Development of Predictive Model for Yield Strength on Fe-based ODS (Oxide Dispersion Strengthened) Alloy

Abstract

Oxide dispersion strengthened (ODS) alloys have the highest usable temperature among metals except for refractory metals. It also has relatively excellent oxidation resistance compared to other high-temperature materials. In particular, the ODS alloy has improved high-temperature properties because fine oxide particles, which are stable at high-temperature, are uniformly dispersed in the metal matrix. These ODS alloys has been continuously improved in properties until recently due to the development of manufacturing technology. Among various ODS alloys, Fe-based ODS alloys have greatly increased in related research due to the need for materials with excellent neutron irradiation resistance than conventional steels during the development of high-speed breeder reactor started in the 1990s. In the 2000s, as the development of 4th generation nuclear reactors and future nuclear fusion reactors, such as International Thermonuclear Experimental Reactor(ITER), centered on the United states, Japan, and the EU began in earnest. These researches, Fe-based ODS alloy, which has excellent high-temperature corrosion resistance, neutron irradiation resistance and creep properties was selected as the structural materials and fuel rod materials. For this reason, studies on Fe-based ODS alloys have been intensively conducted in the 2000s. However, despite these excellent high-temperature properties, the ODS alloy takes a long time to manufacture the specimen due to the complex manufacturing process and various process variables. Therefore, it take a long time to evaluate the properties of the alloy. Accordingly, some studies have been conducted on a predictive model that predicts the strength of an alloy using the results of microstructure analysis in order to solve the disadvantage of consuming a long time until property evaluation. However, the error between the measured value and the predicted value is significant, and for this reason, it is difficult to apply the existing model as it is. The largest error in this predictive model is identified in the grain boundary strengthening model. ODS alloy has very fine oxides that cannot be found in conventional alloys. These fine oxides not only improve the properties of the alloy, but also inhibit grain growth. As a result of this inhibition of grain growth, fine grains are formed, which improves the properties of the ODS alloy. However, until now, there is no grain boundary strengthening model that reflecting this grain growth suppression effect, and no relevant research has been conducted. In this study, Fe-based ODS alloy powder was prepared through a powder metallurgical process, and related studies were conducted using this alloy powder. The alloy powder was prepared using a planetary mill, and each element powder was used. In addition, the microstructure according to mechanical alloying time was analyzed to derive the optimum mechanical alloying condition and to confirm the decomposition of Y2O3 during mechanical alloying process. The decomposition of Y2O3 during mechanical alloying process was confirmed by calculating the formation enthalpy according to the reactants and using the difference in the reaction products according to the reactants. Through the analysis of mechanical alloying powder, the optimum condition powder was fabricated as a sintered specimen at a temperature of 1150℃ by applying spark plasma sintering. The fabricated sintered specimen was subjected to phase analysis using X-ray diffractometer. In addition, in order to confirm the growth behavior of grain and oxide particles according to the heat treatment time, heat treatment was performed from 10 h to 1000 h at a temperature of 680℃ as a time variable. The heat treated specimens were subjected to EBSD analysis to measure the grain size, and the change in grain size over time was expressed as a growth equation using the results. In addition, the change in the size of oxide particles over time was carried out through analysis using TEM and ImageTool program, and based on the results, the change in particle growth over time was expressed as a growth equation. A new grain boundary strengthening model was derived by analyzing the correlation between the calculated grain growth equation and the particle growth equation, and applying the result to Hall-Petch equation, a grain boundary strengthening model. A new predictive model for yield strength at room temperature was developed by applying the strengthening model of matrix and strengthening model of ODS to the new strengthening model of grain boundary thus derived. To confirm the reliability of the developed predictive model, the measured yield strength and calculated values were compared using data from other research papers. In addition, the correlation between yield strength and hardness was analyzed to derive a predictive model for yield strength according to temperature, and the reliability of the model was confirmed.