Artificial intelligence modeling of induction contour hardening of 300M steel bar and C45 steel spur-gear
Source abstract
Abstract
Induction hardening is a heat surface treatment technique widely employed for steel components in order to improve their fatigue life without affecting the metallurgy of the bulk material. The control of the treated components goes through the prediction and the optimization of the induction hardening process parameters. The aim of this work is to propose an approach based on artificial intelligence technique to predict the in-depth hardness profile. For this purpose, experimental tests were first carried out on 300M steel bar and C45 steel spur-gear under single and double frequencies, respectively. Intermediate variables were then generated to be used as input data. Data-driven model based on XGBoost library was finally developed. It was found that the proposed approach predicts with good agreement the hardness profiles and can be used in induction treatment process optimization.
Source: openaire · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Induction Heating and Inverter Technology · Microstructure and Mechanical Properties of Steels · Metal Alloys Wear and Properties
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (53)
Show source-provided references
1. Haimbaugh RE (2015) Practical induction heat treating. ASM international10.31399/asm.tb.piht2.9781627083119 ↗
2. Qiu G, Zhan D, Li C, Yang Y, Qi M, Jiang Z, Zhang H (2020) Effects of yttrium and heat treatment on the microstructure and mechanical properties of clam steel. J Mater Eng Perform 29(1):42–5210.1007/s11665-020-04574-7 ↗
3. Celada-Casero C, Huang B, Yang J-R, San-Martin D (2019) Microstructural mechanisms controlling the mechanical behaviour of ultrafine grained martensite/austenite microstructures in a metastable stainless steel. Mater Des 181:10792210.1016/j.matdes.2019.107922 ↗
4. Hömberg D, Liu Q, Montalvo-Urquizo J, Nadolski D, Petzold T, Schmidt A, Schulz A (2016) Simulation of multi-frequency-induction-hardening including phase transitions and mechanical effects. Finite Elem Anal Des 121:86–10010.1016/j.finel.2016.07.012 ↗
5. Maresca F, Kouznetsova V, Geers M, Curtin W (2018) Contribution of austenite-martensite transformation to deformability of advanced high strength steels : from atomistic mechanisms to microstructural response. Acta Mater 156:463–47810.1016/j.actamat.2018.06.028 ↗
6. Xu S, Li J, Cui Y, Zhang Y, Sun L, Li J, Luan J, Jiao Z, Wang X-L, Liu C et al (2020) Mechanical properties and deformation mechanisms of a novel austenite-martensite dual phase steel. Int J Plast 128:10267710.1016/j.ijplas.2020.102677 ↗
7. Candeo A, Ducassy C, Bocher P, Dughiero F (2011) Multiphysics modeling of induction hardening of ring gears for the aerospace industry. IEEE Trans Magn 47(5):918–92110.1109/TMAG.2010.2073682 ↗
8. Wuppermann C, Míček E (2018) Importance of heat treatment for the variety of applications of modern materials. Prozesswarme 2018:95–101
9. Al Salkhadi AAM, (2021) Modélisation et simulation du procédé de durcissement par induction appliqué à des pignons à chaîne-chauffage à double fréquence. Ph.D. dissertation, Université du Québec à Rimouski
10. Barglik J, Ducki K, Kukla D, Mizera J, Mrówka-Nowotnik G, Sieniawski J, Smalcerz A (2018) Comparison of single and consecutive dual frequency induction surface hardening of gear wheels. In: IOP conference series: materials science and engineering, vol 355, 1 IOP Publishing, pp 01201510.1088/1757-899X/355/1/012015 ↗
11. Kristoffersen H, Vomacka P (2001) Influence of process parameters for induction hardening on residual stresses. Mater Des 22(8):637–644. https://linkinghub.elsevier.com/retrieve/pii/S026130690100033410.1016/S0261-3069(01)00033-4 ↗
12. Li J, Cao Z, Liu L, Liu X, Peng J (2021) Effect of microstructure on hardness and wear properties of 45 steel after induction hardening, Steel Re Int 92(4):2000540. https://onlinelibrary.wiley.com/doi/10.1002/srin.20200054010.1002/srin.202000540 ↗
13. Hájek J, Rot D, Jiřinec J (2019) Distortion in induction-hardened cylindrical part. Defect Diff Forum 395:30–44. https://www.scientific.net/DDF.395.3010.4028/www.scientific.net/DDF.395.30 ↗
14. Grum J (2001) A review of the influence of grinding conditions on resulting residual stresses after induction surface hardening and grinding. J Mater Process Technol pp 1510.1016/S0924-0136(01)00562-3 ↗
