A data-driven machine learning approach to predict the hardenability curve of boron steels and assist alloy design
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OpenAlex topics: Microstructure and Mechanical Properties of Steels · Hydrogen embrittlement and corrosion behaviors in metals · Metal Alloys Wear and Properties
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References (39)
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1. Canale LCF, Albano L, Totten GE, Meekisho L (2014) Hardenability of steel. In: Hashmi S (ed) Comprehensive materials processing. Elsevier, pp 39–9710.1016/B978-0-08-096532-1.01219-X ↗
2. Fu DZ, Lin HG, Zhou RJ, Yu YL (1973) Hardenability hand-book of steels. China Machine Press, Beijing
3. Anggraini Lydia (2017) Hardenability of ASSAB 760 steel during tempering for punch holder applications. Solid State Phenom 266:13–1810.4028/www.scientific.net/SSP.266.13 ↗
4. Kamada Y, Kurayasu H, AbES W (2010) Relation between hardenability and segregation to austenite grain boundaries of boron atom on direct quenching process. Tetsu Hagane 74(11):2153–216010.2355/tetsutohagane1955.74.11_2153 ↗
5. Biaobrzeska B (2021) Effect of alloying additives and microadditives on hardenability increase caused by action of boron. Metals 11(4):58910.3390/met11040589 ↗
6. Sharma M, Ortlepp I, Bleck W (2019) Boron in heat-treatable steels: a review. Steel Res Int 90(11):190013310.1002/srin.201900133 ↗
7. Field J (1943) Calculation of jominy end- queching curves from analysis. Metal Prog 43:402–405
8. Grossman MA (1942) Hardenability calculated from chemical composition. Trans AIME 150:227–255
9. LH Boyd, J Field (1946). Contributions to the metallurgy of steel, N. 12, American Iron and Steel Institute, New York
10. Just E (1969) New formulas for calculating hardenability curves. Metal Prog 96(5):87–88
11. Yu BH (1985) Nonlinear equations for calculating hardenability and mechanical property. Iron Steel 20(3):40–49
12. Kirkaldy JS, Venugopolan D (1983) Phase transformations in ferrous alloys. AIME, New York
13. Saunders N, Guo U, Li X, Miodownik AP, Schillé JP (2003) Using JMatPro to model materials properties and behavior. JOM 55(12):60–6510.1007/s11837-003-0013-2 ↗
14. Chaudhary N, Abu-Odeh A, Karaman I, Arróyave R (2017) A data-driven machine learning approach to predicting stacking faulting energy in austenitic steels. J Mater Sci 52(18):11048–11076. https://doi.org/10.1007/s10853-017-1252-x10.1007/s10853-017-1252-x ↗
15. Geng XX, Wang H, Ullah A, Xue WH, Xiang S, Meng L, Ma G (2020) Prediction of continuous cooling transformation diagrams for Ni–Cr–Mo welding steels via machine learning approaches. JOM 72(9):3926–393410.1007/s11837-020-04057-z ↗
16. Yan F, Song K, Liu Y, Chen S, Chen J (2020) Predictions and mechanism analyses of the fatigue strength of steel based on machine learning. J Mater Sci 55(31):15334–15349. https://doi.org/10.1007/s10853-020-05091-710.1007/s10853-020-05091-7 ↗
17. Swetlana S, Khatavkar N, Singh AK (2020) Development of Vickers hardness prediction models via microstructural analysis and machine learning. J Mater Sci 55(33):15845–15856. https://doi.org/10.1007/s10853-020-05153-w10.1007/s10853-020-05153-w ↗
18. Geng XX, Mao XP, Wu HH, Wang SZ, Xue WH, Zhang GZ, Ullah A (2022) A hybrid machine learning model for predicting continuous cooling transformation diagrams in welding heat-affected zone of low alloy steels. J Mater Sci Technol 107:207–21510.1016/j.jmst.2021.07.038 ↗
19. Ra Haman M, Mu W, Odqvist J, Hedström P (2019) Machine learning to predict the martensite start temperature in steels. Metall Mater Trans A 50:2081–209110.1007/s11661-019-05170-8 ↗
