博碩士論文 106383609 詳細資訊



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姓名 阮覃懷(Nguyen Hoai Tan)  查詢紙本館藏   畢業系所 機械工程學系
論文名稱 脈衝雷射切割無定向矽鋼片及人工智能質量預測的實驗研究
(Experimental Study on Pulsed Laser Cutting of Thin Non-Oriented Silicon Steel and Quality Predication Using Artificial Intelligence)
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摘要(中) 中 文 摘 要

本研究旨在研究雷射參數對無定向矽鋼薄片切割質量的影響，然後通過應用人工智能 (AI) 模型來預測和優化切割質量。首先，本研究提出了四個實驗，實驗一和實驗二以較慢的切割速度研究；實驗三和實驗四則以較快的切割速度研究。在四個實驗中都檢查了熱影響區(HAZ)，因為它對矽鋼材料的磁性影響最大，而熱影響區通過在空氣及溶劑的環境條件下以快慢兩種切割速度進行研究。此外，還評估了幾何形狀、表面粗糙度、切割時間、尺寸精度等質量。
實驗一考察了三種雷射參數對切口波紋度和熱影響區兩種切割質量的影響，即雷射功率、切割速度和脈衝重複率。實驗二表明，在水和氯化鈉的環境條件中切割，擁有比在空氣中進行切割更小的熱影響區。實驗二顯示了HAZ和磁通密度之間的負相關。實驗三研究了雷射參數對切割時間（TC）、切口寬度（KT）、切口錐角（）、HAZ、表面粗糙度（Sa）等多種質量的影響。實驗四評估了環境條件（空氣、去離子水、酒精、潤滑油、氯化鈉和 5%、10%、15% 硝酸酒精溶液）對重鑄層、切割邊緣的浮渣/碎屑的影響。
由於在較短的切割時間內切口的質量良好，因此選擇了初始 10% 的條件來研究三個雷射參數 (P、v、f) 對電機鐵芯疊片、HAZ、尺寸精度（內徑誤差ED1、外徑誤差ED2、齒寬誤差EL、內徑圓度C1、外徑圓度C2）、切削時間（TC）。然後，應用極限學習機（ELM）、多元回歸分析（MRA）、人工神經網絡（ANN）和隨機森林（RF）四種人工智能模型來預測切割質量。結果表明，在所使用的四種模型中，ELM 模型的預測最為準確。此外，隨機森林法還用於確定與響應相關的輸入參數的相對重要性。在全部的四個實驗中都表明，雷射功率對 HAZ 的影響最為顯著。
最後，由於ELM在四個 AI 測試模型中預測響應準確度最高，因此選擇 ELM 模型來優化切割質量。通過實施實驗二預測的最佳加工參數，得到的極限學習機-遺傳算法（ELM-GA）進行優化和實驗確認，得到的MFD和HAZ為1.639 T和30.40 µm。在比較三種模型（ELM、MRA、ANN）的預測精度後，開發了一種多目標優化模型，即基於 ELM - GA 算法的偏好選擇指數（PSI）方法，以優化初始 10% 條件下七種疊片鐵芯的品質。進行實驗驗證以評估最佳預測的準確性，HAZ 為 33.4 µm，ED1 為 14 µm，ED2 為 19 µm，C1 為 21 µm，C2 為 26 µm，EL 為 13 µm，TC 為 34.14 s，其中對於 HAZ、ED1、ED2、C1、C2、EL 和 TC，最佳和實驗確認之間的誤差分別為 4.04%、6.52%、4.02%、0.48%、2.14%、5.09% 和 1.25%。因此，無需任何後處理即可直接形成可組裝鐵芯疊片的優點，使所提出的雷射切割方案成為一種經濟而有效的方法。
摘要(英) This study aims to investigate the effects of laser process parameters on the cutting qualities of thin non-oriented silicon steel; then, the cutting qualities are predicted and optimized by the application of the artificial intelligence (AI) models. Firstly, four experiments are presented in the research: experiment I and experiment II study with the slow cutting speed, experiment III and experiment IV research in higher cutting speed. The heat-affected zone (HAZ) is examined in both four experiments because it has the most influence on the magnetic property of silicon steel material, considered by laser cutting in air and solvent conditions with slow and high cutting speed. Besides, the qualities such as geometry, surface roughness, cutting time, dimensional precision are also investigated. Experiment I examines the influences of the three laser process parameters on two cutting qualities of the kerf waviness and HAZ, which are the laser power, cutting speed and pulse repletion rate. Experiment II shows that cutting in water and sodium chloride achieve a smaller HAZ than cutting in air condition. Experiment II also indicates a negative correlation between HAZ and magnetic flux density. Experiment III studies the influences of laser process parameters on multi-qualities, cutting time (TC), kerf width (KT), kerf taper angle (), HAZ, and surface roughness (Sa). Experiment IV evaluates the influences of environmental conditions (air, deionized water, alcohol, lubricant oil, sodium chloride, and nital solution 5%, 10%, 15%) on the recast layer, dross/debris at the cutting edge. Due to the good quality at the cutting edge in the short cutting time, the nital 10% condition is selected for investigating the effects of three process parameters (P, v, f) on the seven cutting qualities of a motor core’s lamination, HAZ, dimensional precision (inner diameter error ED1, outer diameter error ED2, tooth width error EL, inner diameter roundness C1, outer diameter roundness C2), and cutting time (TC).
