AISI 420 Paslanmaz Çeliğin Çevre Dostu Frezelenmesinde Makine Öğrenmesi Tabanlı Yüzey Pürüzlülüğü Tahmini

Öz

Martenzitik paslanmaz çelikler, üstün mekanik ve kimyasal özellikleri nedeniyle yaygın olarak kullanılmalarına rağmen, zayıf işlenebilirlik özellikleri yüzey kalitesinde bozulmalara yol açabilmektedir. Bu nedenle, gelişmiş talaşlı imalat yöntemleri kullanılarak malzemenin yüzey kalitesinin iyileştirilmesinin yanı sıra; verimliliğin artırılması, israfın azaltılması ve hatalı fonksiyonların önlenmesi amacıyla yüzey kalitesinin önceden tahmin edilmesi büyük önem taşımaktadır. Bu çalışmada, çok duvarlı karbon nanotüp (ÇDKNT) takviyeli nanoakışkan içeren minimum miktarda yağlama (NMMY) yöntemi kullanılarak sürdürülebilir frezeleme koşullarında işlenen AISI 420 martenzitik paslanmaz çeliğin yüzey pürüzlülüğü, kesme koşulu, MMY debisi, nanoparçacık konsantrasyonu, kesici takım kaplaması ve kesme sıcaklığına bağlı olarak polinomsal regresyon, destek vektör regresyonu, karar ağacı regresyonu, rastgele orman regresyonu ve k-en yakın komşu regresyonu gibi çeşitli makine öğrenmesi modelleri kullanılarak ilk kez tahmin edilmiştir. Titanyum nitrür (TiN) kaplamalı tungsten karbür (WC) kesici takım ile birlikte %0,15 (ağırlıkça) ÇDKNT içeren ve 40 mL/saat MMY debisine sahip NMMY yöntemi, kaplamasız WC kesici takım kullanılan kuru kesme koşullarına kıyasla kesme sıcaklığını ve yüzey pürüzlülüğünü sırasıyla %25,3 ve %49,9 oranında iyileştirmiştir. K-en yakın komşular regresyon modeli, 0,9789'luk belirleme katsayısı (R²) ile yüzey pürüzlülüğü için en yüksek tahmin doğruluğunu elde ederek değerlendirilen diğer makine öğrenme yaklaşımlarından daha iyi performans göstermiştir.

Anahtar Kelimeler

• Paslanmaz çelik
• Makine öğrenmesi
• Yüzey pürüzlülüğü tahmini
• Nanoakışkan
• MQL

Machine Learning-Based Surface Roughness Prediction in Eco-Friendly Milling of AISI 420 Stainless Steel

Öz

Although martensitic stainless steels (MSS) are widely used due to their superior mechanical and chemical properties, their poor machinability can lead to surface quality degradation. Therefore, in addition to improving the surface quality of the material using advanced machining methods, it is important to predict the surface quality to increase efficiency, reduce waste, and prevent faulty functionality. In this study, surface roughness was predicted for the first time using various machine learning models such as, polynomial regression, support vector regression, decision tree regression, random forest regression, and k-nearest neighbors regression, depending on the cutting condition, minimum quantity lubrication (MQL) flow rate, nanoparticle concentration, cutting tool coating, and cutting temperature in sustainable milling of AISI 420 MSS using the MQL method with multi-walled carbon nanotube (MWCNT)-reinforced nanofluid (NMQL). Titanium nitride (TiN)-coated tungsten carbide (WC) cutting tool, NMQL method containing 0.15 wt.% MWCNT and 40 mL/h MQL flow rate improved the cutting temperature and surface roughness by 25.3% and 49.9%, respectively, compared to dry cutting condition with the uncoated WC cutting tool. he K-nearest neighbors regression model achieved the highest predictive accuracy for surface roughness, with a coefficient of determination (R²) of 0.9789, outperforming the other evaluated machine learning approaches.

