臺灣博碩士論文加值系統

誌謝
摘要 i
Abstract ii
目錄 iii
表目錄 v
圖目錄 vii
第1章 緒論 1
1.1 研究背景 1
1.2 研究目的 7
1.3 研究架構 9
第2章 理論方法 10
2.1 切削扭矩 10
2.2 切削物理模型(Cutting Force Model) 12
2.3 有效加工段之擷取方法 14
2.4 切削扭矩估測 16
2.4.1 刀具老化估測架構 23
第3章 系統開發 25
3.1 系統架構 25
3.1.1 系統概念 25
3.1.2 系統流程 28
3.2 資料蒐集 29
3.2.1 振動訊號(Vibration) 30
3.2.2 切削扭矩(Cutting Torque) 33
3.3 廠區層Factory Layer 35
3.2.1 IME各模組功能介紹 37
3.3.1 理論切削扭矩(Cutting Torque) 41
3.4 網路層Networking Layer 48
3.5 特徵處理 49
3.5.1 頻域特徵(Frequency Domain Feature) 49
3.5.2 自動編碼器模型建立(Autoencoder) 50
3.5.3 特徵挑選 52
第4章 案例研究與實驗分析 54
4.1 虛實協同加工系統 54
4.2 花鼓加工有效加工段自動擷取及品質估測 56
4.2.1 T1徑向車削 56
4.2.2 T2 內徑車削 59
4.2.3 T3 切槽車削 63
4.2.4 T7 銑削 66
4.2.5 有效加工段自動擷取結果比較 69
4.2.6 花鼓表面粗糙度(Ra)估測 71
4.3 不同加工條件下之切削扭矩估測 81
4.3.1銑削製程 81
4.3.2結果比較 100
第5章 結論與未來展望 101
5.1 結論 101
5.2 未來展望 102
參考文獻 103

[1]B. Kaya, C. Oysu and H. M. Ertunc, “Force-torque based on-line tool wear estimation system for CNC milling of Inconel 718 using neural networks,” Advances in Engineering Software, vol. 42, pp. 7684, 2011/3.
[2]K. Li., C Qiu, X Zhou, M Chen, M Chen, Y C Lin, X Jia, and B Li, “Modeling and tagging of time sequence signals in the milling process based on an improved hidden semi-Markov model,” Expert Systems with Applications, vol. 205, p. 117758, 2022/1.
[3]N. Ghosh et al., "Estimation of tool wear during CNC milling using neural network-based sensor fusion," Mechanical Systems and Signal Processing, vol. 21, no. 1, pp. 466-479, 2007/01/01/ 2007.
[4]葉恒志，自動判別有效加工段之扭矩估測系統-以2軸半CNC為例，國立高雄科技大學電機工程系智慧自動化系統碩士論文，2020。
[5]蕭紋生，可因應侷限樣本之切削品質估測方法，國立高雄科技大學電機工程系智慧自動化系統碩士論文，2020。
[6]洪諺傑，刀具可加工壽命之扭矩估測堆疊方法，國立高雄科技大學電機工程系智慧自動化系統碩士論文，2021。
[7]G. Wang, Y. Yang, and Z. Guo, "Hybrid learning based Gaussian ARTMAP network for tool condition monitoring using selected force harmonic features," Sensors and Actuators A: Physical, vol. 203, pp. 394-404, 2013/12/01/ 2013.
[8]T. Misaka et al., "Prediction of surface roughness in CNC turning by model-assisted response surface method," Precision Engineering, vol. 62, pp. 196-203, 2020/03/01/ 2020
[9]Y. Haghshenas and S. Amini, “A physically-supported data-driven proxy modeling based on machine learning classification methods: Application to water front movement prediction,” Journal of Petroleum Science and Engineering, vol. 196, p. 107828, 2021/1.
[10]A. Behjat, C. Zeng and R. Rai, “A physics-aware learning architecture with input transfer networks for predictive modeling,” Applied Soft Computing, vol. 96, p. 107828, 2020/11.
[11]Y. Yu and H. Yao, “Structural dynamics simulation using a novel physics-guided machine learning method,” Engineering Applications of Artificial Intelligence, vol. 96, p. 103947, 2020/11.
[12]F. J. Montáns and F. Chinesta, “Data-driven modeling and learning in science and engineering,” Comptes Rendus Mécanique, vol. 347, pp. 162177, 2019/11.
[13]T. G. Ritto and F. A. Rochinha, “Digital twin, physics-based model, and machine learning applied to damage detection in structures,” Mechanical Systems and Signal Processing, vol. 155, p. 107614, 2021/1.
[14]D. Aslan and Y. Altintas, "Prediction of Cutting Forces in Five-Axis Milling Using Feed Drive Current Measurements," IEEE/ASME Transactions on Mechatronics, vol. 23, no. 2, pp. 833-844, 2018.