Then, four AI models, which are an extreme learning machine (ELM), multiple regression analysis (MRA), artificial neural network (ANN), and random forest (RF), are applied to predict the cutting qualities. Results reveal that among the four models used, the most accurate predictions are found by the ELM model. Besides, the random forests method is also addressed to determine the relative importance of input parameters associated with the responses. All four experiments show that laser power is the most significant influence on HAZ.
Finally, the ELM model is selected to optimize the cutting qualities due to the highest predictive response accuracy among the four AI tested models. By implementing the predicted optimal processing parameters for experiment II, the resulting Extreme learning machine - genetic algorithm (ELM-GA) for the optimization and experimental confirmation, the obtained MFD and HAZ is 1.639 T and 30.40 mm. A multi-objective optimization model, the ELM - GA algorithm based preference selection index (PSI) method, is developed to optimize the seven output qualities of laminated core in the nital 10% condition after comparing the predicted accuracy of three models applied (ELM, MRA, ANN). The experimental validation is performed to evaluate the accuracy of optimal prediction with HAZ of 33.4 µm, ED1 of 14 µm, ED2 of 19 µm, C1 of 21 µm, C2 of 26 µm, EL of 13 µm, and TC of 34.14 s, which errors between optimal and experimental confirmation were 4.04%, 6.52%, 4.02%, 0.48%, 2.14%, 5.09%, and 1.25% for HAZ, ED1, ED2, C1, C2, EL, and TC, respectively. Consequently, the merit of direct formation is ready-for-assembly core laminations without the need of any post-processing renders the proposed laser cutting scheme an economical and effective approach for manufacturing ready-for-assembly core laminations from the thin silicon steel sheets.
關鍵字(中) ★ 脉冲激光切割 關鍵字(英) ★ Pulsed Laser Cutting
論文目次 中 文 摘 要 i
ABSTRACT iii
LIST OF FIGURES ix
LIST OF TABLES xiv
LIST OF ABBREVIATIONS xvi
Chapter 1. INTRODUCTION 1
1.1. Background 1
1.2. Literature review 2
1.2.1. Effects of cutting methods on the quality of silicon steel 2
1.2.2. Applications of laser for cutting 5
1.2.3. Applications of artificial intelligence 10
1.2.4. Summary 12
1.3. Purpose and scope 13
1.4. Thesis outline 14
1.5. Scientific contributions 14
Chapter 2. RESEARCH METHODOLOGY 16
2.1. Material and characteristic of laser equipment 17
2.2. Experimental measurement 17
2.3. Experimental methods using in the study 20
2.3.1. Experiment I 20
2.3.2. Experiment II 21
2.3.3. Experiment III 23
2.3.4. Experiment IV 25
2.4. Artificial intelligence models 27
2.4.1. Extreme learning machine 28
2.4.2. Artificial neural network 30
2.4.3. Random forest 31
2.4.4. Multiple regression analysis 32
2.5. Weighting analysis for determination of multi-criteria decision 33
2.5.1. Entropy method 33
2.5.2. Preference selection index method 34
2.6. Optimization method 36
Chapter 3. RESULTS AND DISCUSSION 38
3.1. Experimental results 38
3.1.1. Experimental results I 38
3.1.2. Experimental results II 44
3.1.3. Experimental results III 50
3.1.4. Experimental results IV 57
3.2. Determination of variable importance 68
3.3. Performance analysis of artificial intelligence models 71
3.3.1. Performance analysis of experiment I 72
3.3.2. Performance analysis of experiment II 77
3.3.3. Performance of analysis of experiment III 81
3.3.4. Performance analysis of experiment IV 83
3.5. Optimization’s results 87
3.5.1. Optimization by extreme learning machine - genetic algorithm 87
3.5.2. Multi-Optimization by ELM - GA based PSI method 88
Chapter 4. CONCLUSIONS 92
REFERENCES 97
APPENDIXES 111
PUBLICATIONS 128
參考文獻 1. Schoppa A, Schneider J, Roth JO. Influence of the cutting process on the magnetic properties of non-oriented electrical steels. Journal of magnetism and magnetic materials 2000, 215-216, 100-102. https://doi.org/10.1016/S0304-8853(00)00077-9
2. Loisos G, Moses AJ. Effect of mechanical and Nd:YAG laser cutting on magnetic flux distribution near the cut edge of non-oriented steels. Journal of Materials Processing Technology 2005, 161, 151-155. https://doi.org/10.1016/j.jmatprotec.2004.07.061
3. Fischer O, Schneider J. Influence of deformation process on the
