應用分治法於刀具壽命預測模型之研究

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姓名

王治全(Chih-Chuan Wang) 查詢紙本館藏

畢業系所

機械工程學系

論文名稱

應用分治法於刀具壽命預測模型之研究
(Application of Divide and Conquer Approach to Tool Life Prediction Model)

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至系統瀏覽論文 (2024-12-31以後開放)

摘要(中)

刀具殘餘壽命受到多種因素的影響，因此應用深度學習來分析資料集特徵並推論時常會面臨一定的困難。常見的做法是提高模型的學習能力，以便辨識複雜特徵趨勢。然而，本研究認為降低資料集的特徵複雜度也能夠實現同樣的目標。
本研究提出的方法是基於參考分治法，將複雜目標域拆分成數個簡單的局部域，再使用多個神經網路擬合局部域的特性，最後將其統整以預測目標域的表現。首先，應用像是隨機森林回歸之類的弱回歸器找出目標域資料集的重要特徵，用於降低其特徵維度。其次，利用聚類演算法將目標域資料集拆分為數個局部域資料集，藉此讓個別局部域的資料具有相近特徵。第三，利用適當的神經網路學習個別局部域的資料特徵，故個別的局部網路能推論對應之局部域表現。最終，提出一決策網路來統整數個局部網路，達成推論目標域表現之目的。
透過既有的資料集進行驗證，本研究提出的模型架構能夠以適當參數量對複雜的資料集快速學習與有效推論。此外，決策網路能夠解釋各局部網路對全域表現推論的貢獻狀況，顯示此方法的合理性。最後，探討局部網路之間遷移式學習。本研究發現以凍結少許推論層或微調等兩種方式能夠有效地降低個別局部網路的訓練時間，進而進一步地降低時間上的訓練成本。

摘要(英)

Tool residual life is affected by many causes, so it is quite difficult to apply deep learning to analyze dataset features and make inferences. A common solution is to increase the capability of the model in order to identify complicated feature trends. However, this study suggests that the same goal can be achieved by reducing the complexity of the dataset.
The method proposed in this study refers divide and conquer appeorach, in which the complex target domain is split into several simple local domains, and then multiple neural networks are used to fit the properties of the local domains and finally integrate them to predict the representation of the target domain. First, a weak regressor such as random forest regression, is applied to find the important features of the target domain dataset, which is used to reduce its dimension of features. Second, a clustering algorithm is used to split the target domain dataset into several local domain datasets, so that the data in individual local domains have similar and close features. Third, we use appropriate neural networks to learn the features of individual local domain, so that the decision local networks can infer the corresponding local domain performance. Finally, a network is proposed to integrate several local networks to achieve the purpose of inferring the performance of the target domain.
The model framework with appropriate quantity of parameters proposed in this study can learn quickly and infer effectively from complex datasets.In addition, the decision network is able to explain the contribution of each local network to the inference of the global performance, which shows the rationality of this approach. Finally, we investigate the transfer learning among local networks. It is found that freezing a few inference layers or fine-tuning can effectively reduce the training time of local networks, which further reduces the training cost in terms of time.

關鍵字(中)

★ 刀具狀態監測
★ 分治法
★ 深度學習
★ 遷移式學習
★ 聚類演算法

關鍵字(英)

★ Tool Condition Monitoring
★ Divide and Conquer Approach
★ Deep Learning
★ Transfer Learinging
★ Clustering Algorthm

論文目次

摘要 i
Abstract ii
致謝 iii
目錄 iv
圖目錄 vi
表目錄 viii
第一章緒論 1
1-1 研究背景 1
1-2 文獻探討 4
1-2-1 刀具壽命監測的技術發展 4
1-2-2 機器學習應用於刀具壽命監測研究回顧 5
1-2-3 深度學習的發展與回顧 6
1-2-4 深度學習應用於刀具壽命監測研究回顧 9
1-3 研究動機 12
1-4 論文架構 14
第二章相關理論 15
2-1 機器學習 15
2-1-1 機器學習概念 15
2-1-2 聚類演算法 16
2-1-3 深度學習 19
2-1-4 特徵金字塔網路 21
2-2 遷移式學習 22
2-2-1 機器學習在實際應用中的困境 22
2-2-2 遷移式學習的概念 23
2-2-3 遷移式學習的定義與實作 24
2-3 特徵萃取 25
2-3-1 特徵萃取的重要性 25
2-3-2 特徵萃取的方法 26
第三章研究方法與執行步驟 27
3-1 研究方法 27
3-2 執行步驟 28
3-3 成效評估 29
3-4 實作環境 31
第四章實驗設計 32
4-1 資料前處理 32
4-1-1 資料集選用 32
4-1-2 資料清理 32
4-1-3 特徵萃取方式 34
4-1-4 標籤方式 35
4-1-5 特徵重要度 35
4-1-6 資料集切割 35
4-2 聚類演算法 35
4-2-1 K-means 36
4-2-2 BIRCH 36
4-2-3 Spectral Clustering 36
4-2-4 MeanShift 36
4-3 局部網路設計 37
4-3-1 網路架構 37
4-3-2 訓練超參數 39
4-4 全域模型設計 39
4-4-1 決策網路 39
4-4-2 訓練超參數 41
4-5 內遷移式學習 42
4-5-1 檢驗可行性與目標 42
4-5-2 實作方式 42
第五章結果與討論 44
5-1 特徵重要度 44
5-2 聚類簇數 45
5-3 局部網路成效 48
5-4 全域模型成效 55
5-5 內遷移式學習 57
5-5-1 以高壽命群作為源領域 57
5-5-2 以中壽命群作為源領域 59
5-5-3 以低壽命群作為源領域 60
5-5-4 綜合討論 61
第六章結論與未來展望 65
6-1 結論與貢獻 65
6-2 應用與限制 66
6-3 建議及未來展望 66
參考文獻 67

