臺灣博碩士論文加值系統

在沖孔製程中，當沖頭磨耗嚴重時，會影響產品品質，此外，沖頭斷裂時，會影響產品合格率。因此，沖孔製程沖頭失效的即時監控也就變得相當重要，然而，世界現有的沖壓製程監控卻相當稀少。
本研究的目的是探討沖孔製程中，沖頭損壞的及時監控。本研究藉由在精密沖床裝設負荷計、麥克風與加速規，量測沖頭在精微沖孔生命週期的負載、噪音與振動訊號，並與對應期間沖頭磨耗及板材毛邊建立關聯，作為沖孔製程以感測器線上及時監測沖頭損壞的依據。
本研究實驗是以沖頭直徑0.8mm微沖孔加工為例，板材材質為不鏽鋼SUS304，板材厚度0.5mm，沖壓機速度為50rpm。實驗結果顯示，沖頭斷裂前，上模振動訊號在頻率280Hz~500Hz間的峰值明顯變大，下模振動訊號則是在頻率400Hz~750Hz間的峰值有明顯變大，噪音訊號在頻率300Hz~450Hz間的峰值也有變大。在沖切過程，隨著沖切次數增加，沖頭刀刃磨耗逐漸增大，板材毛邊增高，沖切力亦會增加。此外，重現性實驗結果可知，兩次沖頭斷裂時的沖切次數誤差在10%以內。

In the piercing process, when the wear of punch becomes serious, will affect the quality of the product. In addition, the broken punch will affect the product percent of the pass. As a result, the immediate monitoring of the punch failure in the piercing process becomes more important. However, the existing monitoring of punching process is rather scarce in the world.
The purpose of this study is to investigate the immediate monitoring of punch damage in the cutting process. In this paper, the load, noise, and vibration signals are measured in the micro precision punching life cycle by the load meter, the microphone and accelerating gage tha set up in the precision punching machine. And associated with the punch wear and the bur of the board during the corresponding period. The piercing process is based on the sensor to on-line monitor the damage of the punch in time.
This study is based on micro-punching processing used the punch diameter of 0.8mm, the plate material for the stainless steel(SUS304), sheet thickness is 0.5mm, punching machine speed of 50rpm. The experimental results show, before punch broken , the peak vibration signal between the frequency of 280Hz ~ 500Hz obviously increased when the punch nearly broken, the lower mode vibration signal is obviously larger in the frequency between 400Hz ~ 750Hz, the noise signal is at the frequency of 300Hz ~ 450Hz between the peak also increased. In the cutting process, with the number of punching increased, the wear of cutting edge of punch is gradually increased, bur of the sheet is increased, punching force will increase. In addition, the reproducibility test results show that when the punch broke, the punching times of the two punches are less than 10%.

目錄
中文摘要 I
英文摘要 II
致謝 IV
目錄 V
圖目錄 VII
表目錄 IX
第1章 緒論 1
1.1 前言 1
1.2 文獻回顧 2
1.3 研究動機與目的 4
第2章 基礎理論與實驗原理 6
2.1 沖孔加工之基本理論與模擬分析 6
2.1.1 基本理論 6
2.1.2 模擬分析 8
2.2 訊號分析 8
2.2.1 FFT的基本原理 9
2.2.2 FFT的訊號流程圖 11
第3章 研究方法與流程 13
3.1 實驗儀器及監控系統建置 14
3.1.1 噪音量測 15
3.1.2 沖切成形力量測 16
3.1.3 振動量測 18
3.1.4 訊號擷取器 19
3.2 沖孔實驗模具開發 21
3.3 沖孔實驗設備 22
3.4 模具元件與板材毛邊量測設備 24
第4章 結果與討論 27
4.1 沖切訊號 27
4.1.1 噪音訊號 27
4.1.2 振動訊號 29
4.1.3 沖切成形力 31
4.2 失效訊號 32
4.2.1 沖切訊號時域圖 32
4.2.2 沖切成形力變化 36
4.3 模具元件與板材毛邊量測 37
4.3.1 模具元件 37
4.3.2 板材毛邊 39
4.4 重現性實驗 41
4.4.1 重現性實驗訊號時域圖 41
4.4.2 重現性實驗頻域圖 43
4.4.3 重現性實驗沖切成形力變化 46
4.4.4 重現性實驗訊號比對 47
第5章 結論與未來展望 50
5.1 結論 50
5.2 未來展望 52
參考文獻 53