improvement of non-oriented electrical steel. Journal of magnetism and magnetic materials 2003, 254-255, 302-306. https://doi.org/10.1016/S0304-8853(02)00965-4
4. Belhadj A, Baudomin P, Houbaert Y. Simulation of the HAZ and magnetic properties of laser cut non-oriented electrical steels. Journal of magnetism and magnetic materials 2002, 248(1), 34-44. https://doi.org/10.1016/S0304-8853(02)00064-1
5. Emura M, Landgraf FJG, Ross W, Barreta JR. The influence of cutting technique on the magnetic properties of electrical steels. Journal of magnetism and magnetic materials 2003, 254-255, 358-360. https://doi.org/10.1016/S0304-8853(02)00856-9
6. Belhadj A, Baudouin P, Breaban F, Deffontaine A, Dewulf M, Houbaert Y. Effect of laser cutting on microstructure and on magnetic properties of grain non-oriented electrical steels. Journal of magnetism and magnetic materials 2003, 256, 20-31. https://doi.org/10.1016/S0304-8853(01)00937-4
7. Araujo EG, Schneider J, Verbeken K, Pasquarella G, and Houbaert Y. Dimensional effects on magnetic properties of Fe–Si steels due to laser and mechanical cutting. IEEE Transactions on Magnetics 2010, 46, 213-216. 10.1109/TMAG.2009.2034124
8. Siebert R, Schneider J, and Beyer E. Laser cutting and mechanical cutting of electrical steels and its effect on the magnetic properties. IEEE Transactions on Magnetics 2014, 50. 10.1109/TMAG.2013.2285256
9. Hofmann M, Naumoski H, Herr U, Herzog HG. Magnetic properties of electrical steel sheets in respect of cutting: Micromagnetic analysis and macromagnetic modeling. IEEE Transactions on Magnetics 2016, 52. 10.1109/TMAG.2015.2484280
10. Naumoski H, Riedmüller B, Minkow A, Herr U. Investigation of the influence of different cutting procedures on the global and local magnetic properties of non-oriented electrical steel. Journal of magnetism and magnetic materials 2015, 392, 126-133. https://doi.org/10.1016/j.jmmm.2015.05.031
11. Weiss HA, Leuning N, Steentjes S, Hameyer K, Andorfer T, Jenner S, Volk W. Influence of shear cutting parameters on the electromagnetic properties of non-oriented electrical steel sheets. Journal of magnetism and magnetic materials 2017, 421, 250-259. https://doi.org/10.1016/j.jmmm.2016.08.002
12. Landgraf FGJ, Emura M. Losses and permeability improvement by stress relieving fully processed electrical steels with previous small deformations. Journal of magnetism and magnetic materials 2002, 242-245, 152-156. https://doi.org/10.1016/S0304-8853(01)01184-2
13. Paolinelli SC, da Cunha MA. Effect of stress relief annealing temperature and atmosphere on the magnetic properties of silicon steel. Journal of magnetism and magnetic materials 2006, 304 (2): e599-e601. https://doi.org/10.1016/j.jmmm.2006.02.187
14. Chiang CC, Knight AM, Hsieh MF, Tsai MG, Liu BH, Chen IG, Gaing ZL, Tsai MC. Effects of annealing on magnetic properties of electrical steel and performances of SRM after punching. IEEE Transactions on Magnetics 2014, 50. 10.1109/TMAG.2014.2329708
15. Dickmann K. Influence of the laser cutting process on the magnetic properties of electrical sheets. Anales de Fisica B 1990, 86 (82)
16. Kai Y, Yoshida S and Enokizono M. Influence of stress-relief annealing on magnetic properties of motor cores using induced current heating method. Journal of the Japan Society of Applied Electromagnetics and Mechanics 2015, 23, 469-473. https://doi.org/10.14243/jsaem.23.469
17. Park JS, Park JT. Effect of stress relief annealing temperature and atmosphere on the microstructure and magnetic properties of non-oriented electrical steels. 6th International Electric Drives Production Conference (EDPC) 2016, 288-292. IEEE. DOI:10.1109/EDPC.2016.7851344
18. Sheng PS, Joshi VS. Analysis of heat-affected zone formation for laser cutting of stainless steel. Journal of Materials Processing Technology 1995, 53, 879-892. https://doi.org/10.1016/0924-0136(94)01761-O
19. Rajaram N, Sheikh-Ahmad J, Cheraghi SH. CO2 laser cut quality of 4130 steel. International Journal of Machine Tools and Manufacture 2003, 43, 351-358. 10.1016/s0890-6955(02)00270-5
20. Radovanovic M, Madic M. Experimental investigations of CO2 laser cut quality: a review. Nonconventional Technologies Review 2011, 4(4), 35.
