Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/97187
標題: 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
Analysis and Prediction for Surface Roughness of Milling Using Vibration Signal and Artificial Neural Network
作者: 雷凱崴
Kai-Wei Lei
關鍵字: 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
milling
S45C steel
surface roughness
correlation analysis
back propagation articifial neural network
envelope analysis
multi-scale entropy
frequency normalization
引用: [1]D. Karayel, “Prediction and control of surface roughness in CNC lathe using artificial neural network,” Journal of Materials Processing Technology, vol. 209, pp. 3125-3137, 2009. [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.
摘要: 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.
URI: http://hdl.handle.net/11455/97187
文章公開時間: 10000-01-01
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