20. Jiang X, Jia BR, Zhang GF, Zhang C, Wang X, Zhang RJ, Yin HQ, Qu XH, Song Y, Su L, Mi ZL, Hu L, Ma H (2020) A strategy combining machine learning and multiscale calculation to predict tensile strength for pearlitic steel wires with industrial data. Scr Mater 186:272–27710.1016/j.scriptamat.2020.03.064 ↗
21. Gao XH, Deng TY, Wang HR, Qiu CL, Qi KM, Zhou P (2010) Prediction of hardenability of gear steel using stepwise polynomial regression and artificial neural network. Adv Mat Res 118:332–335
22. Dong G, Li X, Zhao J, Su S, Misrs RDK, Xiao R, Shang C (2020) Machine learning guided methods in building chemical composition-hardenability model for wear-resistant steel. Mater Today Commun. 24:10133210.1016/j.mtcomm.2020.101332 ↗
23. Dehghani K, Shafiei A (2008) Predicting the bake hardenability of steels using neural network modeling. Mater Lett 62(2):173–17810.1016/j.matlet.2007.04.114 ↗
24. Chokshi P, Dashwood R, Hughes DJ (2017) Artificial neural network (ANN) based microstructural prediction model for 22MnB5 boron steel during tailored hot stamping. Comput Struct 190:162–17210.1016/j.compstruc.2017.05.015 ↗
25. Liu XQ (2011) Hardenability hand-book of steels. Chemical Industry Press, Beijing
26. Hu ZZ (2011) Handbook of steel and heat treatment curves. National Defense Industry Press, Beijing
27. Sun ZB, Zhu PF, Lin HG, Yu TS (1992) Metallurgical Industry Press, Beijing
28. Cai J, Luo JW, Wang SL, Sheng Y (2018) Feature selection in machine learning: a new perspective. Neurocomputing 300:70–7910.1016/j.neucom.2017.11.077 ↗
29. Sokolowski PK, Lindsley BA (2010) Influence of chemical composition and austenitizing temperature on hardenability of pm steels. Int J Powder Metall 46(1):43–54
30. Karjalainen LP, Somani MC, Porter DA (2003) Regression and solute drag models for the activation energy of static recrystallisation in hot-worked steels. Materials Science Forum. Trans Tech Publ Ltd 426:1181–1188
31. Xiong J, Zhang TY, Shi SQ (2020) Machine learning of mechanical properties of steels. Sci China Technol Sci 63:1247–125510.1007/s11431-020-1599-5 ↗
32. Murtagh F (1991) Multilayer perceptrons for classification and regression. Neurocomputing 2(5–6):183–19710.1016/0925-2312(91)90023-5 ↗
33. Shevade SK, Keerthi SS, Bhattacharyya C, Murthy KRK (2000) Improvements to the SMO algorithm for SVM regression. IEEE Trans Neural Netw 11:188–118810.1109/72.870050 ↗
34. Chelgani SC, Matin SS, Makaremi S (2016) Modeling of free swelling index based on variable importance measurements of parent coal properties by random forest method. Measurement 94:416–42210.1016/j.measurement.2016.07.070 ↗
35. Xiong Z, Cui YX, Liu ZH, Zhao Y, Hu M, Hu JJ (2020) Evaluating explorative prediction power of machine learning algorithms for materials discovery using k-fold forward cross-validation. Comp Mater Sci 171:10920310.1016/j.commatsci.2019.109203 ↗
36. Geng XX, Wang H, Xue WH, Xiang S, Huang HL, Meng L, Ma G (2020) Modeling of CCT diagrams for tool steels using different machine learning techniques. Comput Mater Sci 171:10923510.1016/j.commatsci.2019.109235 ↗
37. Sokolova M, Lapalme G (2009) A systematic analysis of performance measures for classification tasks. Inf Process Manage 45:427–43710.1016/j.ipm.2009.03.002 ↗
38. Jian Z, Read B, Pullen S, Qian S (2012) Achieving carbon neutrality in commercial building developments-Perceptions of the construction industry. Habitat Int 36(2):278–28610.1016/j.habitatint.2011.10.010 ↗
39. Taylor T, Fourlaris G, Clough A (2017) Effect of carbon and microalloy additions on hot-stamped boron steel. Mater Sci Technol 33(16):1964–197710.1080/02670836.2017.1342018 ↗