21. Yilbas BS. Laser cutting quality assessment and thermal efficiency analysis. Journal of Materials Processing Technology 2004, 155-156, 2106-2115. DOI: 10.1016/j.jmatprotec.2004.04.194
22. Sharma A, Yadava V. Experimental analysis of Nd-YAG laser cutting of sheet materials – A review. Optics & Laser Technology 2018, 98, 264-280. https://doi.org/10.1016/j.optlastec.2017.08.002
23. Thawari G, Sundar JS, Sundararajan G, Joshi SV. Influence of process parameters during pulsed Nd:YAG laser cutting of nickel-base superalloys. Journal of Materials Processing Technology 2005, 170, 229-239. DOI: 10.1016/j.jmatprotec.2005.05.021
24. Leone C, Genna S, Tagliaferri V. Fibre laser cutting of CFRP thin sheets by multi-passes scan technique. Optics and Lasers in Engineering 2014, 53, 43-50. https://doi.org/10.1016/j.optlaseng.2013.07.027
25. Davim JP, Barricas N, Conceição M, Oliveira C. Some experimental studies on CO2 laser cutting quality of polymeric materials. Journal of Materials Processing Technology 2008, 198, 99-104. 10.1016/j.jmatprotec.2007.06.056
26. Joshi P, Sharma A. Simultaneous optimization of kerf taper and heat affected zone in Nd-YAG laser cutting of Al 6061-T6 sheet using hybrid approach of grey relational analysis and fuzzy logic. Precision Engineering 2018, 54, 302-313. https://doi.org/10.1016/j.precisioneng.2018.06.007
27. Ghany KA, Newishy M. Cutting of 1.2mm thick austenitic stainless steel sheet using pulsed and CW Nd:YAG laser. Journal of Materials Processing Technology 2005, 168, 438-447. DOI: 10.1016/j.jmatprotec.2005.02.251
28. Leone C, Genna S, Caggiano A, Tagliaferri V, Molitierno R. Influence of process parameters on kerf geometry and surface roughness in Nd:YAG laser cutting of Al 6061T6 alloy sheet. The International Journal of Advanced Manufacturing Technology 2016, 87, 2745-2762. https://doi.org/10.1007/s00170-016-8667-4
29. Teixidor D, Ciurana J, Rodriguez CA. Dross formation and process parameters analysis of fibre laser cutting of stainless steel thin sheets. The International Journal of Advanced Manufacturing Technology 2014, 71, 1611–1621. https://doi.org/10.1007/s00170-013-5599-0
30. Muhammad N, Whitehead D, Boor A, Li L. Comparison of dry and wet fibre laser profile cutting of thin 316L stainless steel tubes for medical device applications. Journal of Materials Processing Technology 2010, 210, 2261-2267. https://doi.org/10.1016/j.jmatprotec.2010.08.015
31. Jain RK, Agrawal DK, Vishwakama SC, Choubey AK, Upadhyaya BN and Oak SM. Development of underwater laser cutting technique for steel and zircaloy for nuclear applications. Pramana 2010, 75,1253-1258. https://doi.org/10.1007/s12043-010-0214-5
32. Mullick S, Madhukar YK, Roy S, Nath AK. An investigation of energy loss mechanisms in water-jet assisted underwater laser cutting process using an analytical model. International Journal of Machine Tools and Manufacture 2015, 91, 62-75. https://doi.org/10.1016/j.ijmachtools.2015.02.005
33. Li L, Achara C. Chemical assisted laser machining for the minimisation of recast and heat affected zone. CIRP Annals 2004, 53, 175-178. https://doi.org/10.1016/S0007-8506(07)60672-6
34. Demir AG, Previtali B. Dross-free submerged laser cutting of AZ31 Mg alloy for biodegradable stents. Journal of Laser Applications 2016, 28(3), 2001. https://doi.org/10.2351/1.4944751
35. Darwish S, Ahmed N, Alahmari AM, Mufti NA. A comparison of laser beam machining of micro-channels under dry and wet mediums. The International Journal of Advanced Manufacturing Technology 2016, 83(9-12), 1539-1555. https://doi.org/10.1007/s00170-015-7658-1
36. Tamrin KF, Nukman Y, Choudhury IA, Shirley S. Multiple-objective optimization in precision laser cutting of different thermoplastics. Optics and Lasers in Engineering 2015, 67, 57-65. https://doi.org/10.1016/j.optlaseng.2014.11.001
37. Madic MJ, Radovanovic MR. Analysis of the heat affected zone in CO2 laser cutting of stainless steel. Thermal Science 2012, 16, 363-373. DOI:10.2298/TSCI120424175M
