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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 蔡曜陽 | zh_TW |
| dc.contributor.advisor | Yao-Yang Tsai | en |
| dc.contributor.author | 呂季軒 | zh_TW |
| dc.contributor.author | Chi-Hsuan Lu | en |
| dc.date.accessioned | 2024-08-16T16:15:23Z | - |
| dc.date.available | 2024-08-17 | - |
| dc.date.copyright | 2024-08-16 | - |
| dc.date.issued | 2024 | - |
| dc.date.submitted | 2024-08-10 | - |
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Zhao, "Study on subsurface damage behavior in ductile ultra-precision grinding of sapphire based on acoustic emission signal processing," Journal of Manufacturing Processes, vol. 109, pp. 326-344, 2024/01/17/ 2024, doi: https://doi.org/10.1016/j.jmapro.2023.11.046. [15] V. Vy, Y. Lee, J. Bak, S. Park, S. Park, and H. Yoon, "Damage localization using acoustic emission sensors via convolutional neural network and continuous wavelet transform," Mechanical Systems and Signal Processing, vol. 204, p. 110831, 2023/12/01/ 2023, doi: https://doi.org/10.1016/j.ymssp.2023.110831. [16] D. Y. Kononenko, V. Nikonova, M. Seleznev, J. van den Brink, and D. Chernyavsky, "An in situ crack detection approach in additive manufacturing based on acoustic emission and machine learning," Additive Manufacturing Letters, vol. 5, p. 100130, 2023/04/01/ 2023, doi: https://doi.org/10.1016/j.addlet.2023.100130. [17] V. Nasir, S. Dibaji, K. Alaswad, and J. 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Subbiah, "Performance machinability through electrochemical grinding of strenx steel," Materials Today: Proceedings, vol. 45, pp. 2479-2481, 2021/01/01/ 2021, doi: https://doi.org/10.1016/j.matpr.2020.11.075. [21] H. Kong, Y. Liu, X. Zhu, and T. Peng, "Study on Ultrasonic Assisted Electrochemical Drill-Grinding of Superalloy," Chemosensors, vol. 8, p. 62, 08/03 2020, doi: 10.3390/chemosensors8030062. [22] 楊宗儫, "探討陶瓷基鋁碳化矽於電化學加工反應層之研究", 機械工程學研究所, 台灣大學, 台北市, 2023. doi:10.6342/NTU202303704 [23] N. S. Qu, Q. L. Zhang, X. L. Fang, E. K. Ye, and D. Zhu, "Experimental Investigation on Electrochemical Grinding of Inconel 718," Procedia CIRP, vol. 35, pp. 16-19, 2015/01/01/ 2015, doi: https://doi.org/10.1016/j.procir.2015.08.055. [24] Y. Ge, Z. Zhu, and Y. Zhu, "Electrochemical deep grinding of cast nickel-base superalloys," Journal of Manufacturing Processes, vol. 47, pp. 291-296, 2019/11/01/ 2019, doi: https://doi.org/10.1016/j.jmapro.2019.10.007. [25] J. Bohse and A. J. 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Wu, "A review on Hilbert-Huang transform: Method and its applications to geophysical studies," Reviews of Geophysics, vol. 46, no. 2, 2008/06/01 2008, doi: https://doi.org/10.1029/2007RG000228. [35] E. S. Gadelmawla, M. M. Koura, T. M. A. Maksoud, I. M. Elewa, and H. H. Soliman, "Roughness parameters," Journal of Materials Processing Technology, vol. 123, no. 1, pp. 133-145, 2002/04/10/ 2002, doi: https://doi.org/10.1016/S0924-0136(02)00060-2. | - |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/94474 | - |