[15]R.-T. René de Jesús, H.-R. Gilberto, T.-V. Iván, and J.-C. Juan Carlos, "Driver current analysis for sensorless tool breakage monitoring of CNC milling machines," International Journal of Machine Tools and Manufacture, vol. 43, no. 15, pp. 1529-1534, 2003/12/01/ 2003.
[16]D. Kim and D. Jeon, "Fuzzy-logic control of cutting forces in CNC milling processes using motor currents as indirect force sensors," Precision Engineering, vol. 35, no. 1, pp. 143-152, 2011/01/01/ 2011.
[17]D. R. Salgado and F. J. Alonso, "An approach based on current and sound signals for in-process tool wear monitoring," International Journal of Machine Tools and Manufacture, vol. 47, no. 14, pp. 2140-2152, 2007/11/01/ 2007.
[18]李琨旭，“開發具智慧檢測刀具狀態之無線刀把”，國立高雄科技大學電機工程系智慧自動化系統碩士班碩士論文，2019。
[19]軸的強度與應力，機械力學II，SlidePlayer，https://slidesplayer.com/slide/15980385/
[20]梅可人，以創新延伸式八元樹法應用於數控加工模擬系統之碰撞檢測與幾何移除，國立成功大學機械工程學系博士班，2015。
[21]G. E. Hinton and R. R. Salakhutdinov, "Reducing the Dimensionality of Data with Neural Networks," Science (New York, N.Y.), vol. 313, pp. 504-7, 08/01 2006.
[22]D. Kingma and M. Welling, Auto-Encoding Variational Bayes. 2014.
[23]AutoEncoder (一)-認識與理解，Leyan Bin Veon，https://medium.com/%E7%A8%8B%E5%BC%8F%E5%B7%A5%E4%BD%9C%E7%B4%A1/autoencoder-%E4%B8%80-%E8%AA%8D%E8%AD%98%E8%88%87%E7%90%86%E8%A7%A3-725854ab25e8
[24]LSTM為何如此有效?五個你一定要知道的祕密https://www.leiphone.com/category/academic/Q6KIrIbPSeuLntAT.html
[25][ML09]Activation Function是什麼？，Tim Wong，https://medium.com/%E6%B7%B1%E6%80%9D%E5%BF%83%E6%80%9D/ml08-activation-function-%E6%98%AF%E4%BB%80%E9%BA%BC-15ec78fa1ce4
[26]7 Types of Neural Network Activation Functions: How to Choose?, missinglink.ai, https://missinglink.ai/guides/neural-network-concepts/7-types-neural-network-activation-functions-right/
[27]機器學習馬拉松 076 優化器 Optimizers，Chenyu Tsai，https://medium.com/uxai/%E6%A9%9F%E5%99%A8%E5%AD%B8%E7%BF%92%E9%A6%AC%E6%8B%89%E6%9D%BE-076-%E5%84%AA%E5%8C%96%E5%99%A8-optimizers-19b8d40e24c5
[28]機器/深度學習: 基礎介紹-損失函數(loss function)，Tommy Huang，https://medium.com/@chih.sheng.huang821/%E6%A9%9F%E5%99%A8-%E6%B7%B1%E5%BA%A6%E5%AD%B8%E7%BF%92-%E5%9F%BA%E7%A4%8E%E4%BB%8B%E7%B4%B9-%E6%90%8D%E5%A4%B1%E5%87%BD%E6%95%B8-loss-function-2dcac5ebb6cb
[29]What is 'fuzzy logic'? Are there computers that are inherently fuzzy and do not apply the usual binary logic?, https://www.scientificamerican.com/article/what-is-fuzzy-logic-are-t/
[30]eMathTeacher: Mamdani's Fuzzy Inference Method, MEMBERSHIP FUNCTIONS, http://www.dma.fi.upm.es/recursos/aplicaciones/logica_borrosa/web/fuzzy_inferencia/funpert_en.htm
[31]Fuzzy Logic - Membership Function, https://www.tutorialspoint.com/fuzzy_logic/fuzzy_logic_membership_function.htm
[32]Fuzzy Logic - Inference System, https://www.tutorialspoint.com/fuzzy_logic/fuzzy_logic_inference_system.htm
[33]Tongtai, TMV-720A, https://www.tongtai.com.tw/tw/product-detail.php?id=174
[34]CHIAH CHYUN, CT2-76YM, https://www.chiah-chyun.com/tw/product/cnc-milling-turning-center
[35]工業技術研究院，振動感測模組，https://www.itri.org.tw/ListStyle.aspx?DisplayStyle=01_content&SiteID=1&MmmID=1036233376235770452&MGID=1037404346132667223
[36]Dytran, MODEL 3293A, TRIAXIAL ACCELEROMETER, https://www.dytran.com/Model-3293A-Triaxial-Accelerometer-P2434/