基於 LSTM 特徵提取器搭配域對抗遷移學習 最佳化碳化矽之線切割加工參數

以作者查詢圖書館館藏

、以作者查詢臺灣博碩士

、以作者查詢全國書目

、勘誤回報

、線上人數：26

、訪客IP：3.149.255.196

姓名

孫永哲(Yung-Jhe Sun) 查詢紙本館藏

畢業系所

機械工程學系

論文名稱

基於 LSTM 特徵提取器搭配域對抗遷移學習最佳化碳化矽之線切割加工參數
(Optimization for WEDM process parameters of SiC by domain-adversarial training with LSTM as the feature extractor)

相關論文

★ 雙光子光致聚合微製造系統之研發	★ 雙光子光致聚合五軸微製造系統之雷射加工路徑生成研究
★ 椎弓根螺釘定位演算法及導引夾治具自動化設計流程開發	★ 雙光子聚合微製造技術以能量均勻橢圓體為基之曝光時間最佳化研究
★ 雙光子光致聚合微製造以弦高誤差為基之切層演算法	★ 雙光子光致聚合微製造技術以螺旋線雷射掃描路徑增強微結構強度研究
★ 雙光子聚合微製造技術之三維結構製造品質改進研究	★ 利用二維多重圖像建構三維三角網格模型的生成與品質改進
★ 組織工程用冷凍成型製造系統之自動化製作流程開發	★ 自動相機校正與二維影像輪廓萃取研究
★ 基於雙光子光致聚合技術之四軸微製造系統製作高深寬比結構之研究	★ 冷凍成型積層製造之機台設計與組織工程支架製作參數調校研究
★ 基於二維影像輪廓重建三維模型技術之多視角相機群組空間座標系統整合	★ 應用於大型物體三維模型重建之多重二維校正板相機校正流程開發
★ 組織工程用冷凍成型積層製造之固態水支撐結構生成研究	★ 聚醚醚酮之積層製造系統開發

檔案

[Endnote RIS 格式]

[Bibtex 格式]

[相關文章]

[文章引用]

[完整記錄]

[館藏目錄]

至系統瀏覽論文 (2029-2-1以後開放)

摘要(中)

近年來第三代半導體飛速發展，以其耐高溫、高功率的特性在電動車、電源供應器等領域大放異彩，能夠在極端的環境下仍持續運作，且能簡化電子產品的電路設計與散熱系統等。但第三代半導體的代表材料之一的碳化矽價格較高昂，由於碳化矽材料需要嚴苛環境進行長晶，且碳化矽的長晶速度緩慢，需要經過大量加工程序才能投入使用，造成碳化矽晶圓生產不易。為了降低碳化矽材料成本，必須優化晶圓製造技術。線切割放電加工是一種成熟且高精度的加工技術，擁有多種可控參數可以針對目標導體材料進行調整，透過更改部分參數可以改良工件的加工品質，且在加工過程也可以即時監測間隙電壓等動態參數。為了最佳化線切割的加工參數並分析參數配置對碳化矽加工品質之影響，本研究透過深度學習的方式改良加工過程，模型使用長短期記憶神經網路(Long Short Term Memory, LSTM)進行預測，LSTM 模型擅長學習時間序列等資料並分析時間序列的因果關係特徵，蒐集線切割加工過程中的參數變化時序作為輸入，模型能夠有效分析線切割的動態參數變化對加工品質的影響。最後面對碳化矽樣本數量較少、蒐集不易的問題，本研究使用遷移學習方法搭配模型共同訓練。遷移學習方法能夠降低模型訓練所需的碳化矽學習樣本數，透過大量相似的矽晶圓樣本配合碳化矽樣本進
行訓練，可以改善模型的泛化程度、解決過擬合等問題並充實模型的訓練集。本研究的目的為建立線切割品質預測模型，利用儀器蒐集加工過程的時序資料，分析各加工參數對碳化矽晶圓之表面粗糙度與切口寬度的影響，以 LSTM 模型配合三種遷移學習方法建立模型，輸出樣本的表面粗糙度與切口寬度。本實驗的輸入為兩種動態參數時序資料與三種固定參數組合而成，固定參數共擁有 27 種參數配置，以矽晶圓作為源域資料集共有 270 個樣本，碳化矽作為目標資料集共有 270 個樣本，訓練集與驗證集比例為 6：4。驗證集之預測結果，表面粗糙度與切口寬度之 R-square 值分別為 95 與 82左右由此可見本模型能夠有效預測碳化矽之加工品質，減少加工過程與深度學習上的材料成本。

