基於多感測器訊號與機器學習技術之刀具磨耗分類與預測

簡易檢索 / 詳目顯示

回結果列表

研究生：	葉幸哲 Xing-Zhe Ye
論文名稱：	基於多感測器訊號與機器學習技術之刀具磨耗分類與預測 Tool Wear Classification and Prediction based on Multiple Sensors and Machine Learning Techniques
指導教授：	劉孟昆 Meng-Kun Liu
口試委員:	藍振洋 Chen-Yang Lan 劉益宏 Yi-Hung Liu
學位類別：	碩士 Master
系所名稱：	工程學院 - 機械工程系 Department of Mechanical Engineering
論文出版年：	2022
畢業學年度：	110
語文別：	中文
論文頁數：	107
中文關鍵詞：	刀具磨耗、特徵指標、特徵提取、特徵選擇、特徵降維、迴歸模型、分類模型、共線性診斷、支持向量機、隨機森林迴歸
外文關鍵詞：	Tool wear, features indexes, feature extraction, feature selection, feature dimensionality reduction, regression model, classification model, collinearity diagnosis, support vector machine, random forest regression
相關次數：	點閱：204 下載：0
分享至:	分享至facebook 分享至twitter

查詢本校圖書館目錄查詢臺灣博碩士論文知識加值系統勘誤回報

摘要    I
Abstract    II
誌謝    IV
目錄    V
表目錄    VIII
圖目錄    X
第一章、緒論    1
1 研究背景與動機    1
2 論文架構    3
第二章、文獻回顧    4
1 刀具磨耗    4
2 特徵處理    5
2.1 特徵前處理    5
2.2 特徵提取    5
2.3 特徵選擇    6
3 機器學習    7
3.1 機器學習之分類    8
3.2 迴歸分析與預測    10
第三章、研究方法    11
1 實驗規劃    11
1.1 實驗設備與設置    11
1.2 刀具磨耗實驗    15
1.3 磨耗結果    17
1.4 實驗流程    18
2 特徵提取    20
2.1 時域特徵    20
2.2 頻域特徵    23
3 特徵選擇    24
3.1 變異數分析(analysis of variance, ANOVA)    24
3.2 皮爾森相關係數(Pearson correlation coefficient, PCC)    25
4 特徵降維    26
4.1 線性判別分析(Linear discriminant analysis, LDA)    26
4.2 主成分分析(principal component analysis, PCA)    27
5 共線性診斷    28
5.1 方差膨脹因子(variance inflation factor, VIF)    28
5.2 迴歸模型正規化    29
6 支持向量機(support vector machine, SVM)    31
7 隨機森林迴歸    35
第四章、分類結果分析    37
1 分類分析流程    37
1.1 無獨立特徵集之分析流程    38
1.2 有獨立特徵集之分析流程    40
2 ANOVA特徵選擇    42
2.1 ANOVA (無獨立特徵集)    42
2.2 ANOVA (有獨立特徵集)    45
3 特徵降維    48
3.1 PCA降維分析(無獨立特徵集)    48
3.2 PCA降維分析(有獨立特徵集)    49
3.3 LDA降維分析    50
4 分類結果    51
第五章、迴歸分析    55
1 迴歸分析流程    55
2 迴歸模型    57
3 線性迴歸模型分析    58
3.1 皮爾森相關係數(PCC)    58
3.2 VIF迴歸分析    60
3.3 LASSO迴歸分析    62
3.4 PCR分析    63
2.5 線性迴歸分析總結    65
4 非線性迴歸模型分析    66
4.1 VIF迴歸分析    66
4.2 LASSO迴歸分析    67
4.3 PCR分析    67
4.4 非線性迴歸分析總結    68
5 迴歸模型與機器學習模型比較    71
第六章、結果討論與未來展望    65
1 分類分析結果討論    65
2 迴歸分析結果討論    66
3 未來展望    68
第七章、參考文獻    69
附錄A、刀具磨耗圖    73
附錄B、各實驗皮爾森相關係數    74
附錄C、各實驗主成分累積貢獻比    77
附錄D、各實驗共線性診斷後平均刀具磨耗預測曲線    80
附錄E、混合特徵集特徵處理後結果    83