Anahtar Kelimeler

• Martensitic stainless steel
• Machine learning
• Surface roughness prediction
• Nanofluid
• MQL

Kaynakça

1. E. Dalibón, A. Dalke, H. Biermann, S.P. Brühl, Short time nitriding and nitrocarburizing of martensitic stainless steel, Surf. Coat. Technol. 485 (2024) 130931. https://doi.org/10.1016/j.surfcoat.2024.130931
2. V. Rajkumar, N.S. Shanmugam, N.P. Kumar, K.K. Kumar, A.R. Kannan, Microstructure, mechanical properties, and corrosion behaviour of wire arc additive manufactured martensitic stainless steel 410 for pressure vessel applications, Int. J. Press. Vessels Pip. 209 (2024) 105171. https://doi.org/10.1016/j.ijpvp.2024.105171
3. E. Saatçi, Y.F. Yapan, M. Uslu Uysal, A. Uysal, Orthogonal turning of AISI 310S austenitic stainless steel under hybrid nanofluid-assisted MQL and a sustainability optimization using NSGA-II and TOPSIS, Sustain. Mater. Technol. 36 (2023) e00628. https://doi.org/10.1016/j.susmat.2023.e00628
4. A. Mohanty, S. Gangopadhyay, A. Thakur, On applicability of multilayer coated tool in dry machining of aerospace grade stainless steel, Mater. Manuf. Process. 31 (7) (2015) 869–879. https://doi.org/10.1080/10426914.2015.1070413
5. P. Ranjan, S.S. Hiremath, Finite element simulation and experimental validation of machining martensitic stainless steel using multi-layered coated carbide tools for industry-relevant outcomes, Simul. Model. Pract. Theory 114 (2022) 102411. https://doi.org/10.1016/j.simpat.2021.102411
6. G.M. Krolczyk, R.W. Maruda, J.B. Krolczyk, S. Wojciechowski, M. Mia, P. Nieslony, G. Budzik, Ecological trends in machining as a key factor in sustainable production – A review, J. Clean. Prod. 218 (2019) 601–615. https://doi.org/10.1016/j.jclepro.2019.02.017
7. M.N. Babu, G. Manimaran, Experimental estimation of MQL in turning on AISI 410 stainless steel, Int. J. Mach. Mach. Mater. 19 (6) (2017) 522–537. https://doi.org/10.1504/IJMMM.2017.088894
8. M.H.S. Elmunafi, D. Kurniawan, M.Y. Noordin, Use of castor oil as cutting fluid in machining of hardened stainless steel with minimum quantity of lubricant, Procedia CIRP 26 (2015) 408–411. https://doi.org/10.1016/j.procir.2015.03.001

