Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/98456
標題: 訊號特徵擷取於銑削加工Inconel 718之表面粗糙度預測以及基因遺傳演算法之加工參數最佳化
Signal Feature Extraction for Predicting Surface Roughness of Inconel 718 in Milling Process and Cutting Parameter Optimization via Genetic Algorithm
作者: 謝士佑
Shih-Yo Hsieh
關鍵字: 銑削加工
表面粗糙度
相關性分析
倒傳遞類神經網路
包絡線分析
頻率正規化
基因遺傳演算法
加工參數最佳化
Milling Process
Surface Roughness
Correlation Analysis
Back-propagation Neural Networks
Envelope Analysis
Frequency Normalization
Genetic Algorithms
Milling Parameter optimization
引用: [1] Y. H. Tsai, J. C. Chen, S. J. Lou, 'An in-process surface recognition system based on neural networks in end milling cutting operations,' International Journal of Machine Tools and Manufacture, vol. 39 pp. 583-605. 1999. [2] 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. [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] S. Varghese, V. Radhakrishnan, 'A multi sensor approach to in-process monitoring of surface roughness,' Journal of materials processing technology,' vol.44, pp. 353-362, 1994. [5] D. Y. Jang, Y. G. Choi, H. G. Kim, A. Hsiao, 'Study of the correlation between surface roughness and cutting vibrations to develop an on-line roughness measuring technique in hard turning,' International Journal of Machine Tools and Manufacture, vol. 36, pp. 453-464, 1996. [6] 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. [7] O. B. Abouelatta, J. Madl, 'Surface roughness prediction based on cutting parameters and tool vibrations in turning operations,' Journal of materials processing technology, vol. 118, pp. 269-277, 2001. [8] W. H. Yang, Y. S. Tarng, 'Design optimization of cutting parameters for turning operations based on the Taguchi method,' Journal of materials processing technology, vol. 84, pp. 122-129,1998 [9] T. J. Ko, H. S. Kim, 'Autonomous cutting parameter regulation using adaptive modeling and genetic algorithms,' Precision Engineering, vol. 22, pp. 243-251, 1998. [10] C. Camposeco-Negrete, 'Optimization of cutting parameters for minimizing energy consumption in turning of AISI 6061 T6 using Taguchi methodology and ANOVA,' Journal of Cleaner Production, vol.53, pp. 195-203, 2013. [11] C. Y. Nian, W. H. Yang, Y. S. Tarng, 'Optimization of turning operations with multiple performance characteristics,' Journal of Materials Processing Technology, vol. 95, pp. 90-96,1999. [12] M. S. Shunmugam, S. V. B. Reddy, A. A. Narendran 'Selection of optimal conditions in multi-pass face-milling using a genetic algorithm,' International Journal of Machine Tools and Manufacture, vol. 40, pp. 401-414, 2003. [13] Z. Deng, H. Zhang, Y. Fu, L. Wan, W. Liu, 'Optimization of process parameters for minimum energy consumption based on cutting specific energy consumption,' Journal of Cleaner Production, vol. 166 pp. 1407-1414, 2017. [14] 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. [15] C. D. Lewis, 'Industrial and Business Forecasting Methods,' Butterworths, London, 1982.
摘要: 銑削加工的表面品質受到許多因素影響,如:切削速度、切削深度、進給速率等加工參數、機台振動、刀具磨耗等。傳統加工只憑藉著經驗或是試誤法決定加工參數,這樣不僅不易掌握加工的品質也增加了時間與成本,所以在要求的製造效率下如果能事先知道掌握加工品質以求解決參數最佳化是有其必要性。 本研究主要透過銑削加工難切削材料Inconel718之過程,同步量測其主軸與虎鉗振動與主軸電流訊號,在不同之加工參數設定下,如:每刃進給、切削深度與切削速度,探討工件表面粗糙度(Ra值),與振動訊號、加工參數、電流訊號特徵的關聯性,並運用類神經網路進行表面粗糙度預測。實驗中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、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,再將篩選出的特徵做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。 銑削參數最佳化是利用基因遺傳演算法做為最佳化的工具,藉由先前建構出的加工預測模型並使用此模型預測的Ra值為限制條件,以得到最大的體積移除率為目標,藉由基因遺傳演算法的過程得到加工參數,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值之差異跟參數最佳化的驗證。
URI: http://hdl.handle.net/11455/98456
文章公開時間: 2021-08-30
Appears in Collections:機械工程學系所

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