參考文獻

[1] S. Y. Wong, J. H. Chuah, and H. J. Yap, "Technical data-driven tool condition monitoring challenges for CNC milling: a review," The International Journal of Advanced Manufacturing Technology, vol. 107, pp. 4837-4857, 2020.
[2] "WIKIPEDIA:Divide-and-conquer algorithm." https://en.wikipedia.org/wiki/Divide-and-conquer_algorithm (accessed 05/08, 2023).
[3] C. E. L. Thomas H. Cormen, Ronald L. Rivest, and Clifford Stein, Introduction to Algorithms. MIT, 2009.
[4] 张. 费成巍, "复杂工程结构系统逼近分析的分布式协同," 中华人民共和国, 2020.
[5] N. Mehta, P. Pandey, and G. Chakravarti, "An investigation of tool wear and the vibration spectrum in milling," Wear, vol. 91, no. 2, pp. 219-234, 1983.
[6] G. Terrazas, G. Martínez-Arellano, P. Benardos, and S. Ratchev, "Online tool wear classification during dry machining using real time cutting force measurements and a CNN approach," Journal of Manufacturing and Materials Processing, vol. 2, no. 4, p. 72, 2018.
[7] A. Kothuru, S. P. Nooka, and R. Liu, "Application of deep visualization in CNN-based tool condition monitoring for end milling," Procedia Manufacturing, vol. 34, pp. 995-1004, 2019.
[8] E. A. Battison, "A New Look at the" Whitney" Milling Machine," Technology and Culture, vol. 14, no. 4, pp. 592-598, 1973.
[9] "WIKIPEDIA:Milling (machining)." https://en.wikipedia.org/wiki/Milling_(machining)#History (accessed 05/08, 2023).
[10] S. Kalpakjian, 機械製造, 3 ed. 1998.
[11] N. Cook, "Prediction of tool life and optimal machining conditions," Wear, vol. 62, no. 1, pp. 223-231, 1980.
[12] S. J. Russell, & Norvig, P., Artificial Intelligence: A Modern Approach Upper Saddle River: NJ: Prentice Hall., 2010.
[13] E. Alpaydin, Introduction to Machine Learning Cambridge: MA: MIT Press., 2010.
[14] W. Yu, C. K. Mechefske, and I. Y. Kim, "Cutting tool wear estimation using a genetic algorithm based long short-term memory neural network," in International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, 2018, vol. 51852: American Society of Mechanical Engineers, p. V008T10A037.
[15] P.-M. Huang and C.-H. Lee, "Estimation of tool wear and surface roughness development using deep learning and sensors fusion," Sensors, vol. 21, no. 16, p. 5338, 2021.
[16] J. Karandikar, "Machine learning classification for tool life modeling using production shop-floor tool wear data," Procedia Manufacturing, vol. 34, pp. 446-454, 2019.
[17] Y.-C. Huang and H.-S. Liao, "Building prediction model for a machine tool with genetic algorithm optimization on a general regression neural network," Journal of Intelligent & Fuzzy Systems, vol. 38, no. 2, pp. 2347-2357, 2020.
[18] C. Sanjay, M. Neema, and C. Chin, "Modeling of tool wear in drilling by statistical analysis and artificial neural network," Journal of materials processing technology, vol. 170, no. 3, pp. 494-500, 2005.
[19] D. Wu, C. Jennings, J. Terpenny, R. X. Gao, and S. Kumara, "A comparative study on machine learning algorithms for smart manufacturing: tool wear prediction using random forests," Journal of Manufacturing Science and Engineering, vol. 139, no. 7, 2017.
[20] R. Zhao, R. Yan, J. Wang, and K. Mao, "Learning to monitor machine health with convolutional bi-directional LSTM networks," Sensors, vol. 17, no. 2, p. 273, 2017.
[21] A. P. Kulkarni, G. G. Joshi, A. Karekar, and V. G. Sargade, "Investigation on cutting temperature and cutting force in turning AISI 304 austenitic stainless steel using AlTiCrN coated carbide insert," International Journal of Machining and Machinability of Materials 2, vol. 15, no. 3-4, pp. 147-156, 2014.
[22] S. Cho, S. Binsaeid, and S. Asfour, "Design of multisensor fusion-based tool condition monitoring system in end milling," The International Journal of Advanced Manufacturing Technology, vol. 46, pp. 681-694, 2010.
[23] S. Shurrab, A. Almshnanah, and R. Duwairi, "Tool wear prediction in computer numerical control milling operations via machine learning," in 2021 12th International Conference on Information and Communication Systems (ICICS), 2021: IEEE, pp. 220-227.
[24] P. J. Werbos, "Backpropagation through time: what it does and how to do it," Proceedings of the IEEE, vol. 78, no. 10, pp. 1550-1560, 1990.
