Prediction of the yield strength of a secondary-hardening steel

By Jian-Sheng Wang, M. D. Mulholland, Gregory B. Olson, David N. Seidman. 2013

168 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Mechanical Engineering, Metals and Alloys

View Paper: https://www.semanticscholar.org/paper/b44554f91ec5e1e1b8efae7344819987410650a7

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Biomedical Engineering

1. Wei Xu
2. S. van der Zwaag

In Mechanical Engineering

1. Wei Xu
2. S. van der Zwaag
3. Chenchong Wang
4. Xiaolu Wei
5. Yong Li
6. Chunguang Shen

Citations in the corpus

• Physical metallurgy-guided machine learning and artificial intelligent design of ultrahigh-strength stainless steel (2019, methodology): “Therefore, the yield strength can be well predicted considering the overall contribution of the aforementioned mechanisms and hence be used to guide the design and optimization of alloy composition and heat treatment conditions [9,12,22].”