| dc.description.abstract | 電化學磨削(Electrochemical grinding , ECG)是結合電化學與磨削作用的特性，但在實際加工中往往需要人工經驗判斷加工過程中的種種現象，為了實現自動監控加工過程，本研究將加速規和聲發射感測器安裝在機台上，加速規訊號經濾波後使用短時傅立葉時頻譜進行分析，聲發射感測器則使用快速傅立葉轉換呈現結果。
研究中發現聲發射感測器可以偵測材料硬度變化時的頻域變化，而加速規則是可以利用時頻譜判斷在實驗過程中所發生的預警現象(火花特徵)，並且在加工過程中也發現機台存在著螺桿背隙以及平台傾斜之問題，導致後續磨削中的電化學反應不穩定。 研究利用田口L27直交表進行實驗，探討加工參數為脈衝電壓、脈衝時間、轉速、平移速率以及深度，實驗結果顯示，不同磨削深度和平移速率對頻率響應有明顯影響，深度增加會導致更強的頻率，而較高的平移速率會增加火花特徵的發生機率。 在電化學反應方面，加工過程中的深度過深和平移速率過快會導致實驗過程中轉變為電化學放電反應，而此電化學放電反應的結果會使試片的硬度和表面粗糙度均比穩定電化學反應的結果更差。本研究也探討了不同平移速率下脈衝電壓對材料組織的影響，結果顯示，隨著脈衝電壓的增加，促進鋁元素的移除，並且材料的孔隙率主要受到脈衝電壓的影響。平移速率的增加對材料孔隙率的影響較小，表明平移速率對於材料結構的影響相對有限。 | zh_TW |
| dc.description.abstract | Electrochemical grinding (ECG) is a process that combines the characteristics of both electrochemical action and grinding. However, in practical machining, various phenomena often need to be judged based on human experience. To achieve automatic monitoring of the machining process, this study installed accelerometers and acoustic emission sensors on the machine. The accelerometer signals were analyzed using Short-Time Fourier Transform (STFT) after filtering, while the acoustic emission sensor results were presented using Fast Fourier Transform (FFT).
The study found that the acoustic emission sensor can detect changes in the frequency domain when there are variations in material hardness, while the accelerometer can utilize the time-frequency spectrum to identify warning phenomena (such as spark characteristics) that occur during the experiment. Furthermore, during the machining process, it was also discovered that there were issues with the machine, such as screw backlash and platform tilt, leading to instability in the electrochemical reactions during subsequent grinding. The study conducted experiments using a Taguchi L27 orthogonal array to investigate machining parameters such as pulse voltage, pulse duration, spindle speed, feed rate, and depth. The experimental results showed that different grinding depths and feed rates have a significant impact on the frequency response, with increased depth leading to stronger frequency signals and higher feed rates increasing the likelihood of spark characteristics. In terms of electrochemical reactions, excessive depth and too fast feed rate during the machining process can result in the transition to electrochemical discharge reactions, leading to poorer hardness and surface roughness of the test piece compared to stable electrochemical reactions. This study also explored the impact of pulse voltage on the material structure at different feed rates. The results showed that as the pulse voltage increased, the removal of aluminum elements was promoted, and the material's porosity was primarily influenced by the pulse voltage. The increase in feed rate had a relatively minor effect on material porosity, indicating that feed rate has a limited impact on material structure. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2024-08-16T16:15:22Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2024-08-16T16:15:23Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | 口試委員會審定書 i