9. Ç.V. Yıldırım, S. Sirin, T. Kıvak, M. Sarıkaya, A comparative study on the tribological behavior of mono and proportional hybrid nanofluids for sustainable turning of AISI 420 hardened steel with cermet tools, J. Manuf. Process. 73 (2022) 695–714. https://doi.org/10.1016/j.jmapro.2021.11.044
10. A. Hamdi, Y.F. Yapan, A. Uysal, et al., The effects of minimum quantity lubrication parameters on the lubrication efficiency in the turning of plastic mold steel, Int. J. Adv. Manuf. Technol. 132 (2024) 5803–5821. https://doi.org/10.1007/s00170-024-13706-5
11. V.H. Nguyen, T.T. Le, Predicting surface roughness in machining aluminum alloys taking into account material properties, Int. J. Comput. Integr. Manuf. 37 (2024) 1–22. https://doi.org/10.1080/0951192X.2024.2372252
12. L.C. Moreira, W.D. Li, X. Lu, M.E. Fitzpatrick, Supervision controller for real-time surface quality assurance in CNC machining using artificial intelligence, Comput. Ind. Eng. 127 (2019) 158–168. https://doi.org/10.1016/j.cie.2018.12.016
13. P.G. Benardos, G.C. Vosniakos, Predicting surface roughness in machining: A review, Int. J. Mach. Tools Manuf. 43 (2003) 833–844. https://doi.org/10.1016/S0890-6955(03)00059-2
14. R. Teti, D. Mourtzis, D.M. D’Addona, A. Caggiano, Process monitoring of machining, CIRP Ann. 71 (2022) 529–552. https://doi.org/10.1016/j.cirp.2022.05.009
15. K. Ullrich, M. von Elling, K. Gutzeit, M. Dix, M. Weigold, J.C. Aurich, R. Wertheim, I.S. Jawahir, H. Ghadbeigi, AI-based optimisation of total machining performance: A review, CIRP J. Manuf. Sci. Technol. 50 (2024) 40–54. https://doi.org/10.1016/j.cirpj.2024.01.012
16. D. Demircioglu Diren, N. Ozsoy, M. Ozsoy, Optimization of cutting parameters and result predictions with response surface methodology, individual and ensemble machine learning algorithms in end milling of AISI 321, Arab. J. Sci. Eng. 48 (2023) 12075–12089. https://doi.org/10.1007/s13369-023-07642-x
17. N.S. Ross, P.M. Mashinini, C.S. Shibi, M.K. Gupta, M.E. Korkmaz, G.M. Krolczyk, V.S. Sharma, A new intelligent approach of surface roughness measurement in sustainable machining of AM-316L stainless steel with deep learning models, Measurement 230 (2024) 114515. https://doi.org/10.1016/j.measurement.2024.114515
18. Y.S. Chuo, J.W. Lee, C.H. Mun, I.W. Noh, S. Rezvani, D.C. Kim, J. Lee, S.W. Lee, S.S. Park, Artificial intelligence enabled smart machining and machine tools, J. Mech. Sci. Technol. 36 (2022) 1–23. https://doi.org/10.1007/s12206-021-1201-0
19. V. Nasir, F. Sassani, A review on deep learning in machining and tool monitoring: Methods, opportunities, and challenges, Int. J. Adv. Manuf. Technol. 115 (2021) 2683–2709. https://doi.org/10.1007/s00170-021-07325-7
20. C. Prakasvudhisarn, S. Kunnapapdeelert, P. Yenradee, Optimal cutting condition determination for desired surface roughness in end milling, Int. J. Adv. Manuf. Technol. 41 (2009) 440–451. https://doi.org/10.1007/s00170-008-1491-8
21. Z. Jurkovic, G. Cukor, M. Brezocnik, A comparison of machine learning methods for cutting parameters prediction in high speed turning process, J. Intell. Manuf. 29 (2018) 1683–1693. https://doi.org/10.1007/s10845-016-1206-1
22. A. Yeganefar, S.A. Niknam, R. Asadi, The use of support vector machine, neural network, and regression analysis to predict and optimize surface roughness and cutting forces in milling, Int. J. Adv. Manuf. Technol. 105 (2019) 951–965. https://doi.org/10.1007/s00170-019-04227-7
