Compared to monochannel membranes, multichannel membranes have the advantage of being able to generate higher permeate flux per unit volume of membrane elements by providing higher mechanical strength and larger filtration area within a given volume. However, the permeability of multichannel membranes is not directly proportional to the filtration area because the contributions of the middle and central channels and wall channels to the permeate flux are not the same. Since the permeated flow through the inner channels is not drained well towards the outer surface, proper placement of exit channels can increase permeate flux.

Through previous studies, it is found that permeate flux is mainly controlled by the permeability ratio of skin layer to porous support, and the geometry, and that proper arrangement of exit channels can increase permeate flux.

Kim Un Ok, a researcher at the Faculty of Applied Mathematics, has investigated the effect of exit channel placement on permeate flow in square 64-channel ceramic membranes which are now in wide use, and determined proper placement for various cases.

She assumed permeate flow to be two-dimensional potential flow and solved the mathematical model by finite element method.

The numerical simulation results show that the placement of exit channels affects permeate flux and it is possible to determine reasonable placement of exit channels according to the permeability ratio of skin layer to porous support.

For more information, you can refer to her paper “Effect of Exit Channel Placement on Permeate Flow in Square 64-Channel Ceramic Membrane” in “Proceedings of KUTIC-2025”.

Several computational methods have been applied to the research to develop new materials. For example, according to the considered scale, they are divided into finite element methods, Monte Carlo methods, molecular dynamics methods, etc. In addition, the experimental data analysis methods include experimental design, neural network, genetic algorithm, etc., which require high-performance computing devices, long computational time and a considerable amount of experimental analysis.

In contrast, grey models are widely used in data analysis as they can improve prediction accuracy from a small amount of poor data. Grey models are mainly applied in various fields such as energy consumption and prediction and CO₂ gas emission, attracting a great deal of interest of many researchers. This has led to active research into grey models but few of the results have been applied to the study of material properties.

Pang Chol Ho, a researcher at the Faculty of Material Science and Technology, proposed a hybrid exponential smoothing method, developed a grey model combined with a structural adaptive discrete grey Bernoulli model, and predicted some properties of material.

The comparison analysis with other grey models showed that the proposed model has the highest predictive accuracy.

He used the proposed model to predict various properties of material. The results showed that the predictive accuracy (MAPE) was 0.007 65 for tensile strength, 0.016 52 for Branell hardness and 0.025 15 for the thermoelectric performance parameters of high-entropy alloy AgSnSbSe_1.5. This means that the proposed model is effective for predicting material properties.

You can find more information in his paper “Application of Hybrid Grey Bernoulli Model in Material Research” in “Proceedings of KUTIC-2025”.

Selective laser melting (SLM) is the most popular AM technique that enables production of complicated metal products with high precision, flexibility, acceptable surface finish and material efficiency by melting metal powders using laser energy.

Since SLM process parameters affect the multiple quality attributes of SLM parts, it is very important to perform optimization and effect assessment of SLM process parameters. AlSi10Mg alloy is widely used in the industrial fields such as automotive and aerospace industries, and it is suitable for SLM processes due to its good castability, good strength, lower thermal expansion coefficient, and excellent wear and corrosion resistance.

Yang Ji Yon, a post-graduate student at the Faculty of Material Science and Technology, performed an analysis of the relationship between the properties (tensile strength, hardness and relative density) and process parameters (laser power LP, scan speed SS, and overlap rate OR) of SLM-manufactured AlSi10Mg alloy using Taguchi and TOPSIS methods.

For the individual properties, the optimal process parameters were LP 320-360W, SS 600mm/s and OR 35%, and the effect rankings varied according to the properties. For the TOPSIS-based overall quality index, the optimal process parameters were LP 320W, SS 600mm/s and OR 35%, and the effect ranking was SS, OR and LP.

For further details, you can refer to her paper “Relationship between Properties and Process Parameters for AlSi10Mg Alloy Manufactured by Selective Laser Melting” in “Proceedings of KUTIC-2025”.