Modelling solid solution hardening in stainless steels |
| |
| Affiliation: | 1. Department of Materials Engineering, Indian Institute of Science, Bangalore 560012, India;2. International Advanced Research Center for Powder Metallurgy and New Materials, Hyderabad 500005, India;3. Materials Research Centre, Indian Institute of Science, Bangalore 560012, India;1. Graduate Institute of Ferrous Technology, POSTECH, 37673 Pohang, Republic of Korea;2. School of Materials Science and Engineering, Yeungnam University, 38541 Gyeongsan, Republic of Korea;3. Technical Research Laboratories, POSCO, 37859 Pohang, Republic of Korea;1. Center for Advanced Aerospace Materials, Pohang University of Science and Technology, Pohang 790-784, Republic of Korea;2. Graduate Institute of Ferrous Technology, Pohang University of Science and Technology, Pohang 790-784, Republic of Korea;3. Sheet Products & Process Research Group, Technical Research Laboratories, POSCO, Kwangyang 545-090, Republic of Korea;1. Collaborative Innovation Center of Steel Technology, University of Science and Technology Beijing, Beijing, 100083, China;2. Beijing Laboratory of Metallic Materials and Processing for Modern Transportation, University of Science and Technology Beijing, Beijing, 100083, China;1. Department of Ferrous Metallurgy, RWTH Aachen University, Intzestraße 1, 52072 Aachen, Germany;2. Chair of Solid-State and Quantum Chemistry, Institute of Inorganic Chemistry, RWTH Aachen University, Landoltweg 1, 52074 Aachen, Germany;3. JARA - High Performance Computing, RWTH Aachen University, Schinkelstraße 2, 52062 Aachen, Germany |
| |
| Abstract: | The solid solution hardening of stainless steels is studied by using the Labusch–Nabarro relation. Models are evaluated in order to predict the mechanical properties from chemical composition, solution hardening misfit parameters, grain size, ferrite content and product thickness. A data source of six grades of steels is used for the modelling. Both austenitic and duplex stainless steels are covered including more than 1100 batches, which are subjected to multiple regression analyses. The models are compared with earlier studies and can be used as tools in material optimisation. |
| |
| Keywords: | |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