23. V.H. Nguyen, T.T. Le, H.S. Truong, M.V. Le, V.L. Ngo, A.T. Nguyen, H.Q. Nguyen, Applying Bayesian optimization for machine learning models in predicting the surface roughness in single-point diamond turning polycarbonate, Math. Probl. Eng. 2021 (2021) 6815802. https://doi.org/10.1155/2021/6815802
24. A.T. Nguyen, V.H. Nguyen, T.T. Le, N.T. Nguyen, Multiobjective optimization of surface roughness and tool wear in high-speed milling of AA6061 by machine learning and NSGA-II, Adv. Mater. Sci. Eng. 2022 (2022) 5406570. https://doi.org/10.1155/2022/5406570
25. Y. Zhang, X. Xu, Machine learning surface roughnesses in turning processes of brass metals, Int. J. Adv. Manuf. Technol. 121 (2022) 2437–2444. https://doi.org/10.1007/s00170-022-09498-1
26. M.P. Motta, C. Pelaingre, A. Delamézière, L.B. Ayed, C. Barlier, Machine learning models for surface roughness monitoring in machining operations, Procedia CIRP 108 (2022) 710–715. https://doi.org/10.1016/j.procir.2022.03.110
27. U.L. Adizue, A.D. Tura, E.O. Isaya, Surface quality prediction by machine learning methods and process parameter optimization in ultra-precision machining of AISI D2 using CBN tool, Int. J. Adv. Manuf. Technol. 129 (2023) 1375–1394. https://doi.org/10.1007/s00170-023-12366-1
28. İ. Asiltürk, M. Kuntoğlu, R. Binali, H. Akkuş, E. Salur, A comprehensive analysis of surface roughness, vibration, and acoustic emissions based on machine learning during hard turning of AISI 4140 steel, Metals 13 (2023) 437. https://doi.org/10.3390/met13020437
29. A. Kosarac, S. Tabakovic, C. Mladjenovic, M. Zeljkovic, G. Orasanin, Next-gen manufacturing: Machine learning for surface roughness prediction in Ti-6Al-4V biocompatible alloy machining, J. Manuf. Mater. Process. 7 (2023) 202. https://doi.org/10.3390/jmmp7060202
30. A.M. Mazid, T. Imam, K.B. Ahsan, N. Khandoker, Characterising surface roughness of Ti-6Al-4V alloy machined using coated and uncoated carbide tools with variable nose radius by machine learning, Eng. Appl. Artif. Intell. 124 (2023) 106546. https://doi.org/10.1016/j.engappai.2023.106546
31. U. Çaydaş, S. Ekici, Support vector machines models for surface roughness prediction in CNC turning of AISI 304 austenitic stainless steel, J. Intell. Manuf. 23 (2012) 639–650. https://doi.org/10.1007/s10845-010-0415-2
32. T. Zhou, L. He, J. Wu, F. Du, Z. Zou, Prediction of surface roughness of 304 stainless steel and multi-objective optimization of cutting parameters based on GA-GBRT, Appl. Sci. 9 (2019) 3684. https://doi.org/10.3390/app9183684
33. X.A. Vasanth, P.S. Paul, A.S. Varadarajan, A neural network model to predict surface roughness during turning of hardened SS410 steel, Int. J. Syst. Assur. Eng. Manag. 11 (2020) 704–715. https://doi.org/10.1007/s13198-020-00986-9
34. C. Du, C.L. Ho, J. Kaminski, Prediction of product roughness, profile, and roundness using machine learning techniques for a hard turning process, Adv. Manuf. 9 (2021) 206–215. https://doi.org/10.1007/s40436-021-00345-2
35. V. Dubey, A.K. Sharma, D.Y. Pimenov, Prediction of surface roughness using machine learning approach in MQL turning of AISI 304 steel by varying nanoparticle size in the cutting fluid, Lubricants 10 (2022) 81. https://doi.org/10.3390/lubricants10050081
36. P.B. Huang, M.M.W. Inderawati, R. Rohmat, The development of an ANN surface roughness prediction system of multiple materials in CNC turning, Int. J. Adv. Manuf. Technol. 125 (2023) 1193–1211. https://doi.org/10.1007/s00170-022-10709-y
37. M.H. Tsai, J.N. Lee, H.D. Tsai, M.J. Shie, T.L. Hsu, H.S. Chen, Applying a neural network to predict surface roughness and machining accuracy in the milling of SUS304, Electronics 12 (2023) 981. https://doi.org/10.3390/electronics12040981