摘要(英)

In recent years, the third generation of semiconductors has developed rapidly. With its high temperature resistance and high power characteristics, it has good effect in the fields of electric vehicles and power supplies and it can operate in extreme environments ,simplify the circuit design ,design of electronic products and Cooling system, etc. However, SiC, one of the representative materials of the third generation semiconductor, is relatively expensive. Since SiC material requires a harsh environment for crystal growth, and the crystal growth rate of SiC is slow, it requires several number of processing procedures before it can be put into use, resulting in SiC Wafer production is difficult. In order to reduce silicon carbide material costs, wafer manufacturing technology must be optimized.Wire Electrical Discharge Machining is a mature and high-precision processing technology. It has a variety of controllable parameters that can be adjusted according to the target conductor material. By changing some parameters, the processing quality of the workpiece can be improved, and the gap can also be monitored in real time during the processing process. dynamic parameters such as voltage. In order to optimize the processing parameters of WEDM and analyze the impact of parameter configuration on the processing quality of SiC, this study improves the processing process through deep learning. The model uses The Long Short Term Memory (LSTM) neural network for prediction. The LSTM model is good at learning time series and analyzing the causal relationship characteristics of the time series. It collects the parameter change time series during the wire cutting process as input. The model can effectively analyze the impact of dynamic parameter changes of the wire cutting on the processing quality. Finally, facing the problem that SiC samples has few samples and difficult to collect, this study uses model training with the transfer learning method. The transfer learning method can reduce the number of SiC learning samples required for model training. Training with SiC samples and a large number of similar silicon wafer samples can improve the generalization of the model, solve
problems such as overfitting, and enrich the model training set.
The purpose of this study is to establish a WEDM quality prediction model, use instruments to collect time-series data of the cutting process, analyze the impact of each processing parameter on the surface roughness and kerf width of silicon carbide wafers and establish LSTM model with two transfer learning methods. The model outputs the surface roughness and kerf width of the sample. The input of this experiment is a combination of two dynamic parameter time-series data and three static parameters. The static parameters have a total of 27 parameter configurations. There are 270 samples of silicon wafer as the source domain data set and 270 samples of SiC as the target data set and the ratio of training set to validation set is 6:4. According to the prediction results of the validation set, the R-square values of surface roughness and kerf width are about 95 and 82 respectively. It can be seen that this model can effectively predict the processing quality of silicon carbide and reduce processing Material costs on process and deep learning.

關鍵字(中)

★ 碳化矽
★ 線切割
★ 放電加工
★ 深度學習
★ 遷移學習

關鍵字(英)

★ Silicon Carbide
★ WEDM
★ EDM
★ Deep Learning
★ Transfer Learning

論文目次

摘要 I
Abstract II
目錄 IV
圖目錄 V
表目錄 VIII
第一章諸論 1
1-1 前言 1
1-2 文獻回顧 3
1-3 研究動機與目的 10
1-4 論文架構 11
第二章理論說明 12
2-1 放電加工介紹 12
2-2 材料之選擇 14
2-3 影像處理方法介紹 15
2-4 人工神經網路 17
2-5 深度學習 22
2-6 遷移學習 29
第三章研究方法 33
3-1 實驗流程簡介 33
3-2 實驗資料之蒐集與前處理 34
3-3 實驗設備與流程 38
3-4 切口平面之切口寬度與粗糙度量測 42
3-5 深度學習模型之架構 44
3-6 遷移學習方法之分析 47
第四章結果與討論 50
4-1 線放電加工品質數據之可信度驗證 50
4-2 LSTM搭配遷移學習預測線切割品質之結果與討論 54
4-3 遷移學習方法成效評估 62
第五章結論與未來展望 65
5-1 總結 65
5-2 未來展望 65
參考文獻 67

