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研究生:蔡國銘
研究生(外文):Kuo-Ming Tsai
論文名稱:形雕放電加工機制與模式之分析
論文名稱(外文):ANALYSIS OF PROCESS MECHANISM AND MODEL FOR DIE-SINKING ELECTRICAL DISCHARGING
指導教授:王培仁
指導教授(外文):Pei-Jen Wang
學位類別:博士
校院名稱:國立清華大學
系所名稱:動力機械工程學系
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:2002
畢業學年度:90
語文別:英文
論文頁數:185
中文關鍵詞:放電加工經驗模式類神經網路模式
外文關鍵詞:Electrical Discharge MachiningSemi-empirical ModelNeural Networks
相關次數:
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放電加工是常用於加工硬化金屬材料之一種非傳統加工方法,其加工過程非常複雜為電、熱、流體、氣體力及化學作用等多項物理現象之組合,目前仍有某些物理現象未能完全瞭解;因此,放電加工對材料加工後之工件品質具有高度之不可預測性,因而造成加工無法連續進行,導致未能全程自動化以提高生產力。
本論文除了探討電極極性對加工品質的影響外,主要是建立放電加工之材料移除率、表面粗糙度與電極磨耗率三個品質特性之經驗及類神經網路預測模式。首先,使用田口式實驗設計法進行基礎實驗,以篩選出影響品質特性的重要加工參數,經實驗與分析可得這些重要參數為材料的極性、峰值電流、放電脈衝時間及材料之種類,再透過因次分析及使用短電弧放電之理論可獲得以這些重要加工參數為基礎之品質特性經驗模式。此經驗模式之係數與指數值可以最佳化方法嵌配實驗數據而得之,本研究比較多種不同最佳化方法皆獲得相同的係數與指數值,並經實驗證明此模式可成功地使用於單一純金屬材料且單一品質特性之加工預測。但是,由於材料之結構與其性質的差異性,無法獲得一個廣泛適用於各種不同純金屬之單性能泛用預測模式。在類神經網路預測模式方面,本研究使用七種監督型類神經網路以獲得放電加工系統之數值模擬模式。比較類神經網路模式與經驗模式所得之預測值,可知類神經網路模式具有較佳的準確性。最後,經由驗證實驗證明這些經驗與類神經網路模式具有可靠的準確度,可為未來工業上之使用奠定良好的基礎。
Electrical discharge machining has been employed for cutting special alloys and hardened steel for some decades; however, the process has still been treated as an empirical art rather than a technical skill because of the complex physical phenomena are incompletely understood during the process. With the advent of neural networks for modeling manufacturing process, the applications of neural networks on modeling of electrical discharge machining could be thoroughly studied and evaluated for better understanding the process; and, as a further step, the results could lead to the improvement of the process efficiency. In this dissertation, complete review on the published literatures has been conducted for including the pertinent process parameters of the process. Then, Design of Experiment has been established for screening the most important parameters in order to compare the effectiveness of various models, namely a semi-empirical model and seven types of neural networks models. The semi-empirical model has been based upon fundamental laws of physics together with dimensional analysis for pure electrode and work materials. Whereas, the neural networks have been completely accounted for the through study on the experimental data given by the design of experiment procedures. The erosions of both tool and work-piece and the surface roughness of the work-piece materials have been measured and analyzed for the purpose of testing the models. With the appropriate training process, the predictions from both the empirical model and the various neural networks models have been compared to the checking experimental results. Evidently, the neural networks have shown better agreement than the empirical model in this study. This could lead to the successful applications of modeling of electrical discharge machining on the shop floor in the near future.
摘 要
ABSTRACT
誌 謝
TABLE OF CONTENTSI
LIST OF FIGURES AND TABLESIII
NOMENCLATURES AND NOTATIONSXI
CHAPTER 1 INTRODUCTION
1.1 Electric Discharging Process
1.2 Problem Descriptions
1.3 Scope of Work
CHAPTER 2 FUNDAMENTALS AND REVIEWS
2.1 Breakdown Phenomena in Arc Discharge
2.2 Theoretical Models
2.3 Empirical Models
2.4 Neural-Networks Models
CHAPTER 3 EXPERIMENTAL STUDIES
3.1 Equipment Setup
3.2 Accuracy Confirmation
3.3 Effects of Polarity
3.4 Experimental Designs
3.5 Screening Procedure
3.6 Model Fitting and Verification Experimentation
CHAPTER 4 SEMI-EMPIRICAL MODELS
4.1 Dimensional Analysis
4.2 Optimization Methods
4.3 Semi-empirical Model
4.4 Conclusions
CHAPTER 5 NEURAL NETWORK MODELS
5.1 Fundamentals of Neural Networks
5.2 Neural Network Models
5.3 Conclusions
CHAPTER 6 CONCLUSIONS AND FUTURE WORK
6.1 Conclusions
6.2 Future Work
BIBLIOGRAPHY
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