圖目錄
圖 1 1監控系統建置與振動訊號[16] 2
圖2 1材料剪切過程[26] 7
圖2 2材料剪切過程中沖壓力的變化 (a)AB彈性變形過程 (b)BD塑性變形過程 (c)DE撕裂過程[26] 7
圖 2 3金屬板剪斷之斷面[26] 8
圖 2 4蝴蝶運算流程圖[27] 11
圖 2 5時間抽取法之FFT訊號流程圖[27] 12
圖 3 1研究流程圖 13
圖 3 2切模具與監控設備 14
圖 3 3克風收音器(GRAS 26CA 1/2”CCP) 15
圖 3 4荷重規(208C05)，資料來源：PCB Piezotronics 17
圖 3 5單軸加速規(603C01)，資料來源：PCB Piezotronics 18
圖 3 6四通道即時分析儀，資料來源：基太克國際股份有限公司 20
圖 3 7沖孔模具設計圖 21
圖 3 8沖切模具實體 22
圖 3 9 3噸精密伺服沖床 23
圖 3 10共軛焦顯微鏡(VK-X100K/X200K) 25
圖 3 11點自動對焦探頭表面紋理測量儀器(PF-60,Mitaka) 26
圖 4 1噪音時域訊號-有沖切板材與無沖切板材比較 28
圖 4 2噪音頻域訊號-有沖切板材與無沖切板材比較 28
圖 4 3振動時域訊號-有沖切板材與無沖切板材比較(a)上模加速規(b)下模加速規 29
圖 4 4振動頻域訊號-有沖切板材與無沖切板材比較(a)上模加速規(b)下模加速規 30
圖 4 5模擬介面圖 31
圖 4 6沖切成形力-實驗與分析比對 31
圖 4 7麥克風量測到的噪音時域訊號 32
圖 4 8上模加速規量測到的振動時域訊號 33
圖 4 9下模加速規量測到的振動時域訊號沖切訊號頻域圖 33
圖 4 10噪音頻域訊號 34
圖 4 11振動頻域訊號-上模加速規 35
圖4 12振動頻域訊號-下模加速規 35
圖 4 13沖切成形力變化 36
圖 4 14沖頭磨耗前後比較圖 38
圖 4 15下模穴磨耗前後比較圖 39
圖 4 16板材毛邊狀況比較(a)初始沖切(b)沖切200次(c)沖切460次[沖頭磨耗](d)沖頭沖斷前 40
圖 4 17麥克風量測到的噪音時域訊號(a)初始沖切(b)沖切200次(c)沖切400次 (d)沖切460次[訊號改變](e)沖頭沖斷前(f)沖切490次沖頭沖斷 41
圖 4 18上模加速規量測到的振動時域訊號(a)初始沖切(b)沖切200次(c)沖切400次(d)沖切460次[訊號改變](e)沖頭沖斷前(f) 沖切490次沖頭沖斷 42
圖 4 19下模加速規量測到的振動時域訊號(a)初始沖切(b)沖切200次(c)沖切400(d)沖切460次[訊號改變](e)沖頭沖斷前(f) 沖切490次沖頭沖斷 43
圖 4 20噪音頻域訊號 44
圖 4 21振動頻域訊號-上模加速規 45
圖 4 22動頻域訊號-下模加速規 45
圖 4 23沖切成形力變化 46
圖 4 24重現性實驗比對-前後次實驗噪音頻域訊號 47
圖 4 25重現性實驗比對-前後次實驗振動頻域訊號比較(a)上模加速規(b)下模加速規 48
圖 4 26重現性實驗比對-前後次實驗沖切力量訊號比較 49

表目錄
表 2 1 FFT與DFT之演算法比較[27] 9
表 3 1麥克風收音器規格(GRAS 26CA 1/2”CCP) 16
表 3 2荷重規規格(208C05)，資料來源：PCB Piezotronics 17
表 3 3單軸加速規規格(603C01)，資料來源：PCB Piezotronics 19
表 3 4四通道即時分析儀規格表，資料來源：基太克國際股份有限公司 20
表 3 5 3噸精密伺服沖床規格表 23
表 3 6形狀分析雷射顯微鏡(VK-X100K/X200K)規格表 25
表 3 7 點自動對焦探頭表面紋理測量儀器(PF-60,Mitaka)規格表 26
表 4 1實驗參數 27