[25] Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, "Gradient-based learning applied to document recognition," Proceedings of the IEEE, vol. 86, no. 11, pp. 2278-2324, 1998.
[26] A. Krizhevsky, I. Sutskever, and G. E. Hinton, "Imagenet classification with deep convolutional neural networks," Communications of the ACM, vol. 60, no. 6, pp. 84-90, 2017.
[27] K. Simonyan and A. Zisserman, "Very deep convolutional networks for large-scale image recognition," arXiv preprint arXiv:1409.1556, 2014.
[28] C. Szegedy et al., "Going deeper with convolutions," in Proceedings of the IEEE conference on computer vision and pattern recognition, 2015, pp. 1-9.
[29] K. He, X. Zhang, S. Ren, and J. Sun, "Deep residual learning for image recognition," in Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, pp. 770-778.
[30] S. Hochreiter and J. Schmidhuber, "Long short-term memory," Neural computation, vol. 9, no. 8, pp. 1735-1780, 1997.
[31] dprogrammer. "RNN, LSTM & GRU." http://dprogrammer.org/rnn-lstm-gru (accessed 06/25, 2023).
[32] A. Gouarir, G. Martínez-Arellano, G. Terrazas, P. Benardos, and S. Ratchev, "In-process tool wear prediction system based on machine learning techniques and force analysis," Procedia CIRP, vol. 77, pp. 501-504, 2018.
[33] F. Aghazadeh, A. Tahan, and M. Thomas, "Tool condition monitoring using spectral subtraction and convolutional neural networks in milling process," The International Journal of Advanced Manufacturing Technology, vol. 98, pp. 3217-3227, 2018.
[34] B. Wang, Y. Lei, N. Li, and W. Wang, "Multiscale convolutional attention network for predicting remaining useful life of machinery," IEEE Transactions on Industrial Electronics, vol. 68, no. 8, pp. 7496-7504, 2020.
[35] Q. An, Z. Tao, X. Xu, M. El Mansori, and M. Chen, "A data-driven model for milling tool remaining useful life prediction with convolutional and stacked LSTM network," Measurement, vol. 154, p. 107461, 2020.
[36] A. Graves, N. Jaitly, and A.-r. Mohamed, "Hybrid speech recognition with deep bidirectional LSTM," in 2013 IEEE workshop on automatic speech recognition and understanding, 2013: IEEE, pp. 273-278.
[37] Y. LeCun, Y. Bengio, and G. Hinton, "Deep learning," nature, vol. 521, no. 7553, pp. 436-444, 2015.
[38] L. Breiman, "Bagging predictors," Machine learning, vol. 24, pp. 123-140, 1996.
[39] Chaya. "Random Forest Regression." https://levelup.gitconnected.com/random-forest-regression-209c0f354c84 (accessed 05/08, 2023).
[40] "WIKIPEDIA:Clustering Analysis." https://en.wikipedia.org/wiki/Cluster_analysis (accessed 05/31, 2023).
[41] "scikit-learn:Clustering." https://scikit-learn.org/stable/modules/clustering.html (accessed 06/01, 2023).
[42] MathWorks. "What Is Overfitting?" https://www.mathworks.com/discovery/overfitting.html (accessed 05/08, 2023).
[43] G. E. Hinton, N. Srivastava, A. Krizhevsky, I. Sutskever, and R. R. Salakhutdinov, "Improving neural networks by preventing co-adaptation of feature detectors," arXiv preprint arXiv:1207.0580, 2012.
[44] V. Nair and G. E. Hinton, "Rectified linear units improve restricted boltzmann machines," in Proceedings of the 27th international conference on machine learning (ICML-10), 2010, pp. 807-814.
[45] "WIKIPEDIA:Rectifier (neural networks)." https://en.wikipedia.org/wiki/Rectifier_(neural_networks) (accessed 07/18, 2023).
[46] T.-Y. Lin, P. Dollár, R. Girshick, K. He, B. Hariharan, and S. Belongie, "Feature pyramid networks for object detection," in Proceedings of the IEEE conference on computer vision and pattern recognition, 2017, pp. 2117-2125.
[47] S. J. Pan and Q. Yang, "A survey on transfer learning," IEEE Transactions on knowledge and data engineering, vol. 22, no. 10, pp. 1345-1359, 2010.
[48] R. E. Bellman, Adaptive Control Processes: A Guided Tour. Princeton University Press, 1961.
[49] R. BA. "PHM data challenge 2010." https://www.kaggle.com/datasets/rabahba/phm-data-challenge-2010 (accessed 05/22, 2023).

指導教授

林錦德(Chin-Te Lin)

審核日期

2023-7-27

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