Tool steels are used for making tools to be employed for working and forming of metals, wood, plastics, and other industrial materials. They can be used for cutting tools, locators, clamps, and gauges.

Tool steel materials are put into various classifications such as high C-Cr die steel, water/air hardening tool steel, hot-work die steel, and shock-resistance tool steel. Different alloying elements such as tungsten, vanadium, molybdenum, manganese and chromium are added to modern tool steel materials to guarantee desired properties for their applications.

Each tool steel material has their own mechanical and physical properties suited for particular applications. For any specific application, several kinds of alternative tool steel material may be a choice, and it is essential to select the most suitable tool steel materials with desired properties.

Material selection is a multi-criteria decision making (MCDM) problem where materials engineers have to select the best material with the best performance among multiple available alternative materials in consideration of two or more attributes.

Many researchers have applied different MCDMs to select suitable tool steel materials for specific purposes. However, it is unknown which is better for given material selection problems.

Ma Chung Hyok, a section head at the Faculty of Material Science and Technology, proposed a best tool steel materials selection method combined with the results obtained from different MCDMs, and applied it to the selection of the best one among 10 tool steel materials in consideration of nine attributes using six popular MCDMs: rank sum ratio (RSR), simple additive weighted (SAW), technique for order preference by similarity to ideal solution (TOPSIS), grey relational analysis (GRA), VIse Kriterijumska Optimizacija Kompromisno Resenje (VIKOR), and preference ranking organization method for enrichment evaluations (PROMETHEE).

The proposed method based on the comprehensive ranks may be considered a suitable one because the result is determined in consideration of the results from individual MCDMs, and the comprehensive ranks are best coincided with the ranks from other MCDMs.

For more information, you can refer to his paper “Selection Method of Best Tool Steel Materials Using Multiple Multi-Criteria Decision Making Techniques” in “Russian Metallurgy” (SCOPUS).

Acoustic sediment classification methods are important for protecting and studying the ecological environment of rivers and seas. Single-beam echosounder, multi-beam echosounder and side-scan sonar are the main measuring means for sediment classification. Recently, neural networks have been applied to sediment classification to improve its accuracy.

Sediment classification using single-beam echosounders has the longest history, and many methods have been proposed for sediment classification using single-beam acoustic sounders.

Jong Kum Chol, a researcher at the Faculty of Naval Architecture and Ocean Engineering, proposed a new MLP-KNN model, a combination of recently widely-used MLP neural network and KNN model to improve sediment classification accuracy in shallow waters.

In shallow waters, the accuracy of sediment classification is reduced because echoes are overlapping. Hence, he first designed an MLP neural network where six feature parameters from the overlapping echo are taken as an input layer and the area of the overlapping part as an output layer, and estimated the area of the overlapping part. Then, based on the estimation results, he calculated E₁ and E₂ derived from the first and second echoes, respectively, and classified sediment using the KNN model.

The comparison results with the sediment classification method by BP neural network showed that the proposed method classifies sediment with higher accuracy in shallow waters.

For more information, please refer to his paper “Acoustic sediment classification using MLP-KNN model on single-beam echosounder data from shallow water” in “Marine Systems & Ocean Technology” (EI).

Traditionally, process planning and scheduling were executed sequentially. Sequential execution of process planning and scheduling has numerous shortcomings including unbalanced resource utilization and unrealistic process plans.

Recently, researchers have investigated various approaches to overcome the shortcoming of the sequential approach for process planning and scheduling. Although there are various meta-heuristics for IPPS, they still need further improvements using a more efficient algorithm. Moreover, there is a lack of research of effective algorithms for multi-objective IPPS.

Kim Yong Ho, an institute head at the Faculty of Automatics, proposed a new multi-objective differential evolutionary algorithm combined with chaotic map to solve IPPS problems.

The experimental results showed that the proposed algorithm is better than other multi-objective optimization algorithms for IPPS problems.

For further details, you can refer to his paper “A Chaotic-Based Multi-Objective Differential Evolutionary Algorithms for Integrated Process Planning and Scheduling” in “Proceedings of KUTIC-2025”.