"Suchasna Elektrometallurgiya" (Electrometallurgy Today), 2026, #3, 42-51 pages
Influence of statistical analysis methods on prediction of the microstructure and properties of high-strength low-alloy steels
V.A. Kostin
E.O. Paton Electric Welding Institute of the NAS of Ukraine
11 Kazymyr Malevych Str., 03150, Kyiv, Ukraine.
E-mail: valerykostinepwi@gmail.com
Abstract
The paper presents complex statistical analysis of the properties of high-strength low-alloy steels (HSLA, HSA, SSHA, ASSHA)
with application of a wide data base. The modern methods of data analysis, including correlation analysis, principal
component analysis (PCA), as well as machine learning (Random Forest, XGBoost) and SHAP values and interpreted matrices
were used to study the interrelation between the chemical composition, microstructure and mechanical properties of steels. Key
elements and parameters, having the maximal impact on strength, ductility and thermal resistance were determined for each
group of steels. Optimal ranges of C, Nb, V, Ni content, as well as heat treatment modes, including austenitization temperature
and cooling rate were substantiated. The results demonstrate that complex control of the composition and heat treatment allows
developing steels with an excellent balance of service characteristic. Practical recommendations section presents specific tips
as to metallurgical design of new steels, including the use of machine learning models for acceleration of development and
optimization of steels. The work also discusses the research gaps, such as the need to standardize the data, study complex interaction
of alloying elements, phase transformation dynamics, and prospects for implementation of AI methods. 12 Ref., 8 Fig.
Keywords: high-strength low-alloy steels, statistical, correlation, cluster analysis, machine learning, microstructure, prediction
of properties
Received: 02.10.2025
Received in revised form: 27.01.2026
Accepted: 14.07.2026
Posted online: 24.07.2026
References
1. Rashid, M.S. (1980) High-strength, low-alloy steels. Science,
208, 862–869. DOI: https://doi.org/10.1126/science.208.4446.862
2. Kuziak, R., Kawalla, R., Waengler, S. (2008) Advanced high
strength steels for automotive industry. Archives of Civil and
Mechanical Engineering, 8(2), 103–117. DOI: https://doi.org/10.1016/S1644-9665(12)60197-6
3. Muhammed, M., Mustapha, M. et al. (2020) Statistical review
of microstructure-property correlation of stainless steel: Implication
for pre- and post-weld treatment. Processes, 8(7),
811. DOI: https://doi.org/10.3390/pr8070811
4. Siwar Chibani, François-Xavier Coudert (2020) Machine
learning approaches for the prediction of materials properties.
APL Materials, 8(8), 080701. DOI: https://doi.org/10.1063/5.0018384
5. Chunyuan Cui, Guangming Cao (2023) A strategy combining
machine learning and physical metallurgical principles to
predict mechanical properties for hot rolled Ti micro-alloyed
steels, J. of Materials Processing Technology, 311, 117810.
DOI: https://doi.org/10.1016/j.jmatprotec.2022.117810
6. Agrawal, A., Choudhary, A. (2016) Perspective: Materials informatics
and big data: Realizing the promise of data-driven
materials science. APL Materials, 4(5), 053208. DOI: https://doi.org/10.1063/1.4946894
7. Harwarth, M, Brauer, A. et al. (2021) Influence of carbon on
the microstructure evolution and hardness of Fe–13Cr–xC
(x = 0–0.7 wt.%) stainless steel. Materials, 14(17), 5063.
DOI: https://doi.org/10.3390/ma14175063
8. Bhadeshia, H.K.D.H., Honeycombe, R.W.K. (2006) Steels:
Structure, properties, and design. 3th Ed. Butterworth-Heine
mann (Ed.). DOI: https://doi.org/10.1016/B978-0-7506-8084-4.X5000-6
9. Li, G.Q., Shen, Y.F. et al. (2022) Microstructural evolution
and mechanical properties of a micro-alloyed low-density
δ-TRIP steel. Materials Science and Engineering: A, 848,
143430. DOI: https://doi.org/10.1016/j.msea.2022.143430
10. Di Schino, A., Testani, C. (2021) Heat treatment of steels. Metals,
11(8), 1168. DOI: https://doi.org/10.3390/met11081168
11. Xueyun, G., Haiyan, W., Huijie, T. rt al. (2023) Data-driven
machine learning for alloy research: Recent applications
and prospects. Materials Today Communications, 36, 106697.
DOI: https://doi.org/10.1016/j.mtcomm.2023.106697
12. Chen, J, Shi, Z, Luo, X et al. (2025) Micro-alloying effects
on microstructure and weldability of high-strength low-alloy
steel: A Review. Materials, 18(5), 1036. DOI: https://doi.org/10.3390/ma18051036
This article is licensed under a
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Suggested Citation
V.A. Kostin (2026) Influence of statistical analysis methods on prediction of the microstructure and properties of high-strength low-alloy steels.
Electrometallurgy Today, 03, 42-51.