38. K.S. Bennett, J.M. DePaiva, S.C. Veldhuis, An integrated framework for a multi-material surface roughness prediction model in CNC turning using theoretical and machine learning methods, Int. J. Adv. Manuf. Technol. 131 (2024) 3579–3598. https://doi.org/10.1007/s00170-024-13201-x
39. P. Bober, K. Zgodavová, M. Cicka, M. Mihaliková, J. Brindza, Predictive quality analytics of surface roughness in turning operation using polynomial and artificial neural network models, Processes 12 (2024) 206. https://doi.org/10.3390/pr12010206
40. P. Ranjan, S.S. Hiremath, An experimental investigation on bio-inspired structure position variation on tool surface during turning of difficult to machine materials, J. Mater. Eng. Perform. 33 (2024) 5100–5119. https://doi.org/10.1007/s11665-023-08329-y
41. M. Corcione, Empirical correlating equations for predicting the effective thermal conductivity and dynamic viscosity of nanofluids, Energy Convers. Manag. 52 (2011) 789–793. https://doi.org/10.1016/j.enconman.2010.06.072
42. E. Şirin, T. Kıvak, Ç.V. Yıldırım, Effects of mono/hybrid nanofluid strategies and surfactants on machining performance in the drilling of Hastelloy X, Tribol. Int. 157 (2021) 106894. https://doi.org/10.1016/j.triboint.2021.106894
43. E. Ostertagová, Modelling using polynomial regression, Procedia Eng. 48 (2012) 500–506. https://doi.org/10.1016/j.proeng.2012.09.545
44. M. Awad, R. Khanna, Support vector regression, Efficient Learning Machines: Theories, Concepts, and Applications for Engineers and System Designers (2015) 67–80. https://doi.org/10.1007/978-1-4302-5990-9_4
45. M. Xu, P. Watanachaturaporn, P.K. Varshney, M.K. Arora, Decision tree regression for soft classification of remote sensing data, Remote Sens. Environ. 97 (2005) 322–336. https://doi.org/10.1016/j.rse.2005.05.008
46. V. Rodriguez-Galiano, M. Sanchez-Castillo, M. Chica-Olmo, M. Chica-Rivas, Machine learning predictive models for mineral prospectivity: An evaluation of neural networks, random forest, regression trees and support vector machines, Ore Geol. Rev. 71 (2015) 804–818. https://doi.org/10.1016/j.oregeorev.2015.01.001
47. O. Kramer, Dimensionality reduction with unsupervised nearest neighbors, Springer, Berlin, 2013, pp. 13–23.
48. D. Chicco, M.J. Warrens, G. Jurman, The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation, PeerJ Comput. Sci. 7 (2021) e623. https://doi.org/10.7717/peerj-cs.623
49. A.C. Cameron, F.A. Windmeijer, An R-squared measure of goodness of fit for some common nonlinear regression models, J. Econom. 77 (1997) 329–342. https://doi.org/10.1016/S0304-4076(96)01818-0
50. A. De Myttenaere, B. Golden, B. Le Grand, F. Rossi, Mean absolute percentage error for regression models, Neurocomputing 192 (2016) 38–48. https://doi.org/10.1016/j.neucom.2015.12.114
51. H. Zare Abyaneh, Evaluation of multivariate linear regression and artificial neural networks in prediction of water quality parameters, J. Environ. Health Sci. Eng. 12 (2014) 1–8. https://doi.org/10.1186/2052-336X-12-40
52. J. Zhao, Z. Liu, B. Wang, J. Hu, Y. Wan, Tool coating effects on cutting temperature during metal cutting processes: Comprehensive review and future research directions, Mech. Syst. Signal Process. 150 (2021) 107302. https://doi.org/10.1016/j.ymssp.2020.107302
53. F. Du, L. He, T. Zhou, P. Tian, Z. Zou, X. Zhou, Analysis of droplet characteristics and cooling lubrication effects in MQL milling of 316L stainless steel, J. Mater. Res. Technol. 19 (2022) 4832–4856. https://doi.org/10.1016/j.jmrt.2022.06.132