參考文獻
[1] R. E. Haber, J. E. Jiménez, C. R. Peres, J. R. Alique, 2004, “An investigation of tool-wear monitoring in a high-speed machining process”, Sensors and Actuators A: Physical, 116, 539-545.
[2] A. G. Rehorn, J. Jiang, P. E. Orban, 2004, “State-of-the-art methods and results in tool condition monitoring”, The International Journal of Advanced Manufacturing Technology, 26, 693-710.
[3] C. Husson, J. P. M. Correia, L. Daridon, S. Ahzi, 2008, “ Finite elements simulations of thin copper sheets blanking”, Study of blanking parameters on sheared edge quality. Journal of Materials Processing Technology, 199, 74-83.
[4] H. Makich, L. Carpentier, G. Monteil, X. Roizard, J. Chambert, P. Picart, 2008, “Metrology of the burr amount - correlation with blanking operation”, International Journal of Material Forming, 1: 1243-1246.
[5] R. Hambli, F. Guerin, B. Dumon, 2003, “Numerical evaluation of the tool wear influence on metal-punching processes”, The International Journal of Advanced Manufacturing Technology, 21, 483–493.
[6] R. Hambli, 2002, “Prediction of burr height formation in blanking processes using neural network”, International Journal of Mechanical Sciences, 44, 2089–2102.
[7] H. G. Shin, Y. S. Shin, B. H. Kim, H. Y. Kim, 2005, “Burr Control in Meso-Punching Process”, Journal of Mechanical Science and Technology, 19, 968~975.
[8] L. C. da Silva, P. R. da Mota, M. B. da Silva, M. B. Sales, Á. R. Machado, M. J. Jackson, 2016, “Burr height minimization using the response surface methodology in milling of PH 13-8 Mo stainless steel”, The International Journal of Advanced Manufacturing Technology, 87(9-12), 3485-3496.
[9] C. W. De Silva, 2007, “Vibration monitoring testing and instrumentation”, Taylor & Francis, Boca Raton, Florida
[10] G. Byrne, D. Dornfeld, I. Inasaki, G. Ketteler, W. Konig, R. Teti, 1995, “Tool condition monitoring(TCM) — the status of research and industrial application”, CIRP Annals - Manufacturing Technology, 44:541–567.
[11] C. K. H. Koh, J. Shi, W. J. William, J. Ni, 1999, “Multiple fault detection and isolation using the haar transform, Part 2: Application to the stamping process”, Journal of Manufacturing Science and Engineering 121: 295-299.
[12] J. Jin, J. Shi, 2000, “Diagnostic feature extraction from stamping tonnage signals based on design of experiments”, Journal of Manufacturing Science and Engineering, 122:360-369.
[13] W. Klingenberg, 2004, “Principles for on-line monitoring of tool wear during sheet metal punching”, Proceedings of the 34th International MATADOR Conference, Springer-Verlag London Limited,169-174
[14] J. Breitling, B. Pfeiffer, T. Altan, K. Siegert, 1997, “Process control in blanking”, Journalof Materials Processing Technology 71:187-192
[15] G. C Zhang, M. Ge, H. Tong, Y. Xu, R. Du, 2002, “Bispectral analysis for on-line monitoring of stamping operation”, Engineering Applications of Artificial Intelligence 15:97–104
[16] J. Z. Zhang, J.C. Chen, 2008, “Tool condition monitoring in an end-milling operation based on the vibration signal collected through a microcontroller-based data acquisition system”, The International Journal of Advanced Manufacturing Technology, 39:118-128.
[17] J. C. Chen, W. L. Chen, 1999, “A tool breakage detection system using an accelerometer sensor”, Journal of Intelligent Manufacturing, 10:187-197.
[18] P. Y. Sevilla-Camacho, J. B. Robles-Ocampo, J. Muñiz-Soria, F. Lee-Orantes, 2015, “Tool failure detection method for high-speed milling using vibration signal and reconfigurable bandpass digital filtering”, The International Journal of Advanced Manufacturing Technology, 81:1187-1194
[19] W. Rmili, A. Ouahabi, R. Serra, R. Leroy, 2016, “An automatic system based on vibratory analysis for cutting tool wear monitoring”, Measurement, 77:117–123
[20] W. H. Hsieh, M. C. Lu, S. J. Chiou, 2012, “Application of backpropagation neural network for spindle vibration-based tool wear monitoring in micro-milling”, The International Journal of Advanced Manufacturing Technology, 61:53–61
[21] D. E. Dimla, 2002, “The correlation of vibration signal features to cutting tool wear in a metal turning operation”, The International Journal of Advanced Manufacturing Technology, 19:705-713.
[22] F. J. Alonso, D.R. Salgado, 2008, “Analysis of the structure of vibration signals for tool wear detection”, Mechanical Systems and Signal Processing, 22:735-748.
[23] S. Takata, 1986, “A Sound Monitoring System for Fault Detection of Machine and Machining States”, CIRP Annals - Manufacturing Technology, 35:289-292.
[24] H. Rafezi, M. Behzad., J. Akbari, 2012, “Time Domain and Frequency Spectrum Analysis of Sound”, International Journal of Computer and Electrical Engineering, 4.
[25] M. C. Lu, B. S. Wan, “Study of high-frequency sound signals for tool wear monitoring in micromilling”, International Journal of Computer and Electrical Engineering, 2013;66:1785–1792.
[26] Http://www.docin.com/p-19084500.html，沖壓基礎知識
[27] http://eshare.stust.edu.tw/EshareFile/2010_6/2010_6_a78298c9.pdf