Data-driven prediction of ultimate tensile strength in low alloy steel: A machine learning approach
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OpenAlex topics: Machine Learning in Materials Science · Microstructure and Mechanical Properties of Steels · Hydrogen embrittlement and corrosion behaviors in metals
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References (53)
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1. High-strength, low-alloy steels10.1126/science.208.4446.862 ↗
2. Development of carbon—Low alloy steel grades for low temperature applications10.1016/j.msea.2011.04.071 ↗
3. Development of fracture toughness of ultrahigh strength low alloy steels for aircraft and aerospace applications10.1179/mst.1991.7.6.481 ↗
4. Effect of alloying elements on mechanical properties of high-strength low-alloy steel, in10.4028/www.scientific.net/MSF.1007.41 ↗
5. Experimental investigation, modelling and optimization in hard turning of high strength low alloy steel (AISI 4340)10.1051/mattech/2018054 ↗
6. On the influence of alloying elements on the bainite reaction in low alloy steels during continuous cooling10.1023/A:1004865209116 ↗
7. Effect of Si on mechanical properties of low alloy steels10.1179/026708399101506238 ↗
8. The effect of increasing Cu and Ni on a significant enhancement of mechanical properties of high strength low alloy, low carbon steels of HSLA-100 type10.1016/j.msea.2019.01.025 ↗
9. Effects of Cr and Ni on the microstructure and corrosion resistance of high-strength low alloy steel10.1016/j.jmrt.2022.12.191 ↗
10. Microalloyed steels through history until 2018: Review of chemical composition, processing and hydrogen service10.3390/met8050351 ↗
11. Effects of alloyed Cr and Cu on the corrosion behavior of low-alloy steel in a simulated groundwater solution10.1016/j.corsci.2015.09.025 ↗
12. Effect of Nb on the hydrogen-induced cracking of high-strength low-alloy steel10.1016/j.corsci.2018.04.041 ↗
13. Machine learning of mechanical properties of steels10.1007/s11431-020-1599-5 ↗
14. Online prediction of mechanical properties of hot rolled steel plate using machine learning10.1016/j.matdes.2020.109201 ↗
15. Application of Machine Learning to predict the mechanical properties of high strength steel at elevated temperatures based on the chemical composition, in10.1016/j.istruc.2023.03.085 ↗
16. Physical metallurgy-guided machine learning and artificial intelligent design of ultrahigh-strength stainless steel10.1016/j.actamat.2019.08.033 ↗
17. A predicting model for properties of steel using the industrial big data based on machine learning10.1016/j.commatsci.2018.12.056 ↗
18. Accelerating materials property predictions using machine learning10.1038/srep02810 ↗
19. A critical examination of robustness and generalizability of machine learning prediction of materials properties10.1038/s41524-023-01012-9 ↗
20. Topological representations of crystalline compounds for the machine-learning prediction of materials properties10.1038/s41524-021-00493-w ↗
21. Machine learning in materials informatics: recent applications and prospects10.1038/s41524-017-0056-5 ↗
22. Machine learning reinforced microstructure-sensitive prediction of material property closures10.1016/j.commatsci.2021.110930 ↗
23. Machine learning algorithms-a review10.21275/ART20203995 ↗
24. Machine learning algorithms: a review
25. Machine learning: Algorithms, real-world applications and research directions10.1007/s42979-021-00592-x ↗
26. Materials discovery and design using machine learning10.1016/j.jmat.2017.08.002 ↗
27. Big data creates new opportunities for materials research: a review on methods and applications of machine learning for materials design10.1016/j.eng.2019.02.011 ↗
28. Machine learning in materials design and discovery: examples from the present and suggestions for the future
29. Application of machine learning for advanced material prediction and design10.1002/eom2.12194 ↗
30. A review of performance prediction based on machine learning in materials science10.3390/nano12172957 ↗
31. Machine learning prediction of mechanical properties in metal additive manufacturing
32. Artificial intelligence in predicting mechanical properties of composite materials10.3390/jcs7090364 ↗
33. Yield strength prediction of high-entropy alloys using machine learning
34. Tensile property prediction by feature engineering guided machine learning in reduced activation ferritic/martensitic steels10.1016/j.jnucmat.2019.151823 ↗
35. Machine learning-driven predictive modeling of mechanical properties in diverse steels
36. Prediction of mechanical properties for deep drawing steel by deep learning10.1007/s12613-022-2547-8 ↗
37. Effect of dataset size and train/test split ratios in QSAR/QSPR multiclass classification10.3390/molecules26041111 ↗
38. Investigating the impact of train/test split ratio on the performance of pre-trained models with custom datasets
39. Pearson correlation coefficient
40. Pearson’s correlation coefficient
41. A survey of forecast error measures
42. A coefficient of determination for generalized linear models10.1080/00031305.2016.1256839 ↗
43. Random forests and adaptive nearest neighbors10.1198/016214505000001230 ↗
44. Random forests10.1023/A:1010933404324 ↗
45. Machine learning prediction of materials properties from chemical composition: Status and prospects10.1063/5.0235541 ↗
46. Mppredictor: an artificial intelligence-driven web tool for composition-based material property prediction10.1021/acs.jcim.3c00307 ↗
47. Relationships among microstructure, precipitation and mechanical properties in different depths of Ti–Mo low carbon low alloy steel plate10.1016/j.msea.2015.10.055 ↗
48. Effect of quenching and tempering on structure and mechanical properties of a low-alloy 0.25 C steel10.1016/j.msea.2023.144757 ↗
49. Effect of rapid heat treatment on mechanical properties and precipitation behavior of medium carbon low alloy steel10.1016/j.jmrt.2025.02.206 ↗
50. Effects of niobium and heat treatment on microstructure and mechanical properties of low carbon cast steels10.1016/j.matdes.2016.05.057 ↗
51. Effects of heat treatment process and niobium addition on the microstructure and mechanical properties of low carbon steel weld metal10.1007/s10853-010-4205-1 ↗
52. Effect of carbon content on microstructure and mechanical properties of hot-rolled low carbon 12Cr–Ni stainless steel10.1016/j.msea.2010.08.023 ↗
53. Effect of carbon content on microstructure and mechanical properties of dual phase steels, Procedia