54. T. Gao, C. Li, Y. Zhang, M. Yang, D. Jia, T. Jin, Y. Hou, R. Li, Dispersing mechanism and tribological performance of vegetable oil-based CNT nanofluids with different surfactants, Tribol. Int. 131 (2019) 51–63. https://doi.org/10.1016/j.triboint.2018.10.025
55. Y. Hwang, J.K. Lee, C.H. Lee, Y.M. Jung, S.I. Cheong, C.G. Lee, B.C. Ku, S.P. Jang, Stability and thermal conductivity characteristics of nanofluids, Thermochim. Acta 455 (2007) 70–74. https://doi.org/10.1016/j.tca.2006.11.036
56. A.A. Seenath, A.A.D. Sarhan, A state-of-the-art review on cutting tool materials and coatings in enhancing the tool performance in machining the superior nickel-based superalloys, Arab. J. Sci. Eng. (2024). https://doi.org/10.1007/s13369-024-08745-9
57. K.D. Bouzakis, E. Bouzakis, S. Kombogiannis, S. Makrimallakis, G. Skordaris, N. Michailidis, P. Charalampous, R. Paraskevopoulou, R. M'Saoubi, J.C. Aurich, F. Barthelmä, D. Biermann, B. Denkena, D. Dimitrov, S. Engin, B. Karpuschewski, F. Klocke, T. Özel, G. Poulachon, J. Rech, V. Schulze, L. Settineri, A. Srivastava, K. Wegener, E. Uhlmann, P. Zeman, Effect of cutting edge preparation of coated tools on their performance in milling various materials, CIRP J. Manuf. Sci. Technol. 7 (2014) 264–273. https://doi.org/10.1016/j.cirpj.2014.05.003
58. Y.S. Ahmed, J.M. Paiva, D. Covelli, S.C. Veldhuis, Investigation of coated cutting tool performance during machining of super duplex stainless steels through 3D wear evaluations, Coatings 7 (2017) 127. https://doi.org/10.3390/coatings7080127
59. M. Mia, M.A. Bashir, M.A. Khan, et al., Optimization of MQL flow rate for minimum cutting force and surface roughness in end milling of hardened steel (HRC 40), Int. J. Adv. Manuf. Technol. 89 (2017) 675–690. https://doi.org/10.1007/s00170-016-9080-8
60. J.S. Dureja, R. Singh, T. Singh, et al., Performance evaluation of coated carbide tool in machining of stainless steel (AISI 202) under minimum quantity lubrication (MQL), Int. J. Precis. Eng. Manuf. Green Technol. 2 (2015) 123–129. https://doi.org/10.1007/s40684-015-0016-9
61. D.B. Cönger, Y.F. Yapan, U. Emiroğlu, A. Uysal, E. Altan, Influence of singular and dual MQL nozzles on sustainable milling of Al6061-T651 in different machining environments, J. Manuf. Process. 109 (2024) 524–536. https://doi.org/10.1016/j.jmapro.2023.12.043
62. A.K. Sharma, A.K. Tiwari, A.R. Dixit, Mechanism of nanoparticles functioning and effects in machining processes: A review, Mater. Today Proc. 2 (2015) 3539–3544. https://doi.org/10.1016/j.matpr.2015.07.331
63. M. Jamil, N. He, W. Zhao, et al., Tribology and machinability performance of hybrid Al₂O₃–MWCNTs nanofluids-assisted MQL for milling Ti-6Al-4V, Int. J. Adv. Manuf. Technol. 119 (2022) 2127–2144. https://doi.org/10.1007/s00170-021-08279-6
64. D. Zhou, C. Li, K. You, K. Bi, Superlubricity transition from ball bearing to nanocoating in the third-body lubrication, Tribol. Int. 181 (2023) 108320. https://doi.org/10.1016/j.triboint.2023.108320
65. A. Yücel, Ç.V. Yıldırım, M. Sarıkaya, Ş. Şirin, T. Kıvak, M.K. Gupta, Í.V. Tomaz, Influence of MoS₂ based nanofluid-MQL on tribological and machining characteristics in turning of AA 2024-T3 aluminum alloy, J. Mater. Res. Technol. 15 (2021) 1688–1704. https://doi.org/10.1016/j.jmrt.2021.09.007
66. O. Öndin, T. Kıvak, M. Sarıkaya, Ç.V. Yıldırım, Investigation of the influence of MWCNTs mixed nanofluid on the machinability characteristics of PH 13-8 Mo stainless steel, Tribol. Int. 148 (2020) 106323. https://doi.org/10.1016/j.triboint.2020.106323