                                

[1]Vetrichelvan, G., Sundaram, S., Kumaran, S. S., & Velmurugan, P. (2015). An investigation of tool wear using acoustic emission and genetic algorithm. Journal of Vibration and Control, 21(15), 3061-3066.
[2]Liu, C., Wang, G. F., & Li, Z. M. (2015). Incremental learning for online tool condition monitoring using Ellipsoid ARTMAP network model. Applied Soft Computing, 35, 186-198.
[3]Byrne, G., Dornfeld, D., Inasaki, I., Ketteler, G., König, W., & Teti, R. (1995). Tool condition monitoring (TCM)—the status of research and industrial application. CIRP annals, 44(2), 541-567.
[4]Pfeifer, T., & Wiegers, L. (2000). Reliable tool wear monitoring by optimized image and illumination control in machine vision. Measurement, 28(3), 209-218.
[5]Cheng, W. N., Cheng, C. C., Lei, Y. H., & Tsai, P. C. (2020). Feature selection for predicting tool wear of machine tools. The International Journal of Advanced Manufacturing Technology, 111(5), 1483-1501.
[6]Ghosh, N., Ravi, Y. B., Patra, A., Mukhopadhyay, S., Paul, S., Mohanty, A. R., & Chattopadhyay, A. B. (2007). Estimation of tool wear during CNC milling using neural network-based sensor fusion. Mechanical Systems and Signal Processing, 21(1), 466-479.
[7]Nouri, M., Fussell, B. K., Ziniti, B. L., & Linder, E. (2015). Real-time tool wear monitoring in milling using a cutting condition independent method. International Journal of Machine Tools and Manufacture, 89, 1-13.
[8]Dimla Sr, D. E., & Lister, P. M. (2000). On-line metal cutting tool condition monitoring.: I: force and vibration analyses. International Journal of Machine Tools and Manufacture, 40(5), 739-768.
[9]Shi, X., Wang, R., Chen, Q., & Shao, H. (2015). Cutting sound signal processing for tool breakage detection in face milling based on empirical mode decomposition and independent component analysis. Journal of Vibration and Control, 21(16), 3348-3358.
[10]Nayak, S. C., Misra, B. B., & Behera, H. S. (2014). Impact of data normalization on stock index forecasting. International Journal of Computer Information Systems and Industrial Management Applications, 6(2014), 257-269.
[11]Ahmad, T., & Aziz, M. N. (2019). Data preprocessing and feature selection for machine learning intrusion detection systems. ICIC Express Lett, 13(2), 93-101.
[12]Caggiano, A. (2018). Tool wear prediction in Ti-6Al-4V machining through multiple sensor monitoring and PCA features pattern recognition. Sensors, 18(3), 823.
[13]Zhou, Y., Sun, B., Sun, W., & Lei, Z. (2020). Tool wear condition monitoring based on a two-layer angle kernel extreme learning machine using sound sensor for milling process. Journal of Intelligent Manufacturing, 1-12.
[14]Zhang, K. F., Yuan, H. Q., & Nie, P. (2015). A method for tool condition monitoring based on sensor fusion. Journal of Intelligent Manufacturing, 26(5), 1011-1026.
[15]Benkedjouh, T., Medjaher, K., Zerhouni, N., & Rechak, S. (2015). Health assessment and life prediction of cutting tools based on support vector regression. Journal of intelligent manufacturing, 26(2), 213-223.
[16]Guyon, I., & Elisseeff, A. (2003). An introduction to variable and feature selection. Journal of machine learning research, 3(Mar), 1157-1182.
[17]Neef, B., Bartels, J., & Thiede, S. (2018, July). Tool wear and surface quality monitoring using high frequency CNC machine tool current signature. In 2018 IEEE 16th International Conference on Industrial Informatics (INDIN) (pp. 1045-1050). IEEE.
[18]Li, Z., Liu, R., & Wu, D. (2019). Data-driven smart manufacturing: tool wear monitoring with audio signals and machine learning. Journal of Manufacturing Processes, 48, 66-76.
