以人工智慧模型預測雷射切割鋼板寬度

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：76

、訪客IP：18.225.92.95

姓名

陳國欽(I Putu Andhi Indira Kusuma) 查詢紙本館藏

畢業系所

機械工程學系

論文名稱

以人工智慧模型預測雷射切割鋼板寬度
(Artificial Intelligence-Based Model for Kerf Width Prediction in Laser Cutting of Electrical Steel Sheet Using Vibration Signals as Inputs)

相關論文

★ 四弦型非對稱光學讀取頭致動器模態共振分析與抑制	★ 網路連接儲存裝置熱分析與設計
★ 小型垂直軸風力發電機之有限元素分析	★ 平板受聲源作用之振動與輻射聲場分析
★ 壓電吸振器應用於平板的振動與噪音控制	★ 主動式吸振器應用於薄板減振與減噪
★ 離散振動系統之分析軟體製作	★ 有洞薄方板之動態分析與激振後之聲場
★ 撓性結構之主動振動控制	★ 速度與位移回饋式壓電吸振器之減振研究
★ 以LabVIEW為介面之模態測試軟體製作	★ 電壓回饋壓電吸振器對平板之振動控制
★ 可調式消音閥的分析與最佳設計	★ 旋轉樑的動態分析與壓電吸振器之減振設計
★ 自感式壓電吸振器之設計與應用於矩形板之減振	★ 多孔薄方板之振動與聲場分析

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]

[檢視]

[下載]

本電子論文使用權限為同意立即開放。
已達開放權限電子全文僅授權使用者為學術研究之目的，進行個人非營利性質之檢索、閱讀、列印。
請遵守中華民國著作權法之相關規定，切勿任意重製、散佈、改作、轉貼、播送，以免觸法。

摘要(中)

矽鋼是一種含矽的特種鋼，具有磁性，是電工領域廣泛使用的一種軟磁合金，故又名電工鋼，矽鋼板並以其良好的磁性能成為電機尤其是電機定轉子的優良鐵芯材料。鑑於製造業(尤其是電動車業)對矽鋼片的需求增長，亟需要矽鋼板切割工藝和精度有所增進。近期由於脈衝雷射切割之非接觸性、靈活性、小批量生產的可用性以及與各種裝配線直接集成的能力，已成為一種實用的矽鋼板加工方法。但是使用雷射切割矽鋼板的切口寬度、切口波紋度和熱影響區 (HAZ) 等產品品質主要取決於功率、脈衝重複率和切割速度的設定，且受雷射本身的性質和材料特性的影響，獲得最佳的切割參數非常具有挑戰性。由於工件的許多特性都可以在振動信號中表示，振動信號可以提供一種有用的替代方法來預測用雷射切割時的切口寬度。出於這些原因，本研究旨在研究利用從振動信號中提取的特徵，再結合人工智慧(AI)模型，以預測無取向矽鋼板脈衝雷射切割切口寬度。本論文的主要分析包括四個主要部分：首先，先討論三種不同類型的AI 的預測模型，即機器學習 (ML)、深度學習 (DL) 和集成學習 (EL)，主要目的是得到較高的預測正確定；其次，採用不同的輸入特徵策略進行探討，包含原始時域振動信號和從小波變換技術中提取的特徵；第三，分析最佳的基礎小波選擇和最佳超參數的策略，並採用雷射掃描儀和 X-Y 工作台等兩種激光切割動作機制，以評估從實驗模型和 EL 模型獲得的切口寬度域測結果。由結果中顯示，將提取振動特徵作為輸入與基於 AI 的預測模型相結合，可以預測切口寬度至一定之預測正確度。如使用原始時域振動信號和從小波變換中選擇的特徵作為輸入特徵，DL 模型的預測精度分別為平均百分比誤差 (MAPE) 6.00% 和 5.75%；通過使用從最佳基礎小波中選擇的特徵作為輸入，ML 模型的預測為MAPE 1.69% 的；若使用原始時域振動信號結合 EL 模型並考慮兩種切割運作的預測精度，XY 工作台切割機制的 MAPE 為 5.50%，激光掃描儀切割機制的 MAPE 為 6.98%。最後本論文之總結為：此研究可以用於脈衝雷射切割矽鋼板的切口寬度預測，並且為未來開發基於人工智能的實時預測模型奠定基礎。