誌謝 ii 中文摘要 iii 英文摘要 iv 目次 vi 圖次 x 表次 xvi 第1章 緒論 1 1.1 前言 1 1.2 文獻回顧 2 1.2.1 感測器在工程領域中之應用文獻 2 1.2.2 訊號處理方面文獻 4 1.2.3 感測器與機器學習方面文獻 5 1.2.4 電化學磨削技術相關文獻 8 1.3 研究動機與目的 10 1.4 論文大綱 12 第2章 基礎理論 13 2.1聲發射(AE)理論 13 2.2傅立葉轉換理論 14 2.2.1短時傅立葉轉換 15 2.3 電化學磨削理論 16 2.4田口方法 (Taguchi Method) 17 2.4.1 影響因子 18 2.4.2 田口直交表 19 2.4.3 訊號雜訊比 (S/N比) 20 2.4小波包轉換 (Discrete Wavelet Transform, DWT) 21 2.5經驗模態分解 (Empirical Mode Decomposition) 21 2.6希爾伯特轉換 (Hilbert transform) 23 2.7表面粗糙度理論 25 2.6.1 表面粗糙度/輪廓評定參數 25 第3章 實驗設備與規劃 27 3.1實驗設備與儀器 27 3.1.1 鋁碳化矽 27 3.1.3 電化學磨削機 28 3.1.4 PXIe-1078 訊號擷取系統 30 3.1.5 PXI-6132擷取卡 30 3.1.6 TB 2709接線盒 31 3.1.7 聲發射感測器 32 3.1.8 聲發射感測放大器 33 3.1.9 加速規 35 3.1.10 酸鹼度計 36 3.1.11 電源供應器 38 3.1.12 TEKTRONIX AM503S電流量測系統 39 3.1.13 桌上型數位示波器 40 3.1.14 表面粗度測定儀 41 3.1.15 洛氏硬度計 42 3.1.16 非接觸3D自動聚焦輪廓儀 44 3.1.17 掃描式電子顯微鏡 45 3.2 研究規劃架構 46 3.2.1 實驗量測流程 46 3.2.2 訊號數據處理 48 3.3 實驗配置 49 3.3.1 實驗參數設定 51 第4章 實驗結果與討論 53 4.1電化學磨削之實驗結果 53 4.1.1聲發射感測器-FFT結果 53 4.1.1.1比較不同脈衝電壓之AE訊號 65 4.1.2加速規-時頻譜解釋 71 4.1.3 AE感測器與加速規之比較 76 4.1.4磨削振動平穩性與表面深度不均勻 80 4.1.5不均勻接觸訊號特徵 84 (1) 不均勻接觸訊號-參數分析 84 4.2磨削參數與訊號之影響 91 4.2.1深度與分貝(db)的影響 92 4.2.1.1轉速400 rpm與平移速率10 mm/min分貝(db)變化 93 4.2.1.2轉速600 rpm與平移速率15 mm/min分貝(db)變化 99 4.2.1.3轉速800 rpm與平移速率25 mm/min分貝(db)變化 106 4.2.2平移速率與分貝(db)的影響 114 4.2.2.1轉速400 rpm與深度0.1 mm分貝(db)變化 114 4.2.2.2轉速600 rpm與深度0.2 mm分貝(db)變化 117 4.2.2.3轉速800 rpm與深度0.3 mm分貝(db)變化 120 4.3訊號分析預警 123 (1) 訊號之電流 123 (2) 燒焦反應 128 (3) 火花特徵週期性 130 4.3.1磨棒磨損之訊號比較 132 4.4電化學反應表現(電流/硬度/表面粗糙度) 135 4.4.1電化學與硬度表現 135 4.4.2電化學與表面粗糙度表現 137 4.4.3電化學材料組織影響 148 (1) EDS分析結果 149 (2) 材料表面孔隙率 150 第5章 結論與未來展望 152 5.1結論 152 5.2未來展望 154 參考文獻 155 | - |
| dc.language.iso | zh_TW | - |
| dc.subject | 加速規 | zh_TW |
| dc.subject | 電化學磨削 | zh_TW |
| dc.subject | 短時傅立葉轉換 | zh_TW |
| dc.subject | 快速傅立葉轉換 | zh_TW |
| dc.subject | 聲發射感測器 | zh_TW |
| dc.subject | Acoustic Emission Sensor | en |
| dc.subject | Fast Fourier Transform | en |
| dc.subject | Short-Time Fourier Transform | en |
| dc.subject | Accelerometer | en |
| dc.subject | Electrochemical Grinding | en |
| dc.title | 電化學磨削健康診斷系統 | zh_TW |
| dc.title | Health Diagnosis System for Electrochemical Grinding | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 112-2 | - |
| dc.description.degree | 碩士 | - |
| dc.contributor.oralexamcommittee | 盧銘詮;黃晧庭 | zh_TW |
| dc.contributor.oralexamcommittee | Ming-Chyuan Lu;Hao-Ting Huang | en |
| dc.subject.keyword | 電化學磨削,加速規,聲發射感測器,快速傅立葉轉換,短時傅立葉轉換, | zh_TW |
| dc.subject.keyword | Electrochemical Grinding,Accelerometer,Acoustic Emission Sensor,Fast Fourier Transform,Short-Time Fourier Transform, | en |
| dc.relation.page | 157 | - |
| dc.identifier.doi | 10.6342/NTU202404072 | - |
| dc.rights.note | 同意授權(限校園內公開) | - |
| dc.date.accepted | 2024-08-13 | - |
| dc.contributor.author-college | 工學院 | - |
| dc.contributor.author-dept | 機械工程學系 | - |
| 顯示於系所單位： | 機械工程學系 | |
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