38. Rao S, Sethi A, Das AK, Mandal N, Kiran P, Ghosh R, Dixit A R, Mandal A. Fibre Laser Cutting of CFRP Composites and Optimization of Process Parameters through Response Surface Methodology. Materials and Manufacturing Processes 2017, 32(14), 1612-1621. https://doi.org/10.1080/10426914.2017.1279296
39. Biswas R, Kuar AS, Mitra S. Multi-objective optimization of hole characteristics during pulsed Nd:YAG laser microdrilling of gamma-titanium aluminide alloy sheet. Optics and Lasers in Engineering 2014, 60, 1-11. https://doi.org/10.1016/j.optlaseng.2014.03.014
40. Mathew J, Goswami GL, Ramakrishnan N, Naik NK. Parametric studies on pulsed Nd:YAG laser cutting of carbon fibre reinforced plastic composites. Journal of Materials Processing Technology 1999, 89, 198-203. https://doi.org/10.1016/S0924-0136(99)00011-4
41. Sharma A, Yadava V. Modelling and optimization of cut quality during pulsed Nd:YAG laser cutting of thin Al-alloy sheet for curved profile. Optics and Lasers in Engineering 2013, 51, 77-88. https://doi.org/10.1016/j.optlaseng.2012.07.012
42. Norkey G, Dubey AK, Agrawal S. Artificial intelligence based modeling and optimization of heat affected zone in Nd:YAG laser cutting of duralumin sheet. Journal of Intelligent & Fuzzy Systems 2014, 27, 1545-1555. DOI: 10.3233/ifs-141121
43. Tsai MJ, Li CH, Chen CC. Optimal laser-cutting parameters for QFN packages by utilizing artificial neural networks and genetic algorithm. Journal of Materials Processing Technology 2008, 208, 270-283. https://doi.org/10.1016/j.jmatprotec.2007.12.138
44. Madić M, Radovanović M. Comparative modeling of CO2 laser cutting using multiple regression analysis and artificial neural network. International Journal of Physical Sciences 2012, 7, 2422-2430. DOI: 10.5897/ijps12.109
45. Masoudi S, Mirabdolahi M, Dayyani M, Jafarian F, Vafadar A, Dorali MR. Development of an intelligent model to optimize heat-affected zone, kerf, and roughness in 309 stainless steel plasma cutting by using experimental results. Materials and Manufacturing Processes 2019, 34(3), 345-356. https://doi.org/10.1080/10426914.2018.1532579
46. Quintana G, Rudolf T, Ciurana J, Brecher C. Surface roughness prediction through internal kernel information and external accelerometers using artificial neural networks. Journal of Mechanical Science and Technology 2011, 25, 2877-2886. https://doi.org/10.1007/s12206-011-0806-0
47. Maudes J, Bustillo A, Guerra AJ, Ciurana J. Random Forest ensemble prediction of stent dimensions in microfabrication processes. The International Journal of Advanced Manufacturing Technology 2017, 91(1), 879-893. https://doi.org/10.1007/s00170-016-9695-9
48. Syn CZ, Mokhtar M, Feng CJ, Manurung YH. Approach to prediction of laser cutting quality by employing fuzzy expert system. Expert Systems with Applications 2011, 38(6), 7558-7568. https://doi.org/10.1016/j.eswa.2010.12.111
49. Nukman Y, Hassan M, Harizam M. Optimization of Prediction Error in CO2 Laser Cutting process by Taguchi Artificial Neural Network Hybrid with Genetic algorithm. Applied Mathematics & Information Sciences 2013, 7(1), 363-370. DOI: 10.12785/amis/070145
50. Jović S, Radović A, Šarkoćević Ž, Petković D, Alizamir M. Estimation of the laser cutting operating cost by support vector regression methodology. Applied Physics A 2016, 122(9), 1-5. https://doi.org/10.1007/s00339-016-0287-1
51. Anicic O, Jović S, Skrijelj H, Nedić B. Prediction of laser cutting heat affected zone by extreme learning machine. Optics and Lasers in Engineering 2017, 88, 1-4. https://doi.org/10.1016/j.optlaseng.2016.07.005
52. Mustafa AY. Modelling of the hole quality characteristics by Extreme Learning Machine in fiber laser drilling of Ti-6Al-4V. Journal of Manufacturing Processes 2018, 36, 138-148. https://doi.org/10.1016/j.jmapro.2018.10.003
53. Kuar AS, Acherjee B, Ganguly D, Mitra S. Optimization of Nd:YAG Laser Parameters for Microdrilling of Alumina with Multiquality Characteristics via Grey–Taguchi Method. Materials and manufacturing processes 2012, 27(3), 329-336. https://doi.org/10.1080/10426914.2011.585493
54. Canel T, Zeren M, Sınmazçelik T. Laser parameters optimization of surface treating of Al 6082-T6 with Taguchi method. Optics & Laser Technology 2019, 120, 105714. https://doi.org/10.1016/j.optlastec.2019.105714