摘要(英)

Non-oriented electrical steel sheet, emerging as an excellent core material for electrical machinery especially stators and rotors of electric motors due to its good magnetic properties. The need to improve electrical steel sheet cutting processes and accuracy while maintaining a flexible process and lower costs are unavoidable given the manufacturing industry′s explosive growth and demand for electric vehicles (EV). Due to its non-contact nature, flexibility, availability for small-batch production, and ability to be directly integrated with a variety of assembly lines, laser cutting has emerged as a promising method of processing electrical steel sheets. However, the product quality such as kerf width, kerf waviness, and heat-affected zone (HAZ) of cutting electrical steel sheets using laser cutting mainly depends on the optimal setup of laser power, pulse repetition rate, and cutting speed. Choosing the values for the optimal laser cutting parameters is very challenging since affected by the nature of the laser itself and the characteristics of the materials. As many characteristics of a workpiece can be represented in the vibration signals, which may provide a useful alternative judgment to predict kerf width when cutting with a pulsed laser. For these reasons, this research aimed to investigate the potential of employing extracted features from vibration signals combined with artificial intelligence (AI) based predictive models for kerf width prediction of pulsed laser cutting of non-oriented electrical steel sheets. The analysis consisted of four primary parts. Firstly, three different kinds of AI-based predictive models have been explored i.e. machine learning (ML), deep learning (DL), and ensemble learning (EL). Every predictive model has advantages and drawbacks, the main objective of exploring and comparing them is to manage and achieve the highest possible prediction accuracy. Secondly, two strategies from preprocessing the input features into the DL predictive models were considered i.e. raw time domain vibration signals and the extracted features from the wavelet transformation technic. Thirdly, the optimum base wavelet selection and strategies to select the optimal hyperparameters were explored in several notable ML models. Lastly, two laser cutting mechanisms such as the laser scanner and the X-Y table were investigated and compared to assess the kerf width quality obtained from the experimental and EL models. The results indicate that the choice of vibration-extracted features as the input to the AI-based predictive models can provide acceptable prediction accuracy for predicting the kerf width. The prediction accuracy for DL models by using raw time domain vibration signals and selected features from wavelet transformation as the input features yield 6.00% and 5.75% of mean average percentages error (MAPE), respectively. The prediction accuracy for ML models by using selected features from optimal base wavelet as the input features yields 1.69% of MAPE. Meanwhile, the prediction accuracy by using raw time domain vibration signals combined with EL models and considering two types of laser cutting movements yields 5.50% MAPE for the XY-table cutting mechanism and 6.98% MAPE for the laser scanner cutting mechanism. In general, this study lays the groundwork for future research into developing a real-time AI-based predictive model for kerf width prediction in pulsed laser cutting of non-oriented electrical steel sheets.

關鍵字(中)

關鍵字(英)

★ Non-Oriented Silicon Steel Sheet
★ Artificial Intelligence
★ Kerf Width
★ Pulsed Laser Cutting
★ Vibration Signals
★ Wavelet Transform