參考文獻

[1] B. Bojorquez, R. T. Marloth, and O. S. Es-Said, “formation of A Crater in the Workpiece on an Electrical Discharge Machine”, ENGINEERING FAILURE ANALYSIS, vol. 9 pp. 93-97,2002.
[2] K. H. Ho, S. T. Newman, S. Rahimifard, and R. D. Allen, “State of the Art in Wire Electrical Discharge Machining (WEDM)”, INTERNATIONAL JOURNAL OF MACHINE TOOLS & MANUFACTURE, vol. 44, pp. 1247-1259,2004.
[3] J. Schmidhuber, “Deep Learning in Neural Networks: an Overview”, NEURAL NETWORKS, vol. 61, pp. 85-117,2015.
[4] S. J. Pan, and Q. A. Yang, “A Survey on Transfer Learning”, IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, vol. 22,pp. 1345-1359, 2010.
[5] H. Morkoc, S. Strite, G. B. Gao, M. E. Lin, B. Sverdlov, and M. Burns, “Large-Band-Gap SiC, III-V Nitride, and II-VI Znse-Based Semiconductor-Device Technologies”, JOURNAL OF APPLIED PHYSICS, vol. 76, pp. 1363-1398,1994.
[6] X. She, A. Q. Huang, O. Lucia, and B. Ozpineci, “Review of Silicon Carbide Power Devices and their Applications”, IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, vol. 64, pp. 8193-8205, 2017.
[7] T. Ozben, E. Kilickap, and O. Cakir, “investigation of Mechanical and Machinability Properties of SiC Particle Reinforced Al-MMC”, JOURNAL OF MATERIALS PROCESSING TECHNOLOGY, vol. 198, pp. 220-225,2008.
[8] H. Huang, Y. X. Zhang, and X. P. Xu, “Experimental investigation on the Machining Characteristics of Single-Crystal SiC Sawing with the Fixed Diamond Wire”, INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY, vol. 81, pp. 955-965, 2015.
[9] Sharma S, “Optimization of Machining Process Parameters for Surface Roughness of Al-Composites“, JOURNAL OF THE INSTITUTION OF ENGINEERS, vol. 94, pp327-333, 2013.
[10] R. K. Bhushan, “Multiresponse Optimization of Al Alloy-SiC Composite Machining Parameters for Minimum Tool Wear and Maximum Metal Removal Rate”, JOURNAL OF MANUFACTURING SCIENCE AND ENGINEERING-TRANSACTIONS OF THE ASME, vol. 135 , 2013.
[11] Z. L. Wang, X. S. Geng, G. X. Chi, and Y. K. Wang, “Surface integrity Associated with SiC/Al Particulate Composite by Micro-Wire Electrical Discharge Machining”, MATERIALS AND MANUFACTURING PROCESSES, vol. 29, pp. 532-539, 2014.
[12] K. Ishfaq, S. anwar, M. A. Ali, M. H. Raza, M. U. Farooq, S. Ahmad, C. I. Pruncu, M. Saleh, and B. Salah, “Optimization of WEDM for Precise Machining of Novel Developed Al6061-7.5% SiC Squeeze-Casted Composite”, INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY, vol. 111, pp. 2031-2049,2020.
[13] M. Ulas, O. Aydur, T. Gurgenc, and C. Ozel, “Surface Roughness Prediction of Machined Aluminum Alloy with Wire Electrical Discharge Machining by Different Machine Learning Algorithms”, JOURNAL OF MATERIALS RESEARCH AND TECHNOLOGY-JMR&T, vol. 9, pp. 12512-12524,2020.
[14] J. R. Jiang, and C. T. Yen, “Product Quality Prediction for Wire Electrical Discharge Machining with Markov Transition Fields and Convolutional Long Short-Term Memory Neural Networks”, APPLIED SCIENCES-BASEL, vol. 11, 5922,2021.
[15] K. H. Ho, and S. T. Newman, “State of the Art Electrical Discharge Machining (EDM)”, INTERNATIONAL JOURNAL OF MACHINE TOOLS & MANUFACTURE, vol. 43, pp. 1287-1300, 2003.
[16] S. Singh, S. Maheshwari, and P. C. Pandey, “Some Investigations into the Electric Discharge Machining of Hardened Tool Steel Using Different Electrode Materials”, JOURNAL OF MATERIALS PROCESSING TECHNOLOGY, vol. 149, pp. 272-27, 2004.