55. Liu Y, Liu C, Liu W, Ma Y, Tang S, Liang C, Cai Q, Zhang C. Optimization of parameters in laser powder deposition AlSi10Mg alloy using Taguchi method. Optics & Laser Technology 2019, 111, 470-480. https://doi.org/10.1016/j.optlastec.2018.10.030
56. Sharma A, Yadava V. Optimization of Cut Quality Characteristics during Nd:YAG Laser Straight Cutting of Ni-Based Superalloy Thin Sheet Using Grey Relational Analysis with Entropy Measurement. Materials and Manufacturing Processes 2011, 26(12), 1522-1529. https://doi.org/10.1080/10426914.2011.551910
57. Pandey AK, Dubey AK. Multiple quality optimization in laser cutting of difficult-to-laser-cut material using grey–fuzzy methodology. The International Journal of Advanced Manufacturing Technology 2012, 65(1-4), 421-431. https://doi.org/10.1007/s00170-012-4181-5
58. Kumar SS, Uthayakumar M, Kumaran ST, Parameswaran P, Mohandas E, Kempulraj G, Babu BR, Natarajan SA. Parametric optimization of wire electrical discharge machining on aluminium based composites through grey relational analysis. Journal of Manufacturing Processes 2015, 20, 33-39. https://doi.org/10.1016/j.jmapro.2015.09.011
59. Chandrasekhar N, Ragavendran M, Ravikumar R, Vasudevan M, Murugan S. Optimization of hybrid laser–TIG welding of 316LN stainless steel using genetic algorithm. Materials and Manufacturing Processes 2017, 32(10), 1094-1100. https://doi.org/10.1080/10426914.2017.1317793
60. Kalita K, Shivakoti I, Ghadai RK. Optimizing process parameters for laser beam micro-marking using genetic algorithm and particle swarm optimization. Materials and Manufacturing Processes 2017, 32(10), 1101-1108. https://doi.org/10.1080/10426914.2017.1303156
61. Pandey AK, Dubey AK. Simultaneous optimization of multiple quality characteristics in laser cutting of titanium alloy sheet. Optics & Laser Technology 2012, 44, 1858-1865. https://doi.org/10.1016/j.optlastec.2012.01.019
62. Jain NK, Jain VK, Deb K. Optimization of process parameters of mechanical type advanced machining processes using genetic algorithms. International Journal of Machine Tools and Manufacture 2007, 47(6), 900-919. https://doi.org/10.1016/j.ijmachtools.2006.08.001
63. Shrivastava PK, Pandey AK. Geometrical quality evaluation in laser cutting of Inconel-718 sheet by using Taguchi based regression analysis and particle swarm optimization. Infrared Physics & Technology 2018, 89, 369-380. https://doi.org/10.1016/j.infrared.2018.01.028
64. Sun W, Wang C, Zhang C. Factor analysis and forecasting of CO2 emissions in Hebei, using extreme learning machine based on particle swarm optimization. Journal of cleaner production 2017, 162, 1095-1101. https://doi.org/10.1016/j.jclepro.2017.06.016
65. Ciurana J, Arias G, Ozel T. Neural Network Modeling and Particle Swarm Optimization (PSO) of Process Parameters in Pulsed Laser Micromachining of Hardened AISI H13 Steel. Materials and Manufacturing Processes 2009, 24(3), 358-368. https://doi.org/10.1080/10426910802679568
66. Kalita K, Shivakoti I, Ghadai RK. Optimizing process parameters for laser beam micro-marking using genetic algorithm and particle swarm optimization. Materials and Manufacturing Processes 2017, 32(10), 1101-1108. https://doi.org/10.1080/10426914.2017.1303156
67. Leone C, Genna S, Tagliaferri F. Multiobjective optimisation of nanosecond fiber laser milling of 2024 T3 aluminium alloy. Journal of Manufacturing Processes 2020, 57, 288-301. https://doi.org/10.1016/j.jmapro.2020.06.026
68. Britto ASF, Raj RE, Mabel MC. Prediction and optimization of mechanical strength of diffusion bonds using integrated ANN-GA approach with process variables and metallographic characteristics. Journal of Manufacturing Processes 2018, 32, 828-838. https://doi.org/10.1016/j.jmapro.2018.04.015
69. Wang ZH, Gong DY, Li X, Li GT, Zhang DH. Prediction of bending force in the hot strip rolling process using artificial neural network and genetic algorithm (ANN-GA). The International Journal of Advanced Manufacturing Technology 2017, 93(9-12), 3325-3338.