論文目次

ABSTRACT III
ACKNOWLEDGEMENT V
TABLE OF CONTENTS VI
LIST OF TABLES IX
LIST OF FIGURES XI
LIST OF ABBREVIATIONS XIV
CHAPTER I. INTRODUCTION 1
1.1 Research trend on laser cutting of silicon steel sheet 1
1.2 Research trends on the application of vibration-extracted features as the input for predictive models 2
1.3 Research Objectives 3
1.4 Constitution of the dissertation 5
CHAPTER II. THEORIES 7
2.1 Artificial intelligence for manufacturing 7
2.1.1 Deep Neural Network (DNN) 10
2.1.2 Support Vector Regression (SVR) 12
2.1.3 Extreme Learning Machine (ELM) 13
2.1.4 Ensemble Learning 15
2.2 Input Variables Type for Deep and Machine Learning 16
2.3 Vibration Signals Features Extraction 18
2.3.1 Raw Vibration Signals 18
2.3.2 Wavelet Transform 18
2.4 Feature Selection and Correlation 20
2.5 Hyperparameter Selection and Performance Indicators 22
CHAPTER III. EXPERIMENTAL PROCEDURES 25
3.1 First Configuration of Pulsed Laser Cutting System 25
3.2 Second Configuration of Pulsed Laser Cutting System 26
3.3 Kerf Width Measurements Using Machine Vision System 27
CHAPTER IV. RESULTS AND DISCUSSION 29
4.1 Product Quality Prediction Using Deep Learning 29
4.1.1 Measurements Process and Results 29
4.1.2 DNN Model Structure and Data Division Strategy 32
4.1.3 Raw Vibration Signal Input Features 33
4.1.4 Wavelet Decomposition Input Features 38
4.1.5 Conclusion of Chapter 4.1 45
4.2 Product Quality Prediction Using Machine Learning 47
4.2.1 Measurements Process and Results 47
4.2.2 Base Wavelet Selection 48
4.2.3 Feature Extraction and Selection 51
4.2.4 Data Division Strategy 53
4.2.5 Prediction performance using training data 54
4.2.6 Prediction performance using testing data 56
4.2.7 Conclusion of Chapter 4.2 59
4.3 Product Quality Prediction Using Ensemble Learning 61
4.3.1 Measurements Process and Results 61
4.3.2 Data Division Strategy 64
4.3.3 Proposes Ensemble Learning 65
4.3.4 Prediction Accuracy Comparison 66
4.3.5 Conclusion of Chapter 4.3 71
CHAPTER V. CONCLUSIONS AND FUTURE PERSPECTIVES 72
5.1 Conclusions 72
5.2 Future Perspectives 73
REFERENCES 74