15. Rodman D, Krause C, Nürnberger F, Bach F-W, Haskamp K, Kästner M, Reithmeier E (2011) Induction hardening of spur gearwheels made from 42CrMo4 hardening and tempering steel by employing spray cooling. Steel Res Int 82(4):329–336 https://onlinelibrary.wiley.com/doi/10.1002/srin.20100021810.1002/srin.201000218 ↗
16. Yi J, Gharghouri M, Bocher P, Medraj M (2013) Distortion and residual stress measurements of induction hardened AISI 4340 discs. Mater Chem Phys 142(1):248–258. https://linkinghub.elsevier.com/retrieve/pii/S025405841300535X10.1016/j.matchemphys.2013.07.012 ↗
17. Hutton DV (2003) Fundamentals of finite element analysis. McGraw-Hill Science Engineering
18. Guo Y, Liu M, Yan Y (2021) Hardness prediction of grind-hardening layer based on integrated approach of finite element and cellular automata. Materials 14(19):565110.3390/ma14195651 ↗
19. Derouiche K, Garois S, Champaney V, Daoud M, Traidi K, Chinesta F (2021) Data-driven modeling for multiphysics parametrized problems-application to induction hardening process. Metals 11(5):73810.3390/met11050738 ↗
20. Zhang Y, Ruan J, Huang T, Yang X, Zhu H, Yang G (2012) Calculation of temperature rise in air-cooled induction motors through 3-d coupled electromagnetic fluid-dynamical and thermal finite-element analysis. IEEE Trans Magn 48(2):1047–105010.1109/TMAG.2011.2174433 ↗
21. Javaheri V, Pohjonen A, Asperheim JI, Ivanov D, Porter D (2019) Physically based modeling, characterization and design of an induction hardening process for a new slurry pipeline steel. Mater Des 182:10804710.1016/j.matdes.2019.108047 ↗
22. Daoud M, Kubler R, Bemou A, Osmond P, Polette A (2021) Prediction of residual stress fields after shot-peening of trip780 steel with second-order and artificial neural network models based on multi-impact finite element simulations. Journal of Manufacturing Processes 72:529–54310.1016/j.jmapro.2021.10.034 ↗
23. Fisk M, Lindgren L-E, Datchary W, Deshmukh V (2018) Modelling of induction hardening in low alloy steels. Finite elements in analysis and design 144:61–7510.1016/j.finel.2018.03.002 ↗
24. Sumithra P, Thiripurasundari D (2017) Review on computational electromagnetics. Advanced Electromagnetics 6(1):42–5510.7716/aem.v6i1.407 ↗
25. W. Li, Z. Yuan, and Z. Chen (2013) Adaptive mesh morphing method for numerical analysis of electromagneto-mechanical coupling using lagrangian approach. In: Proc 19th Int Conf Comput Electromagn Fields COMPUMAG, pp 1–2
26. N. Barka, P. Bocher, J. Brousseau, M. Galopin, and S. Sundararajan (2007) Modeling and sensitivity study of the induction hardening process. In: Advanced Materials Research, vol 15. Trans Tech Publ, pp 525–53010.4028/www.scientific.net/AMR.15-17.525 ↗
27. Leitner M, Aigner R, Grün F (2019) Numerical fatigue analysis of induction-hardened and mechanically post-treated steel components. Machines 7(1):110.3390/machines7010001 ↗
28. Kurek K, Dolega D (2003) Modeling of induction hardening. Int sci colloquium : modeling of electromagnetic processing. Hannover, Germany, pp 125–130
29. Kennedy MW, Akhtar S, Bakken JA, Aune RE (2011) Analytical and experimental validation of electromagnetic simulations using comsol®, reinductance, induction heating and magnetic fields. COMSOL users conference. Stuttgart Germany, Citeseer, pp 1–9
30. M. W. Kennedy, S. Akhtar, J. A. Bakken, and R. E. Aune (2011) Review of classical design methods as applied to aluminum billet heating with induction coils. In: EPD congress 2011-TMS 2011 annual meeting and exhibition10.1002/9781118495285.ch77 ↗
31. San Diego, CA, United States, 27 February-3 March, 2011, pp 707-722
32. Areitioaurtena M, Segurajauregi U, Akujärvi V, Fisk M, Urresti I, Ukar E (2021) A semi-analytical coupled simulation approach for induction heating. Advanced Modeling and Simulation in Engineering Sciences 8(1):1–1910.1186/s40323-021-00199-0 ↗
33. Hömberg D, Petzold T, Rocca E (2015) Analysis and simulations of multifrequency induction hardening. Nonlinear Analysis: Real World Applications 22:84–97
34. Smokvina Hanza S, Marohnić T, Iljkić D, Basan R (2021) Artificial neural networks-based prediction of hardness of low-alloy steels using specific jominy distance. Metals 11(5):71410.3390/met11050714 ↗