70. Laouissi A, Yallese MA, Belbah A, Belhadi S, Haddad A. Investigation, modeling, and optimization of cutting parameters in turning of gray cast iron using coated and uncoated silicon nitride ceramic tools. Based on ANN, RSM, and GA optimization. The International Journal of Advanced Manufacturing Technology 2018, 101(1-4), 523-548. https://doi.org/10.1007/s00170-018-2931-8
71. Pandey AK, Dubey AK. Modeling and optimization of kerf taper and surface roughness in laser cutting of titanium alloy sheet. Journal of mechanical science and technology 2013, 27(7), 2115-2124. https://doi.org/10.1007/s12206-013-0527-7
72. Rong Y, Zhang G, Chang Y, Huang Y. Integrated optimization model of laser brazing by extreme learning machine and genetic algorithm. The International Journal of Advanced Manufacturing Technology 2016, 87(9), 2943-2950. https://doi.org/10.1007/s00170-016-8649-6
73. Ciurana J, Arias G, Ozel T. Neural Network Modeling and Particle Swarm Optimization (PSO) of Process Parameters in Pulsed Laser Micromachining of Hardened AISI H13 Steel. Materials and Manufacturing Processes 2009, 24(3), 358-368. https://doi.org/10.1080/10426910802679568
74. Wen KL, Chang TC, You ML. The grey entropy and its application in weighting analysis. In SMC′98 Conference Proceedings. IEEE International Conference on Systems, Man, and Cybernetics 1998, 2, 1842-1844. DOI: 10.1109/ICSMC.1998.728163
75. Datta S, Nandi G, Bandyopadhyay A. Application of entropy measurement technique in grey based Taguchi method for solution of correlated multiple response optimization problems: A case study in welding. Journal of Manufacturing Systems 2009, 28(2-3), 55-63. https://doi.org/10.1016/j.jmsy.2009.08.001
76. Moshat S, Datta S, Bandyopadhyay A, Pal PK. Parametric optimization of CNC end milling using entropy measurement technique combined with grey-Taguchi method. International Journal of Engineering, Science and Technology 2010, 2(2), 1-12.
77. Chaki S, Bose D, Bathe RN. Multi-Objective Optimization of Pulsed Nd: YAG Laser Cutting Process Using Entropy-Based ANN-PSO Model. Lasers in Manufacturing and Materials Processing 2020, 7(1), 88-110. https://doi.org/10.1007/s40516-019-00109-8
78. Shannon CE, Weaver W. The mathematical theory of communication. Champaign, IL: University of Illinois press 1947.
79. Wang TC, Lee HD. Developing a fuzzy TOPSIS approach based on subjective weights and objective weights. Expert Systems with Applications 2009, 36(5), 8980–8985. https://doi.org/10.1016/j.eswa.2008.11.035
80. Tiwari V, Jain PK, Tandon P. An integrated Shannon entropy and TOPSIS for product design concept evaluation based on bijective soft set. Journal of Intelligent Manufacturing 2019, 30(4), 1645-1658. https://doi.org/10.1007/s10845-017-1346-y
81. Oluah C, Akinlabi ET, Njoku HO. Selection of phase change material for improved performance of Trombe wall systems using the entropy weight and TOPSIS methodology. Energy and Buildings 2020, 217, 109967. https://doi.org/10.1016/j.enbuild.2020.109967
82. Maniya K, Bhatt MG. A selection of material using a novel type decision-making method: Preference selection index method. Materials & Design 2010, 31(4), 1785-1789. https://doi.org/10.1016/j.matdes.2009.11.020
83. Miloš M, Antucheviciene J, Radovanović M, Petković D. Determination of laser cutting process conditions using the preference selection index method. Optics & Laser Technology 2017, 89, 214-220. https://doi.org/10.1016/j.optlastec.2016.10.005
84. Thin-gauge silicon steel strips, Nikkindenji kogyo Co., Ltd. http://www.nikkindenjikogyo.co.jp/english/company-english.html.
85. Österle W, Rooch H, Pyzalla A, Wang L. Investigation of white etching layers on rails by optical microscopy, electron microscopy, X-ray and synchrotron X-ray diffraction. Materials Science and Engineering: A 2001, 303(1-2), 150-157. https://doi.org/10.1016/S0921-5093(00)01842-6
86. Harada H, Mikami T, Shibata M, Sokai D, Yamamoto A, Tsubakino H. Microstructural Changes and Crack Initiation with White Etching Area Formation under Rolling/Sliding Contact in Bearing Steel. ISIJ International 2005, 45(12), 1897-1902.