參考文獻

1. Laser systems Europe, High precision laser cutting of electrical steel. https://www.lasersystemseurope.com/press-releases/high-precision-laser-cutting-electrical-steel, accessed on 17 February, 2022.
2. Emura M, Landgraf FJG, Ross W, Barreta JR (2003) The influence of cutting technique on the magnetic properties of electrical steels. J Magn Magn, 254-255:358-360.
https://doi.org/10.1016/S0304-8853(02)00856-9
3. Pandey AK, Dubey AK (2013) Modeling and optimization of kerf taper and surface roughness in laser cutting of titanium alloy sheet. J Mech Sci Technol, 27(7):2115–2124. https://doi.org/10.1007/s12206-013-0527-7
4. Tsai MJ, Li CH, Chen CC (2008) Optimal laser-cutting parameters for QFN packages by utilizing artificial neural networks and genetic algorithm. J Mater Process Technol, 208:270-283. https://doi.org/10.1016/j.jmatprotec.2007.12.138
5. Ghany KA, Newishy M (2005) Cutting of 1.2 mm thick austenitic stainless steel sheet using pulsed and CW Nd: YAG laser. J Mater Process Technol, 168:438-447.
https://doi.org/10.1016/j.jmatprotec.2005.02.251
6. Nguyen DT, Ho JR, Tung PC, Lin CK (2021) An improved real-time temperature control for pulsed laser cutting of non-oriented electrical steel. Opt Laser Technol, 136:1-12. https://doi.org/10.3390/math9182261
7. Rohman MN, Ho JR, Tung PC, Lin CK (2022) Prediction and optimization of geometrical quality for pulsed laser cutting of non-oriented electrical steel sheet. Opt Laser Technol, 149:107847. https://doi.org/10.1016/j.optlastec.2022.107847.
8. Nguyen TH, Lin CK, Tung PC, Nguyen-Van C, Ho JR (2021) Artificial intelligence-based modeling and optimization of heat-affected zone and magnetic property in pulsed laser cutting of thin nonoriented silicon steel. Int J Adv Manuf Technol, 113:3225-3240. https://doi.org/10.1007/s00170-021-06847-4
9. Nguyen DT, Ho JR, Tung PC, Lin CK (2021) Prediction of kerf width in laser cutting of thin non-oriented electrical steel sheets using convolutional neural network. Mathematics, 9(18), 2261. https://doi.org/10.3390/math9182261
10. Masoudi S, Mirabdolahi M, Dayyani M, Jafarian F, Vafadar A, Dorali MR (2018) Development of an Intelligent Model to Optimize Heat-affected Zone, Kerf, and Roughness in 309 Stainless Steel Plasma Cutting by Using Experimental Results. Mater. Manuf. Process, 34(3), 345–356. https://doi.org/10.1080/10426914.2018.1532579
11. Fang N and Pai PS (2018) A new computational intelligence approach to predicting the machined surface roughness in metal machining. Int J of Mach Learn Comput, (8), 524-529.
12. Yongbin Y, Bagherzade SA, Azimy H, Akbari M, Karimipour A (2020) Comparison of the artificial neural network model prediction and the experimental results for cutting region temperature and surface roughness in laser cutting of AL6061T6 alloy. Infrared Phys Technol, 108, 1-8. https://doi.org/10.1016/j.infrared.2020.103364
13. Lin YC, Wu KD, Shih WC, Hsu PK, Hung JP (2020) Prediction of surface roughness based on cutting parameters and machining vibration in end milling using regression method and artificial neural network. App Sci, 10(11), 3941. https://doi.org/10.3390/app10113941
14. Wu TY, Lei KW (2019) Prediction of surface roughness in milling process using vibration signal analysis and artificial neural network. Int J Adv Manuf Technol, 102:305-314. https://doi.org/10.1007/s00170-018-3176-2
15. Upadhyay V, Jain PK, Mehta NK (2013) In-process prediction of surface roughness in turning of Ti-6Al-4V alloy using cutting parameters and vibration signals. Measurement, 46:154-160. https://doi.org/10.1016/j.measurement.2012.06.002
16. Salgado DR, Alonso FJ, Cambero I, Marcelo A (2009) In-process surface roughness prediction system using cutting vibrations in turning. Int J Adv Manuf Technol, 43, 40–51. https://doi.org/10.1007/s00170-008-1698-8
17. Sahu NK, Andhare AB, Andhale S, Abraham RR (2018) Prediction of surface roughness in turning of Ti-6Al-4V using cutting parameters, forces and tool vibration. IOP Conf. Ser.: Mater. Sci. Eng, 346.https://doi.org/10.1088/1757899X/346/1/012037.