35. Asadzadeh MZ, Raninger P, Prevedel P, Ecker W, Mücke M (2021) Hybrid modeling of induction hardening processes. Applications in Engineering Science 5:10003010.1016/j.apples.2020.100030 ↗
36. A. Sorsa, K. Leiviskä, S. Santa-aho, and T. Lepistö (2012) Quantitative prediction of residual stress and hardness in case-hardened steel based on the Barkhausen noise measurement. NDT E International 46:100–106. https://linkinghub.elsevier.com/retrieve/pii/S096386951100173310.1016/j.ndteint.2011.11.008 ↗
37. J. Wróbel and A. Kulawik (2015) Using the artificial neural networks in the modelling of the induction heating. In: AIP conference proceedings, vol 1648. AIP Publishing LLC, pp 85009010.1063/1.4913145 ↗
38. P. D. Deshpande, B. Gautham, U. Gupta, and D. Khan (2014) Modeling the steel case carburizing quenching process using statistical and machine learning techniques. In: 2014 9th international conference on industrial and information systems (ICIIS), IEEE, pp 1–610.1109/ICIINFS.2014.7036589 ↗
39. Lambiase F, Di Ilio A, Paoletti A (2013) Prediction of laser hardening by means of neural network. Procedia CIRP 12:181–18610.1016/j.procir.2013.09.032 ↗
40. Ghaisari J, Jannesari H, Vatani M (2012) Artificial neural network predictors for mechanical properties of cold rolling products. Advances in Engineering Software 45(1):91–9910.1016/j.advengsoft.2011.09.016 ↗
41. T. Chen and C. Guestrin (2016) Xgboost: a scalable tree boosting system. arXiv:1603.0275410.1145/2939672.2939785 ↗
42. L. Ruisen, D. Songyi, W. Chen, C. Peng, T. Zuodong, Y. YanMei, and W. Shixiong (2018) Bagging of xgboost classifiers with random under-sampling and tomek link for noisy label-imbalanced data. In: IOP conference series: materials science and engineering, vol 428. IOP Publishing, pp 01200410.1088/1757-899X/428/1/012004 ↗
43. Nenchev B, Tao Q, Dong Z, Panwisawas C, Li H, Tao B, Dong H (2022) Evaluating data-driven algorithms for predicting mechanical properties with small datasets: a case study on gear steel hardenability. Int J Miner Metall Mater 29(4):836–84710.1007/s12613-022-2437-0 ↗
44. Chen J, Zhao F, Sun Y, Zhang L, Yin Y (2019) Prediction model based on xgboost for mechanical properties of steel materials. International Journal of Modelling, Identification and Control 33(4):322–33010.1504/IJMIC.2019.107482 ↗
45. Schwenk M, Fisk M, Cedell T, Hoffmeister J, Schulze V, Lindgren L-E (2012) Process simulation of single and dual frequency induction surface hardening considering magnetic nonlinearity. Materials Performance and Characterization 1(1):1–2010.1520/MPC104374 ↗
46. Schwenk M, Hoffmeister J, Schulze V (2014) Experimentally validated residual stresses and distortion prediction for dual frequency induction hardening. International Journal of Applied Electromagnetics and Mechanics 44(2):127–13510.3233/JAE-141752 ↗
47. Yun D, Park H, Koo J-H, Ham S, Lee S (2015) Investigation of heat treatment of gears using a simultaneous dual frequency induction heating method. IEEE Transactions on Magnetics 51(11):1–410.1109/TMAG.2015.2436906 ↗
48. Iso E (2018) 6507–1: 2018-metallic materials-vickers hardness test-part 1: test method. Geneva, Switzerland, ISO
49. Tamayo D, Silburt A, Valencia D, Menou K, Ali-Dib M, Petrovich C, Huang CX, Rein H, Van Laerhoven C, Paradise A et al (2016) A machine learns to predict the stability of tightly packed planetary systems. The Astrophysical Journal Letters 832(2):L2210.3847/2041-8205/832/2/L22 ↗
50. Möller A, Ruhlmann-Kleider V, Leloup C, Neveu J, Palanque-Delabrouille N, Rich J, Carlberg R, Lidman C, Pritchet C (2016) Photometric classification of type ia supernovae in the supernova legacy survey with supervised learning. Journal of Cosmology and Astroparticle Physics 2016(12):00810.1088/1475-7516/2016/12/008 ↗
51. F. Giannakas, C. Troussas, A. Krouska, C. Sgouropoulou, and I. Voyiatzis (2021) Xgboost and deep neural network comparison: The case of teams’ performance. In: International conference on intelligent tutoring systems, Springer, pp 343–34910.1007/978-3-030-80421-3_37 ↗
52. Zamani JM, Cao C, Ni X, Bashir B, Talebiesfandarani S (2019) Pm2. 5 prediction based on random forest, xgboost, and deep learning using multisource remote sensing data. Atmosphere 10(7):37310.3390/atmos10070373 ↗
53. T. S. Jaakkola and D. Haussler (1999) Probabilistic kernel regression models. In: Seventh international workshop on artificial intelligence and statistics, PMLR