Industrial steel property prediction is often constrained by limited labelled data, reducing the effectiveness of conventional machine learning models. This study investigated whether metallurgy-informed feature engineering enhances predictive performance under small-data conditions. A representative set of 300 samples...
S. Tiwari, H. Ahn, Jongwon Lee et al.· Metals· 0 citations
Three tree-based machine learning models were developed using 36 unique alloy grade–temperature observations from a validated NIMS 5Cr-0.5Mo tensile dataset, demonstrating the potential of interpretable, uncertainty-aware ML for small alloy datasets, while emphasizing the need for larger, compositionally diverse datase...
S. Tiwari, H. Ahn, Jongwon Lee et al.· Metals· 0 citations
Overall, these technologies show meaningful potential to shorten development time and improve treatment safety, although further prospective validation is required before realizing this potential at scale.
Priya Sharma, S. Tiwari, Nokeun Park et al.· International Journal of Mol...· 0 citations
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