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The mechanical characteristics of steel are assessed through various tests that evaluate its strength, toughness, and flexibility. These tests include tension, torsion, impact, bending, and hardness assessments, each providing crucial information about steel's suitability for specific applications.
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Author Spotlight: Optimization of Processing Technology for Tiebangchui with Zanba Based on CRITIC Combined with Box-Behnken Response Surface Method
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Machine Learning-Based Multi-Objective Composition Optimization of High-Nitrogen Austenitic Stainless Steels.

Yinghu Wang1,2, Long Chen3, Limei Cheng2

  • 1National Center for Materials Service Safety, University of Science and Technology Beijing, Beijing 100083, China.

Materials (Basel, Switzerland)
|December 11, 2025
PubMed
Summary

This study introduces a novel workflow for designing high-nitrogen austenitic stainless steels (HNASS) by combining thermodynamic calculations and machine learning. The method optimizes steel composition for enhanced corrosion resistance and microstructural stability, suppressing undesirable phases.

Keywords:
high-nitrogen austenitic stainless steelmachine learningmulti-objective genetic optimizationthermodynamic calculation of phase diagrams

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Area of Science:

  • Materials Science
  • Metallurgy
  • Computational Materials Design

Background:

  • High-nitrogen austenitic stainless steels (HNASS) require careful compositional control to balance corrosion resistance and microstructural stability.
  • Suppression of deleterious phases like delta ferrite and precipitates (e.g., Cr2N, sigma phase, M23C6 carbides) is crucial for optimal performance.

Purpose of the Study:

  • To develop an explainable, multi-objective design workflow for HNASS.
  • To couple thermodynamic modeling with machine learning for predicting steel properties.
  • To identify optimal compositions that maximize corrosion resistance (PREN) and microstructural stability.

Main Methods:

  • Utilized Calculation of Phase Diagrams (CALPHAD) for thermodynamic descriptors (equilibrium and Scheil calculations).
  • Employed machine learning surrogate models (Random Forest, XGBoost) trained on extensive compositional data.
  • Integrated multi-objective optimization algorithms (NSGA-III, TOPSIS) with physics-informed features.

Main Results:

  • Random Forest model demonstrated high accuracy (PREN RMSE ≈0.004) and generalization.
  • Shapley Additive Explanations (SHAP) provided metallurgically consistent insights into elemental effects.
  • Pareto fronts were generated to minimize undesirable phases and maximize PREN, identifying optimal composition windows.

Conclusions:

  • The developed workflow is efficient, reproducible, and interpretable for data-driven stainless steel design.
  • Actionable composition candidates with improved PREN and controlled precipitation were identified.
  • The methodology offers a transferable approach for designing advanced stainless steels.