Austenitic stainless steels are widely used in a large number of fields such as energy, petroleum chemistry and biological engineering, due to their good plasticity, nonmagnetic property, biological interchangeability and anticorrosion. Now, austenitic stainless steels account for 70% of the production and application of stainless steels. The recent increase in demand for nickel chrome austenitic stainless steel is depleting nickel resources year by year.

Many researchers made efforts to use cheap elements instead of nickel. Addition of nitrogen element to austenitic stainless steels can raise the strength of austenitic stainless steels and stabilize the austenitic structure.

The Cr17Mn17Mo3NiN steel is a typical low nickel austenitic stainless steel where Mn, Mo and N are used instead of Ni. The Cr17Mn17Mo3NiN steel has improved strength, plasticity and local corrosion resistance.

Accurate prediction of the flow stress of Cr17Mn17Mo3NiN low nickel austenitic stainless steel in hot working processes is of primary importance in economizing the material for new austenitic stainless steel, designing rational hot working processes such as hot rolling and forging, and increasing the processing efficiency.

Jang Kun Song, a student at the Faculty of Material Science and Technology, proposed phenomenological and physical-based constitutive models for the Cr17Mn17Mo3NiN low nickel stainless steel, based on the true stress-strain curves acquired from isothermal uniaxial compression tests, and estimated and compared the prediction accuracy of the proposed constitutive models.

As a result, the flow stresses predicted by the newly modified Johnson-Cook model and the microstructural-based constitutive model were in good consistent with the experimental results. Especially, the prediction accuracy of the microstructural-based constitutive model was the highest.

For more information, you can refer to his paper “Comparison of phenomenological and physical-based constitutive material models for Cr17Mn17Mo3NiN low nickel stainless steel” in “Materials Research Express” (SCI).

The 3rd generation high-strength Al-Li alloy, AA2099 alloy has high specific strength and hardness, strong corrosion resistance, favorable service characteristics at ultra-low temperatures and good damage tolerance. Especially, its fine-grained compact fiber structure which does not have any internal defects provides high thermal creep strength and good surface quality after hot working. These advantages make AA2099 alloys an alternative to the traditional high-strength aluminum alloys in aerospace fields.

Like other high-strength Al-Li alloys, AA2099 alloys have such weak plasticity at room temperature that the cold plastic working methods are not applicable to making machine parts or workpieces which require complicated or large deformation. Therefore, hot working is the main choice. Accurate prediction of the flow stress of AA2099 alloys in hot working process is of primary importance in economizing Al-Li alloys widely used in aerospace industry, designing rational hot working processes such as hot rolling and forging, and increasing the processing efficiency.

During hot working process, the plastic deformation exerted on the material causes changes in the microstructure and mechanical properties, which are shown as the change in flow stress. Therefore, the development of constitutive material models is of major theoretical and practical significance in predicting the flow stress at different deformation temperatures, strain rates and strains to provide important theoretical basis for the selection of hot working equipment and engineering design.

The constitutive models developed so far include phenomenological models, physical models and ANN models. Phenomenological constitutive models include Johnson-Cook (J-C), Arrhenius (AH) models, etc. which are simple to develop, but have low prediction accuracy. In contrast, physical models such as microstructure-based models (MS-B) have very high prediction accuracy, but they are complex to express and difficult to develop. On the other hand, ANN constitutive models require a large number of experimental data to train the neural network, so it costs a lot to develop them. In a word, each model has its own advantages and disadvantages.

Han Yun Hyok, a student at the Faculty of Material Science and Technology, has developed phenomenological models (modified J-C and strain compensated Arrhenius (SC-AH) model) and a physical model (MS-B model) for predicting the hot flow stress behavior in AA2099 alloy, and identified a constitutive model that can predict the flow stress of this alloy most accurately by comparing their predictability.

He estimated the prediction accuracy by means of the average absolute relative error and correlation coefficient. The accuracy was 5.09% and 0.992 8 for the modified Johnson-Cook model, 6.12% and 0.9820 for the strain compensated Arrhenius model, and, 1.58% and 0.9994 for the microstructure-based model.

The accuracy of the proposed MS-B model was the highest with 1.58% and 0.9994 and the flow stress predicted by the proposed microstructure-based model was in good consistent with the experimental results. This indicates that the proposed microstructure-based model can accurately describe the hot deformation behavior of AA2099 Al-Li alloy.

You can find the details in his paper “Constitutive Analysis on Hot Deformation Behavior of AA2099 Alloy for Modeling of Flow Stress” in “Mechanics of Solids” (SCI).

Studies on infectious viruses have been conducted over a long period of time to understand their transmission and characteristics of variation, to gain a lot of knowledge such as testing methods and treatment measures, and to develop various vaccines.

The world’s danger of contagion still continues to spread, and the reality implies that viruses need to be more deeply studied.

One of the problems is the prediction of virus transmission by mathematical modeling. This research based on mathematical modeling has been widely conducted in the world, running up to thousands of pieces.

Mathematical modeling makes it possible to predict future transmission situations and to predict the number of medical personnel, beds, medical equipment, medical drug supplies, etc. that hospitals need to prepare. It can also simulate and find a rational approach for health care.

Kim Ju Hyong, a researcher at the Faculty of Biomedical Engineering, proposed a genetic algorithm method to determine the unknown parameters involved in the mathematical model that describes the transmission of virus infection, one of the global optimization calculation methods.

To test the validity of this method, he built a SEIHRD model and compared the accuracy of the simulation with the determination results of unknown parameters when the underlying data were prepared in various forms.

For further details, please refer to his paper “A Method to Determine the Unknown Parameters of Mathematical Model of Epidemic Transmission by Genetic Algorithm” in “Proceedings of KUTIC-2025”.