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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 楊宏智 | |
dc.contributor.author | Kuan-Wen Chen | en |
dc.contributor.author | 陳冠文 | zh_TW |
dc.date.accessioned | 2021-06-08T04:13:40Z | - |
dc.date.copyright | 2010-08-16 | |
dc.date.issued | 2010 | |
dc.date.submitted | 2010-08-16 | |
dc.identifier.citation | [1] 張文彬,“基於聲音和圖像的刀具磨損狀態監測技術的研究”,浙江工業大學碩
士論文, 2003 [2] N.H. Cook, “Tool Wear and Tool Life”, Journal of Engineering for Industry, Nov.1973/33 [3] 馬寧元,李新中,“刀具破損之探討”, 機械工業雜誌第291期, 2007 [4] Stöferle,“Fluidic System for Monitoring Machine Tool Wear During a Machining Operation”, US Patent, No.3,889,520, 1957 [5] 梁有燈,駱錦榮,邱亦契,“影像套合於鈦基鍍膜面銑削刀具磨耗的自動量測”,中國機械工程學會第二十一屆全國學術研討會論文集, 2004 [6] S. Ramalingam and P.K. Wright, “Abrasive wear in machining: Experiment with material of controlled microstructure,” Journal of Engineering Materials and Technology, Vol. 103, 151-156, 1981 [7] M.C. Shaw, Metal Cutting Principles, Oxford Univ. Press New York, 1984 [8] Mayer,“Device for Early Detection of Break and Marginal Wear in The Cutting Edges of Tools”, US Patent, No.4,885,530, 1989 [9] A. Prateepasen, Y. H. J. Au, B. E. Jones, “Acoustic Emission and Vibration for Tool Wear Monitoring in Single-Point Machining Using Belief network,” IEEE Instrumentation and Measurement Technology Conference, Budapest, Hungary, May 21-23, 2001 [10] C. C. Tan, “Monitoring of Tool Wear Using Acoustic Emission,” Intelligent Control and Instrumentation, 1992. SICICI '92. Proceedings., Singapore International Conference, Vol. 2, 17-21 Feb 1992 [11] I.S. Kanga, J.S. Kimb, M.C. Kangc, K.Y. Leed, “Tool condition and machined surface monitoring for micro-lens array fabrication in mechanical machining,” Journal of Materials Processing Technology, Volume 201, Issues 1-3, 26 May 2008, Pages 585-589 [12] Sadettin Orhana, Ali Osman Era, Necip Camuşcua and Ersan Aslana, “Tool wear evaluation by vibration analysis during end milling of AISI D3 cold work tool steel with 35 HRC hardness,” NDT & E International, Volume 40, Issue 2, March 2007, Pages 121-126 [13] Mohammad Malekiana, Simon S. Parka and Martin B.G. Junb, “Tool wear monitoring of micro-milling operations,” Journal of Materials Processing Technology, Volume 209, Issue 10, 1 June 2009, Pages 4903-4914 [14] P. Srinivasapa Pai and P. K. Ramakrishna Rao, “Acoustic emission analysis for tool wear monitoring in face milling,” int. j. prod. res., vol. 40, no. 5, 2002, 1081-1093 [15] Yonghong Peng, “Empirical Model Decomposition Based Time-Frequency Analysis for the Effective Detection of Tool Breakage,” Journal of Manufacturing Science and Engineering, Volume 128, Issues 1, February 2006, Pages 154 [16] A.M. Bassiuny and Xiaoli Li, “Flute breakage detection during end milling using Hilbert–Huang transform and smoothed nonlinear energy operator,” International Journal of Machine Tools and Manufacture, Volume 47, Issue 6, May 2007, Pages 1011-1020 [17] Rodolfo E. Haber, Jose E. Jiménez, C. Ronei Peres and José R. Alique, “An investigation of tool-wear monitoring in a high-speed machining process,” Sensors and Actuators A: Physical, Volume 116, Issue 3, 29 October 2004, Pages 539-545 [18] Litao Wang, Mostafa G. Mehrabi, Elijah Kannatey-Asibu, Jr., “Hidden Markov Model-based Tool Wear Monitoring in Turning,” Journal of Manufacturing Science and Engineering, Volume 124, Issue 3, August 2002, Pages 651 [19] 張銘偉, “模糊—小波於心電圖分析之應用,” 國立台灣大學機械工程學研究所碩士論文, 2005 | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/22198 | - |
dc.description.abstract | 在自動化生產加工系統中,刀具狀態偵測系統扮演著極為重要的角色,當刀具發生磨耗或崩損時,適時的更換刀具能減少不良廢品造成的損失及提昇產品品質與生產良率。
本研究之目的在於針對刀具狀態偵測系統開發進行初步探討及研究,根據許多文獻指出加工時震動訊號之特徵頻率能量與刀具狀態有密切之相關性。但為了分離出特徵頻率使用傅立葉轉換法須選擇適當頻段的濾波器,這對沒有相關專業知識背景的操作員而言是相當困難的,為解決此問題本研究提出一簡易量化頻譜變化的演算法-頻譜相關法,實驗結果顯示頻譜相關法能有效辨識出刀具磨耗及微崩的發生但無法得知刀具崩損之位置。對此本研究也針對切削力進行測量及探討希望藉由比對切削力之變化對應出刀具發生崩損之訊號門檻與可能位置,根據實驗結果顯示當刀具崩損位置切入工件後其切削力明顯劇增,故切削力本身即可作為辨識刀具損壞之指標,因此利用切削力之特徵趨勢配合閾值建立之演算法系統便能輕易達到辨識刀具微崩發生位置之目的,根據實驗結果顯示此法能有效的達到刀具微崩發生位置之辨識。 | zh_TW |
dc.description.abstract | It is well known that tool condition monitoring system plays an important role in automatic machining system. By detecting, and thus changing the worn tool in time, the loss due to defect products can be greatly reduced and hence ensuring product quality and reliability.