18. Lin WJ, Lo SH, Young HT, Hung, CL (2019). Evaluation of deep learning neural networks for surface roughness prediction using vibration signal analysis. Applied Sciences, 9(7), 1462. https://doi.org/10.3390/app9071462
19. Jurkovic Z, Cukor G, Brezocnik M, Brajkovic T (2016) A comparison of machine learning methods for cutting parameters prediction in high speed turning process, J Intell Manuf, 29: 1683–1693. https://doi.org/10.1007/s10845-016-1206-1
20. Cho S, Binsaeid S, Asfour S (2010) Design of multisensor fusion-based tool condition monitoring system in end milling. Int J Adv Manuf Technol, 46, 681–694. https://doi.org/10.1007/s00170-009-2110-z.
21. Binsaeid S, Asfour S, Cho S, Onar A (2009) Machine ensemble approach for simultaneous detection of transient and gradual abnormalities in end milling using multisensor fusion. J. Mater Process Technol, 209(10), 4728–4738.
https://doi.org/10.1016/j.jmatprotec.2008.11.038
22. Pimenov DY, Bustillo A, Mikolajczyk T (2018) Artificial intelligence for automatic prediction of required surface roughness by monitoring wear on face mill teeth, J Intell Manuf, 29, 1045–1061. https://doi.org/10.1007/s10845-017-1381-8
23. Bustillo A, Díez-Pastor JF, Quintana G. et al. (2011) Avoiding neural network fine tuning by using ensemble learning: application to ball-end milling operations. Int J Adv Manuf Technol, 57, 521-532. https://doi.org/10.1007/s00170-011-3300-z
24. Patra K, Pal SK, Bhattacharyya K (2007) Application of wavelet packet analysis in drill wear monitoring. Mach. Sci. Technol, 11(3), 413-432.
25. Liu S, Hu Y, Li C, Lu H et al. (2017) Machinery condition based on wavelet and support vector machine. J Intell Manuf, 28, 1045-1055.https://doi.org/10.1007/s10845-015-1045-5
26. Xu S, Wang Y, Zhao J, Le G (2008) A study of fault monitoring in CNC machining of free-form surfaces based on NN-wavelet analysis-technical communication. Mach. Sci. Technol, 12(3), 405-416. https://doi.org/10.1080/10910340802306892
27. Goyal D, Choudhary A, Pabla BS, Dhami SS (2020) Support vector machines based non-contact fault diagnosis system for bearings. J Intell Manuf, 31, 1275-1289. https://doi.org/10.1007/s10845-019-01511-x
28. Kulkarni PG, Sahasrabudhe AD (2017) Investigations on mother wavelet selection for health assessment of lathe bearings. Int J Adv Manuf Technol, 90, 3317-3331. https://doi.org/10.1007/s00170-016-9664-3
29. Li DZ, Zheng X, Xie QW, Jin QB (2018) A sequential feature extraction method based on discrete wavelet transform, phase space reconstruction, and singular value decomposition and an improved extreme learning machine for rolling bearing fault diagnosis. Proc. Inst. Mech. Eng. E: J. Process Mech. Eng, 232(6), 635–649.
https://doi.org/10.1177/0954408917733130
30. Rodrigues AP, D’Mello G, Pai PS (2016) Selection of mother wavelet for wavelet analysis of vibration signals in machining. J Mech Eng Autom, 6(5A):81-85.
31. Ruqiang Y, Robert XG (2009) Base wavelet selection for bearing vibration signal analysis. Int J of Wavelets Multiresolution Inf Process, 7(4):411-426.
https://doi.org/10.1142/S0219691309002994
32. Rai H, Tiwari MZ, Ivanov D, Dolgui A (2021) Machine learning in manufacturing and industry 4.0 applications, Int J Prod Res, 59(16):4773-4778.
https://doi.org/10.1080/00207543.2021.1956675
33. Choudhary AK, Harding JA, Tiwari MK (2009) Data mining in manufacturing: a review based on the kind of knowledge, J Intell Manuf, 20, 501–521.
https://doi.org/10.1007/s10845-008-0145-x
34. Khan P, et al (2021) Machine Learning and Deep Learning Approaches for Brain Disease Diagnosis: Principles and Recent Advances, IEEE Access, 9:37622-37655. https://doi.org/10.1109/ACCESS.2021.3062484
35. Wang J, Ma Y, Zhang L, Gao RX, Wu D (2018) Deep learning for smart manufacturing: Methods and applications, J. Manuf. Syst, 48 Part C:144-156.
https://doi.org/10.1016/j.jmsy.2018.01.003
36. Janiesch C, Zschech P, Heinrich K (2021) Machine learning and deep learning, Electron. Markets, 31:685–695. https://doi.org/10.1007/s12525-021-00475-2