[17] J. Marafona, and J. A. G. Chousal, “A Finite Element Model of EDM Based on the Joule Effect”, INTERNATIONAL JOURNAL OF MACHINE TOOLS & MANUFACTURE, vol. 46, pp. 595-602, 2006.
[18] N. M. Abbas, D. G. Solomon, and M. F. Bahari, “A Review on Current Research Trends in Electrical Discharge Machining (EDM)”, INTERNATIONAL JOURNAL OF MACHINE TOOLS & MANUFACTURE, vol. 47, pp. 1214-1228,2007.
[19] Z. N. Guo, T. C. Lee, T. M. Yue, and W. S. Lau, “Study on the Machining Mechanism of WEDM with Ultrasonic Vibration of the Wire”, JOURNAL OF MATERIALS PROCESSING TECHNOLOGY, vol. 69, pp. 212-221, 1997.
[20] M. Kunieda, and C. Furudate, “High Precision Finish Cutting by Dry WEDM”, CIRP ANNALS-MANUFACTURING TECHNOLOGY, vol. 50, pp. 121-124, 2001.
[21] P. Pecas, and E. Henriques, “influence of Silicon Powder-Mixed Dielectric on Conventional Electrical Discharge Machining”, INTERNATIONAL JOURNAL OF MACHINE TOOLS & MANUFACTURE, vol. 43, pp. 1465-1471, 2003.
[22] OpenCV官方網站，取自http://opencv.org。
[23] 李立宗，「科班出身的AI人必修課Open CV影像處理使用Python」，2019。
[24] Y. Pinhasi, and D. Peri, “A Generalized analysis of Binary Half-Tone Representation of Images”, OPTICS COMMUNICATIONS, vol. 101, pp. 277-285,1993.
[25] S. Mallat, and S. Zhong, “Characterization of Signals from Multiscale Edges”, IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, vol. 14, pp. 710-732, 1992.
[26] G. Q. Zhang, B. E. Patuwo, and M. Y. Hu, “Forecasting with Artificial Neural Networks: the State of the Art”, INTERNATIONAL JOURNAL OF FORECASTING, vol. 14, pp. 35-62, 1998.
[27] Y. LeCun, Y. Bengio, and G. Hinton, “Deep Learning”, NATURE, vol. 521, no. 7553, pp. 436-444, 2015.
[28] V. Sze, Y. H. Chen, T. J. Yang, and J. S. Emer, “Efficient Processing of Deep Neural Networks: A Tutorial and Survey”, PROCEEDINGS OF THE IEEE, vol. 105, pp. 2295-2329, 2017.
[29] A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet Classification with Deep Convolutional Neural Networks”, COMMUNICATIONS OF THE ACM, vol. 60, pp. 84-90, 2017.
[30] S. Hochreiter, and J. Schmidhuber, “Long Short-Term Memory”, NEURAL COMPUTATION, vol. 9, pp. 1735-1780,1997.
[31] M. Schuster, and K. K. Paliwal, “Bidirectional Recurrent Neural Networks”, IEEE TRANSACTIONS ON SIGNAL PROCESSING, vol. 45, pp. 2673-2681, 1997.
[32] R. Caruana, “Multitask Learning”, MACHINE LEARNING, vol. 28, pp. 41-75,1997.
[33] Y. Ganin, E. Ustinova, H. Ajakan, P. Germain, H. Larochelle, F. Laviolette, M. Marchand, and V. Lempitsky, “Domain-Adversarial Training of Neural Networks”, JOURNAL OF MACHINE LEARNING RESEARCH, vol. 17, 2016.
[34] J. Lu, J. Li, Z. Yan, F. H. Mei, and C. S. Zhang, “Attribute-Based Synthetic Network (ABS-Net): Learning More from Pseudo Feature Representations”, PATTERN RECOGNITION, vol. 80, pp. 129-142, 2018.
[35] CHMER股份有限公司官網，取自https://www.chmer.com/tw/about/profile。
[36] OWON科技有限公司官網，取自https://www.owon.com.hk/index-1.asp。
[37] ZEISS官方網站，取自https://www.micro-shop.zeiss.com/en/de/shop/objectives/。
[38] 基恩士股份有限公司官網，取自https://www.keyence.com.tw/。
[39] Pytorch 官方網站，取自 https://pytorch.org/docs/stable/generated/torch.nn.LSTM.html。
[40] Google colab官方網站，取自https://colab.google/。

指導教授

廖昭仰(Chao-Yaug Liao)

審核日期

2024-1-19

推文