The purpose of this research is to develop a tool condition detection and monitoring system for the tool wear and breakage during cutting process. According to many research findings, the characteristic frequency energy of cutting vibration signal gives the best information corresponding to the tool condition. However, in order to isolate the characteristic frequencies, it requires selecting appropriate filters that are difficult for the less-skilled operators in applications of the Fourier based methods. To avoid this difficulty, spectrum correlation method is proposed in this study. The experimental results showed that the spectrum correlation method was able to detect the tool wear and chipping, but it is unable to find out the chipping position of the worn tool. For this reason, further investigation was made to the cutting forces. The experimental results showed that cutting forces increase sharply right after the tool chipping zone was engaged into the workpiece. A rapid change in cutting forces can itself be a good indicator to detect the tool failure. According to the force features received, the tool chipping detection and monitoring system, that has the capacity to recognize the chipping position of cutting tool, was developed. Experimental verification was conducted with a high degree of success. | en |
dc.description.provenance | Made available in DSpace on 2021-06-08T04:13:40Z (GMT). No. of bitstreams: 1 ntu-99-R97522732-1.pdf: 6123890 bytes, checksum: be267c69183f131344df0ff85d71cde6 (MD5) Previous issue date: 2010 | en |
dc.description.tableofcontents | 致謝 II
摘要 III Abstract IV 目 錄 V 圖目錄 VII 表目錄 XI 第一章 緒論 1 1.1 研究背景 1 1.2 研究目的 2 1.3 研究方法 2 1.4 論文架構 4 第二章 相關文獻回顧 5 2.1 簡介 5 2.2 刀具壽命 5 2.2.1 刀具磨損過程 7 2.2.2 刀具損壞機制 8 2.3 刀具狀態監測方法概敘 11 2.3.1 直接監測法 12 2.3.2 間接測量法 14 2.4 刀具壽命偵測理論及分析 18 2.5 小結 24 第三章 實驗與分析方法 26 3.1簡介 26 3.2分析方法 26 3.2.1 狀態空間之特徵向量法 26 3.2.2 傅立葉級數展開與傅立葉轉換 28 3.2.3 頻譜相關法 32 3.3 實驗設計與規劃 33 3.3.1 實驗設備 33 3.3.2 感測器簡介與安裝注意事項 39 3.3.3 實驗參數 44 3.3.4 實驗步驟與分析流程 46 第四章 實驗分析結果與討論 55 4.1 面銑加工之實驗及結果 55 4.1.1刀具狀態對時頻圖及頻譜相關性之影響 55 4.1.2加工參數對時頻圖及頻譜相關性之影響 73 4.1.3刀具狀態辨識方法及閾值建立 75 4.1.4 小結 79 4.2 成形車刀車削加工之實驗及結果 80 4.2.1 微崩刀具實驗規劃 80 4.2.2 成形刀具微崩對頻譜圖及頻譜相關性之影響 88 4.2.3 成形刀具微崩對切削力之影響 90 4.2.4 成形刀具微崩發生位置對切削力之影響 94 4.2.5 成形刀具微崩辨識方法及閾值建立 98 4.2.6 小結 104 第五章 結論與未來展望 105 5.1 結論 105 5.2 未來展望 106 參考文獻 108 | |
dc.language.iso | zh-TW | |
dc.title | 工具機刀具狀態偵測系統之開發研究 | zh_TW |
dc.title | Development of Tool Condition Detection and Monitoring System for Machine Tools | en |
dc.type | Thesis | |
dc.date.schoolyear | 98-2 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 陳炤彰,張復瑜,李貫銘 | |
dc.subject.keyword | 刀具狀態偵測,頻譜相關法,微崩偵測, | zh_TW |
dc.subject.keyword | tool condition monitoring,tool chipping detecting,spectrum correlation, | en |
dc.relation.page | 111 | |
dc.rights.note | 未授權 | |
dc.date.accepted | 2010-08-16 | |
dc.contributor.author-college | 工學院 | zh_TW |
dc.contributor.author-dept | 機械工程學研究所 | zh_TW |
顯示於系所單位: | 機械工程學系 |
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