hybrid Al–SiCp–B4Cp and Al–SiCp–Glassp MMCs”, International Journal Advanced Manufacturing Technology,Vol.44,PP.520–528, 2009.
4.Joshi S.N. and Pande S.S., Development of an intelligent process model for EDM, International Journal of Advanced Manufacturing Technology,Vol.45,PP.300-317, 2009.
5.Panda DK., Bhoi RK., Artificial neural network prediction of material removal rate in electro discharge machining, Materials and Manufacturing Processes,Vol.20, PP.645-672, 2005.
6.Pradhan M.K., Biswas C.K., Neuro-fuzzy and neural network-based prediction of various responses in electrical discharge machining of AISI D2 steel, International Journal of Advanced Manufacturing Technology,Vol.50,PP.591-610, 2010.
7.Ji R.J., Liu Y.H., Diao R.Q., Xu C.C., Li X.P., Cai B.Q. and Zhang Y.Z., Influence of Electrical Resistivity and Machining Parameters on Electrical Discharge Machining Performance of Engineering Ceramics, Plos one, 11, e110775, 2014.
8.Wu Y.C., Peiqing H.Z., Wang C.H., Edge fabrication and process optimization of precision woodworking PCD millers with disk electrical dischargemachining, International Journal of Advanced Manufacturing Technology,Vol.85,PP.279-286, 2016.
9.Torres A., Puertas I., Luis C. J., EDM machinability and surface roughness analysis of INCONEL 600 using graphite electrodes, International Journal of Advanced Manufacturing Technology,Vol.84,PP.2671-2688, 2016.
10. M. Kunieda, T. Kobayashi, “Clarifying mechanism of Determining tool electrode wear ratio in EDM using spectroscopic measurement of vapor density”,ournal of Materials Processing Technology,Vol.149,284-288,2004
11.M.P. Jahan, Y.S. Wong, M. Rahman,” A study in the fine-tinish diesinking nicro-EDM of tungsten carbide using different electrode materials”, Journal of Materials Processing Technology Vol 209,PP.8,3956-3967,2009
12.謝銘倫,”微放電加工之加工參數影響研究”,國立成功大學機械工程系,碩士論文,2008
13.Pradhan M.K., Biswas C.K., Neuro-fuzzy and neural network-based prediction of various responses in electrical discharge machining of AISI D2 steel, International Journal of Advanced Manufacturing Technology, 50, 591-610, 2010.
14.朱柏翰,”微放電加工之加工參數對表層特性之影響”,國立成功大學機械工程學系,碩士論文,2009
15.Joudivand Sarand M.H., Shabgard M.R., Investigation of the effect of thermal diffusivity coefficient of tool material on electrode-tool wear in the EDM process, Archives of Civil and Mechanical Engineering,Vol.15,PP.806-821, 2015.
16.Maradia U., Boccadoro M., Stirnimann J., Kuster F., Wegenera K., Electrode wear protection mechanism in meso–micro-EDMU, Journal of Materials Processing Technology,Vol.223,PP.22-33, 2015.
17.呂駿,”多道次放電加工之智能排程”,私立南台科技大學機械工程學系,碩士論文,2018
18.Torres A., Puertas I., Luis C. J., EDM machinability and surface roughness analysis of INCONEL 600 using graphite electrodes, International Journal of Advanced Manufacturing Technology, Vol.84, PP.2671-2688, 2016.
19.Muttamara A.P. and Kanchanomai C.H., Effect of Carbon in the Dielectric Fluid and Workpieces on the Characteristics of Recast Layers Machined by Electrical Discharge Machining, Metallurgical and Materials Transactions A-physical metallurgy and materials science, Vol.47, PP.3248-3255, 2016.
20.Long B.T., Phan N.H., Cuong N.G. and Toan N.D., Surface quality analysis of die steels in powder-mixed electrical discharge machining using titan powder in fine machining, Advances in Mechanical Engineering,Vol.8,PP.1-13, 2016.
21.A.B. Puri , B. Bhattacharyya “Modeling and analysis of white layer depth in a wire-cut EDM process through response surface methodology” The International Journal of Advanced Manufacturing Technology ,Vol.25,PP.301-307,2005
22.Wang F., Liu Y.H., Shen Y., Ji R.J., Tang Z.M. and Zhang Y.Z., Machining Performance of Inconel 718 Using High Current Density Electrical Discharge Milling, Materials and Manufacturing Processes, Vol.28, PP.1147-1152, 2013.
23.Dewangan S., Biswas C.K. and Gangopadhyay S.,Influence of Different Tool Electrode Materials on EDMed Surface Integrity of AISI P20 Tool Steel, Materials and Manufacturing Processes,Vol.29,PP.1387-1394, 2014.
24.Joudivand Sarand M.H., Shabgard M.R., Investigation of the effect of thermal diffusivity coefficient of tool material on electrode-tool wear in the EDM process, Archives of Civil and Mechanical Engineering, Vol.15, PP.806-821, 2015.
25.黃舜韋,”以田口法漢類神經網路探討石墨和紅銅電極對SKD11冷作工具鋼放電加工之結果”,私立南台科技大學機械工程系,碩士論文,2017
26. S. Assarzadeh, M. Ghoreishi, Statistical modeling and optimization of process parameters in electro-discharge machining of cobalt-bonded tungsten carbide composite (WC/6%Co), Department of Mechanical Engineering, K. N. Toosi University of Technology, Vol.6, PP.463-467, 1989。
27.林炎成,”超硬合金放電加工之特性”,中洲學報機械工程系,2002。

28.林炎成, Study of the effect of machining parameters on the machining characterisitic in electrical discharge machining of tungsten carbide,Department of Mechanical and Production Engineering,The National University of Singapore, Vol.115, PP.344-358,2003
29.Mahdavinejad, R; Mahdavinejad, A., ED Machinig of WC-CO. J. Mater.Process, Vol.162-163, PP.637-643,2005
30.Shabgard, M.R. Kabirinia, F., Effect of Dielectric Liquid on Characteristics of WC-Co Powder Synthesized Using EDM Process Mater Manuf Process, Vol. 29, PP.1269-1276, 2014
31.馮世徨,”用倒傳遞法於石墨電極之放電加工表層特性預測”,私立南台科技大學機械工程系,碩士論文,2017

QRCODE
 
 
 
 
 
                                                                                                                                                                                                                                                                                                                                                                                                               
第一頁 上一頁 下一頁 最後一頁 top
系統版面圖檔 系統版面圖檔