37. Savita KS, Ayesha S (2019) Deep Learning Algorithms and Applications in Computer Vision, Int. J. Comput. Sci, 7(7):195-201. https://doi.org/10.26438/ijcse/v7i7.195201
38. Zhang WJ, Yang G, Lin Y, Ji C, Gupta, MM (2018) On definition of deep learning. 2018 World Automation Congress (WAC), 1-5. https://doi.org/10.23919/WAC.2018.8430387
39. Baek J, Choi Y (2019) Deep neural network for ore production and crusher utilization prediction of truck haulage system in underground mine. Applied Sciences, 9(19), 4180. https://doi.org/10.3390/app9194180
40. Zhang R, Peng Z, Wu L, Yao B, Guan, Y (2017) Fault diagnosis from raw sensor data using deep neural networks considering temporal coherence. Sensors, 17(3), 549.
https://doi.org/10.3390/s17030549
41. Moolayil J (2019) Learn keras for deep neural networks: a fast-track approach to modern deep learning with python (pp.35-38) (1st ed.). Apress. https://doi.org/10.1007/978-1-4842-4240-7
42. Kingma DP, Ba J (2015). Adam: a method for stochastic optimization. CoRR, abs/1412.6980.
43. Srivastava N, Hinton G, Krizhevsky A, Sutskever I, Salakhutdinov R (2014) Dropout: A simple way to prevent neural networks from overfitting. Journal of Machine Learning Research, 15, 1929-1958. https://jmlr.org/papers/v15/srivastava14a.html
44. Drucker H, Burges CJC, Kaufman L, Smola A, Vapnik V (1997) Support vector regression machines, Adv Neural Inf Process Syst, 9:155-161.
45. Zhi-qiang J, Hang-guang F, Ling-jun L (2005) Support vector machine for mechanical faults classification. J Zhejiang Univ.-Sci. A, 6:433–439.
https://doi.org/10.1631/jzus.2005.A0433
46. Sun J, Rahman M, Wong YS, Hong GS (2004) Multiclassification of tool wear with support vector machine by manufacturing loss consideration. Int J Mach Tools Manuf, 44:1179–1187. https://doi.org/10.1016/j.ijmachtools.2004.04.003
47. Çaydaş U, Ekici, S (2012) Support vector machines models for surface roughness prediction in CNC turning of AISI 304 austenitic stainless steel, J Intell Manuf, 23:639–650. https://doi.org/10.1007/s10845-010-0415-2
48. Seyedzadeh S, Rahimian FP, Glesk I, Roper M (2018) Machine learning for estimation of building energy consumption and performance: a review, Vis in Eng, 6(5):1-20. https://doi.org/10.1186/s40327-018-0064-7
49. Cao LJ, Tay FEH (2003), Support vector machine with adaptive parameters in financial time series forecasting, IEEE Trans Neural Netw, 14(6): 1506-1518.
https://doi.org/10.1109/TNN.2003.820556
50. Huang GB, Zhu QY, Siew CK (2006) Extreme learning machine: theory and applications. Neurocomputing, 70(1-3):489-501.
https://doi.org/10.1016/j.neucom.2005.12.126
51. Li DZ, Zheng X, Xie QW, Jin QB. (2018) A sequential feature extraction method based on discrete wavelet transform, phase space reconstruction, and singular value decomposition and an improved extreme learning machine for rolling bearing fault diagnosis. Proc. Inst. Mech. Eng. E: J. Process Mech. Eng, 232(6), 635–649.
https://doi.org/10.1177/0954408917733130
52. Laddada S, Si-Chaib MO, Benkedjouh T, Drai R. (2020) Tool wear condition monitoring based on wavelet transform and improved extreme learning machine. Proc. Inst. Mech. Eng. C: J. Mech. Eng. Sci, 234(5), 1057-1068. https://doi.org/10.1177/0954406219888544
53. Friedman, JH. Greedy function approximation: a gradient boosting machine, Ann Stat, 2001, 29, 189-232. https://doi.org/10.1214/aos/1013203451
54. Breiman, L. Bagging predictors, Mach Learn, 1996, 24, 123-140.
https://doi.org/10.1007/BF00058655.
55. Swetha, A. et al. Ensemble methods on weak classifiers for improved driver distraction detection, in N. Nain et al. (Eds), Computer Vision and Image Processing (CVIP 2019), Communications in Computer and Information Science (CCIS 1148), Springer, Singapore, 2020, 233-242. https://doi.org/0.1007/978-981-15-4018-9_22.
56. Breiman L (2001) Random forests. Mach Learn, 45:5–32.
https://doi.org/10.1023/A:1010933404324
