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研究生:雷凱崴
研究生(外文):Kai-Wei Lei
論文名稱:應用振動訊號與類神經網路於銑削加工之表面粗糙度分析與預測
論文名稱(外文):Analysis and Prediction for Surface Roughness of Milling Using Vibration Signal and Artificial Neural Network
指導教授:吳天堯
指導教授(外文):Tian-Yau Wu
口試委員:鄭志鈞盧銘詮
口試委員(外文):Chih-Chun ChengMing-Chyuan Lu
口試日期:2017-07-11
學位類別:碩士
校院名稱:國立中興大學
系所名稱:機械工程學系所
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:2017
畢業學年度:105
語文別:中文
論文頁數:65
中文關鍵詞:銑削加工中碳鋼表面粗糙度相關性分析倒傳遞類神經網路包絡線分析多尺度熵頻率正規化
外文關鍵詞:millingS45C steelsurface roughnesscorrelation analysisback propagation articifial neural networkenvelope analysismulti-scale entropyfrequency normalization
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本研究主要透過銑削加工中碳鋼S45C之過程,同步量測其主軸振動訊號,在不同之加工參數設定下,如:每刃進給、切削深度、虎鉗夾持力矩等,探討不同等級之工件表面粗糙度(Ra值),與振動訊號、加工參數的關聯性,並運用類神經網路進行表面粗糙度預測。實驗中藉由架設於主軸與虎鉗之加速規擷取振動訊號,透過不同之訊號處理與分析方法,如:包絡線分析、平均方根、峰度、偏度、多尺度熵、快速傅立葉轉換、以及頻率正規化,得到訊號特徵,並藉由相關性分析比較篩選出與表面粗糙度Ra值較具相關性之特徵,再將篩選出的特徵作為倒傳遞類神經網路之輸入層參數來進行表面粗糙度預測。分析結果亦比較與討論不同類別之特徵輸入的預測效果以及實際Ra值與預測Ra值之差異。
This study primarily investigates the correlation among the cutting parameters, the surface roughness level of S45C steel through the milling process and the vibration signals that are recorded synchronously. With different combinations of cutting parameters, such as: feed rate of per cut, cutting depth and clamping torgue of vise, the different levels of surface roughness are predicted by using the artificial neural network (ANN). The vibrations are measured by the accelerometers which are mounted on the spindle and the vise. The features of vibration signals are extracted through utilizing the envelope analysis, RMS (root-mean-square), kurtosis, skewness, fast Fourier transform (FFT) and frequency normalization. The features of higher priority are selected based on the analysis of correlation and then collected as the input layer parameters of ANN for surface roughness prediction. The prediction accuracy and results of using different classes of input features are also disscussed and compared.
誌謝 i
摘要 ii
Abstract iii
目錄 iv
圖目錄 vii
表目錄 x
第一章 緒論 1
1-1 前言 1
1-2 文獻回顧 2
1-3 研究動機 4
1-4 論文大綱 5
第二章 理論 6
2-1 包絡線分析(Envelope Analysis) 6
2-2 希爾伯特黃轉換(Hilbert-Huang Transform) 6
2-2-1 固有模態函數(Instrinsic Mode Functions) 7
2-2-2 經驗模態分解法(Emipirical Mode Decompostion) 7
2-3 取樣熵(Sample Entropy) 9
2-3-1 多尺度熵(Multiscale Entropy) 11
2-4 皮爾森相關性分析(Pearson Correlation Coefficient) 13
2-5 類神經網路(Artificial Neural Network) 14
2-5-1 學習規則 15
2-5-2 倒傳遞神經網路(Back Propagation Neural Network) 15
第三章 實驗架設與規劃 19
3-1 實驗架設 19
3-1-1 實驗加工設備 21
3-1-2 實驗加工刀具 21
3-1-3 實驗工件材料 22
3-1-4 擷取卡 23
3-1-5 加速規 24
3-1-6 表面粗糙度量測設備 25
3-2 實驗規劃 27
3-2-1 加工參數配置 27
3-2-2 訊號擷取與訊號取樣頻率 28
3-2-3 表面粗糙度量測方式 28
第四章 實驗結果與討論 30
4-1訊號分析流程 30
4-2穩定加工訊號擷取 31
4-3訊號趨勢項消除 32
4-4訊號包絡線分析 35
4-5時域訊號處理與分析 36
4-5-1多尺度熵 36
4-6頻域訊號處理與分析 37
4-6-1非轉速振動相關特徵討論 37
4-6-2頻率正規化與點數插補 39
4-7訊號特徵篩選 42
4-7-1時域訊號特徵相關性分析 43
4-7-2頻域訊號特徵相關性分析 49
4-8倒傳遞類神經網路 58
4-8-1倒傳遞類神經網路結果比較 59
第五章 結論與未來展望 62
5-1結論 62
5-2未來展望 63
參考文獻 64
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[2]C. Lu, “Study on prediction of surface quality in machining process,” Journal of Materials Processing Technology, vol. 205, pp. 439–450, 2008.
[3]İ Asiltürk, M. Çunkaş, “Modeling and prediction of surface roughness in turning operations using artificial neural network and multiple regression method,” Expert Systems With Applications, vol. 38, pp. 5826–5832, 2011.
[4]N. R. Abburi, U. S. Dixit, “A knowledge-based system for the prediction of surface roughness in turning process,” Robotics and Computer-Integrated Manufacturing, vol. 22, pp. 363–372, 2006.
[5]Y. Jiao, S. Lei, Z. J. Pei, E. S. Lee, “Fuzzy adaptive networks in machining process modeling: surface roughness prediction for turning operations,” International Journal of Machine Tools and Manufacture, Vol. 44, pp. 1643–1651, 2004.
[6]E. D. Kirby, Z. Zhang, J. C. Chen, “Development of an accelerometer-based surface roughness prediction system in turning operations using multiple regression techniques,” Journal of Industrial Technology, vol. 20, pp.1–8, 2004.
[7]H. K. Chang, J. H. Kim, I. H. Kim, D. Y. Jang, D. C. Han, “In-process surface roughness prediction using displacement signals from spindle motion,” International Journal of Machine Tools and Manufacture, vol. 47 , pp.1021-1026, 2007.
[8]O. B. Abouelatta, J. Mádl, “Surface roughness prediction based on cutting parameters and tool vibrations in turning operations,” Journal of Materials Processing Technology, vol. 118, pp.269-277, 2001.
[9]M. Elangovan, N. R. Sakthivel,S. Saravanamurugan, B. B. Nair, V. Sugumaran, “Machine learning approach to the prediction of surface roughness using statistical features of vibration signal acquired in turning,” Procedia Computer Science, vol. 50, pp.282-288, 2015.
[10]E. Daniel Kirby, Joseph C. Chen, “Development of a fuzzy-nets-based surface roughness prediction system in turning operations,” Computers & Industrial Engineering, vol. 53, pp.30-42, 2007.
[11]N. E. Huang, Z. Shen, S. R. Long, M. L. C. Wu, H. H. Shih, Q. N. Zheng, N. C. Yen, C. C. Tung, and H. H. Liu, “The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis,” Proceedings of the Royal Society of London Series a-Mathematical Physical and Engineering Sciences, vol. 454, pp. 903-995, 1998.
[12]C. D. Lewis, “Industrial and Business Forecasting Methods,” Butterworths, London, 1982.
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