87. Zhang HW, Ohsaki S, Mitao S, Ohnuma M, Hono K. Microstructural investigation of white etching layer on pearlite steel rail. Materials Science and Engineering: A 2006, 421(1-2), 191-199. https://doi.org/10.1016/j.msea.2006.01.033
88. Madhav SP. Quality engineering using robust design. Prentice Hall 1989, New Jersey.
89. Cica D, Kramar D. Multi-objective optimization of high-pressure jet-assisted turning of Inconel 718. The International Journal of Advanced Manufacturing Technology 2019, 105 (11), 4731-4745
90. Phillip JR. Taguchi techniques for quality engineering. McGraw-Hill 1996, New Delhi.
91. Chowdhary, K. R. Fundamentals of Artificial Intelligence. New Delhi: Springer India 2020. ISBN 978-81-322-3972-7. https://doi.org/10.1007/978-81-322-3972-7
92. Joshi A V. Machine Learning and Artificial Intelligence. Springer Nature Switzerland AG 2020. ISBN 978-3-030-26622-6. https://doi.org/10.1007/978-3-030-26622-6
93. Huang GB, Zhu QY, Siew CK. Extreme learning machine: Theory and applications. Neurocomputing 2006, 70(1-3), 489-501. https://doi.org/10.1016/j.neucom.2005.12.126
94. Nian R, He B, Zheng B, Van Heeswijk M, Yu Q, Miche Y, Lendasse A. Extreme learning machine towards dynamic model hypothesis in fish ethology research. Neurocomputing 2014, 128, 273-284. https://doi.org/10.1016/j.neucom.2013.03.054
95. Abdullah SS, Malek MA, Abdullah NS, Kisi O, Yap KS. Extreme Learning Machines: A new approach for prediction of reference evapotranspiration. Journal of Hydrology 2015, 527, 184-195. https://doi.org/10.1016/j.jhydrol.2015.04.073
96. Mohammadi K, Shamshirband S, Motamedi S, Petković D, Hashim R, Gocic M. Extreme learning machine based prediction of daily dew point temperature. Computers and Electronics in Agriculture 2015, 117, 214-225. https://doi.org/10.1016/j.compag.2015.08.008
97. Kasabov NK. Foundations of Neural Networks, A Bradford Book, (The MIT Press, Fuzzy systems and Knowledge Engineering, Cambridge, Massachusetts, London, England, 1995), 581 pp.
98. Shanmuganathan S, Samarasinghe S (eds.), Artificial Neural Network Modelling, Studies in Computational Intelligence 628, DOI 10.1007/978-3-319-28495-8_1
99. Breiman L. Random Forests. Machine Learning 2001, 45(1), 5-32.
100. Ho TK. Random Decision Forests. Proceedings of the 3rd International Conference on Document Analysis and Recognition, Montreal, QC, 14–16 August 1995, 278-282.
101. Chelgani SC, Matin SS, Hower JC. Explaining relationships between coke quality index and coal properties by Random Forest method. Fuel 2016, 182, 754-760. https://doi.org/10.1016/j.fuel.2016.06.034
102. Shahbazi B, Chelgani SC, Matin SS. Prediction of froth flotation responses based on various conditioning parameters by Random Forest method. Colloids and Surfaces A: Physicochemical and Engineering Aspects 2017, 529, 936-941. https://doi.org/10.1016/j.colsurfa.2017.07.013
103. Draper NR, Smith H. Applied Regression Analysis. Wiley Series in Probability and Statistics 1998. Doi:10.1002/9781118625590
104. Singh T, Tejyan S, Patnaik A, Chauhan R, Fekete G. Optimal design of needle punched nonwoven fiber reinforced epoxy composites using improved preference selection index approach. Journal of Materials Research and Technology, 2020, 9(4): 7583-7591. https://doi.org/10.1016/j.jmrt.2020.04.101
105. Steen W, Mazumder J. Laser Material Processing. 4th Ed. London: Springer-Verlag; 2010. ISBN: 978-1-84996-062-5. https://doi.org/10.1007/978-1-84996-062-5
106. Wang H, Xu Y, Liu J, Hu Q, Wang X, Ren N, Zhou W, Ren X. Magnet-assisted laser hole-cutting in magnesium alloys with and without water immersion. Journal of Manufacturing Processes 2021, 61, 539-560. https://doi.org/10.1016/j.jmapro.2020.11.026
107. Choubey A, Jain RK, Ali S, Singh R, Vishwakarma SC, Agrawal DK, Oak SM. Studies on pulsed Nd: YAG laser cutting of thick stainless steel in dry air and underwater environment for dismantling applications. Optics & Laser Technology 2015, 71, 6-15.
108. Saleem A, Goldbaum D, Brodusch N, Gauvin R, Chromik RR. Microstructure and mechanical property connections for a punched non-oriented electrical steel lamination. Materials Science and Engineering: A 2018, 725, 456-465.
https://doi.org/10.1016/j.msea.2018.04.054
指導教授 何正榮 董必正(Jeng-Rong Ho Pi-Cheng Tung) 審核日期 2021-10-18
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