57. Yang BS, Di X, Han T. (2008) Random forests classifier for machine fault diagnosis. J Mech Sci Technol, 22:1716–1725. https://doi.org/10.1007/s12206-008-0603-6
58. GeeksforGeeks, Gradient boosting in ML. https://www.geeksforgeeks.org/ml-gradient-boosting/, accessed on 17 February, 2022.
59. Tercan H, Meisen T (2022) Machine learning and deep learning based predictive quality in manufacturing: a systematic review. J Intell Manuf 33, 1879–1905.
https://doi.org/10.1007/s10845-022-01963-8
60. Mao W, He J, Tang J, Li Y (2018) Predicting remaining useful life of rolling bearings based on deep feature representation and long short-term memory neural network. Adv. Mech. Eng, 10(12):1-18. https://doi.org/10.1177/1687814018817184
61. Ruqiang Y, Robert XG (2009) Base wavelet selection for bearing vibration signal analysis. Int. J. Wavelets Multiresolution Inf. Process, 7(4):411-426.
https://doi.org/10.1142/S0219691309002994
62. Plaza EG, Nunez Lopez PJ (2018) Application of the wavelet packet transform to vibration signals for surface roughness monitoring in CNC turning operations. Mech Syst Signal Process, 98:902-919. https://doi.org/10.1016/j.ymssp.2017.05.028
63. Kwak JS (2006) Application of wavelet transform technique to detect tool failure in turning operations. Int J Adv Manuf Technol, 28:1078-1083.
https://doi.org/10.1007/s00170-004-2476-x
64. Bailly A, et al., (2022) Effects of dataset size and interactions on the prediction performance of logistic regression and deep learning models. Comput. Methods Programs Biomed, 213, Art. no. 106504. https://doi.org/10.1016/j.cmpb.2021.106504
65. Liu B, Wei Y, Zhang Y, Yang Q (2017) Deep neural networks for high dimension, low sample size data. In Proceedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI′17), AAAI Press, 2287–2293, 2017.
https://doi.org/10.24963/ijcai.2017/318
66. Lee SH, Kim KY, Shin Y (2020) Effective feature selection method for deep learning-based automatic modulation classification scheme using higher-order statistics. Applied Sciences, 10(2), 588. https://doi.org/10.3390/app10020588
67. Liu H (2011) Feature selection. In: Sammut C., Webb G.I. (eds) Encyclopedia of Machine Learning (pp.402-406). Springer. https://doi.org/10.1007/978-0-387-30164-8_306
68. Ostertagová E, Ostertag O (2013) Methodology and application of oneway ANOVA. Am. J. Mech. Eng., 1(7):256-261.
69. Elgeldawi E, Sayed A, Galal AR, Zaki AM (2021) Hyperparameter Tuning for Machine Learning Algorithms Used for Arabic Sentiment Analysis, Informatics, 8:79.
https://doi.org/10.3390/informatics8040079
70. Bergstra J, Bengio Y (2012) Random Search for Hyper-Parameter Optimization. J Mach Learn Res, 13(10), 281-305.
71. Zhao Y, Sun J, Gupta MM, Moody W, Laverty WH, Zhang W (2017) Developing a mapping from affective words to design parameters for affective design of apparel products. Text. Res. J., 87(18):2224-2232. https://doi.org/10.1177/0040517516669072
72. Feng CXJ, Yu ZGS, Emanuel JT, Li PG., et al. (2008) Threefold versus fivefold cross-validation and individual versus average data in predictive regression modelling of machining experimental data. Int J Comput Integr Manuf., 21(6):702-714.
https://doi.org/10.1080/09511920701530943
73. Pedregosa F et al. (2011), Scikit-learn: Machine Learning in Python, J Mach Learn Res, 12(85): 2825-2830.
74. Birecikli B, Karaman ÖA, Çelebi SB, Turgut A (2020) Failure load prediction of adhesively bonded GFRP composite joints using artificial neural networks, J. Mech. Sci. Technol, 34:4631–4640. https://doi.org/10.1007/s12206-020-1021-7.
75. Raschka S (2018) MLxtend: Providing machine learning and data science utilities and extensions to Python′s scientific computing stack, J. Open Source Softw, 3(24), 638. https://doi.org/10.21105/joss.00638
76. Wu D, Jennings C, Terpenny J, Gao RX, Kumara S (2017) A comparative study on machine learning algorithms for smart manufacturing: tool wear prediction using random forest. ASME J Manuf Sci Eng, 139(7), 071018. https://doi.org/10.1115/1.4036350.

指導教授

黃以玫(Huang Yi-Mei)

審核日期

2023-7-4

推文