[19]Wu, D., Jennings, C., Terpenny, J., Gao, R. X., & Kumara, S. (2017). A comparative study on machine learning algorithms for smart manufacturing: tool wear prediction using random forests. Journal of Manufacturing Science and Engineering, 139(7).
[20]Mahesh, B. (2020). Machine learning algorithms-a review. International Journal of Science and Research (IJSR).[Internet], 9, 381-386.
[21]Raschka, S., & Mirjalili, V. (2019). Python machine learning: Machine learning and deep learning with Python, scikit-learn, and TensorFlow 2. Packt Publishing Ltd.
[22]Liu, X. Y., Wu, J., & Zhou, Z. H. (2008). Exploratory undersampling for class-imbalance learning. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 39(2), 539-550.4
[23]Buda, M., Maki, A., & Mazurowski, M. A. (2018). A systematic study of the class imbalance problem in convolutional neural networks. Neural Networks, 106, 249-259.
[24]Zhou, Z. H., & Liu, X. Y. (2005). Training cost-sensitive neural networks with methods addressing the class imbalance problem. IEEE Transactions on knowledge and data engineering, 18(1), 63-77.
[25]Eitrich, T., & Lang, B. (2006). Efficient optimization of support vector machine learning parameters for unbalanced datasets. Journal of computational and applied mathematics, 196(2), 425-436.
[26]Wong, T. T. (2015). Performance evaluation of classification algorithms by k-fold and leave-one-out cross validation. Pattern Recognition, 48(9), 2839-2846.
[27]Marcot, B. G., & Hanea, A. M. (2021). What is an optimal value of k in k-fold cross-validation in discrete Bayesian network analysis?. Computational Statistics, 36(3), 2009-2031.
[28]Liu, M. K., Tseng, Y. H., & Tran, M. Q. (2019). Tool wear monitoring and prediction based on sound signal. The International Journal of Advanced Manufacturing Technology, 103(9), 3361-3373.
[29]Geramifard, O., Xu, J. X., Zhou, J. H., Li, X., & Gan, O. P. (2012, July). Feature selection for tool wear monitoring: A comparative study. In 2012 7th IEEE Conference on Industrial Electronics and Applications (ICIEA) (pp. 1230-1235). IEEE.
[30]Kong, D., Chen, Y., & Li, N. (2018). Gaussian process regression for tool wear prediction. Mechanical systems and signal processing, 104, 556-574.
[31]Shen, Y., Yang, F., Habibullah, M. S., Ahmed, J., Das, A. K., Zhou, Y., & Ho, C. L. (2021). Predicting tool wear size across multi-cutting conditions using advanced machine learning techniques. Journal of Intelligent Manufacturing, 32(6), 1753-1766.
[32]Caggiano, A. (2018). Tool wear prediction in Ti-6Al-4V machining through multiple sensor monitoring and PCA features pattern recognition. Sensors, 18(3), 823.
[33]ISO, I. (1989). 8688-2: 1989_Tool life testing in milling—Part 2: End milling. International Organization for Standardization. ISO.
[34]張竣閔（2021）。基於擴展派克向量模數與支持向量機之感應馬達故障診斷。國立臺灣科技大學機械工程系研究所碩士論文，台北市。
[35]Montgomery, D. C. (2017). Design and analysis of experiments. John wiley & sons.
[36]Akoglu, H. (2018). User's guide to correlation coefficients. Turkish journal of emergency medicine, 18(3), 91-93.
[37]劉益宏教授, ”人工智慧特論”上課講義, 國立台灣科技大學, 2022.
[38]O’brien, R. M. (2007). A caution regarding rules of thumb for variance inflation factors. Quality & quantity, 41(5), 673-690.
[39]Friedman, J., Hastie, T., & Tibshirani, R. (2010). Regularization paths for generalized linear models via coordinate descent. Journal of statistical software, 33(1), 1.
[40]Breiman, L. (2001). Random forests. Machine learning, 45(1), 5-32

全文公開日期 2027/08/09 (校內網路)
全文公開日期 2027/08/09 (校外網路)
全文公開日期 2027/08/09 (國家圖書館：臺灣博碩士論文系統)

簡易檢索 / 詳目顯示

相關論文