엔트로피 지표(섀넌 엔트로피, 지니계수, 허시만-허핀달지수)와 경제복잡성 (ECI) 지표, 네트워크 분석을 활용하여 국내 반도체 산업의 구조를 실증적으로 분석하였다. 엔트로피 지표를 산업의 계열화/전문화와 연계하고 ECI와 엔트로피 지표의 특성을 상호보완 적으로 활용하는 새로운 접근을 시도하였다. 전국 단위로 반도체 산업 관련 업종에 해당하는 기업을 추출하여 기업 홈페이지 및 소개자료 등을 통해 반도체 산업 가치사슬 중 어느 부분에 해당하는지를 분류하는 작업을 실시하였으며, 최종적으로 2,957개 기업을 후공정, 전공정, 소재, 부품, 장비, 설계 기업으로 분류하여 특정하였다. 이 중 중 거래관계를 추출 가능한 1,212개 기업의 66,210개 거래관 계망 정보를 활용하여 실증 연구를 진행하였다. 국내 반도체 산업의 거래 네트워크는 무척도성, 작은세상 특성, 모듈성, 계층성을 모두 보이는 소수 핵심기업 중심 계열화된 네트워크였다. 국내 반도체 산업의 두 가지 구조변화 (2020년 거래관계망 계열화, 2023~2024년 복잡성지수 추세 반전)등을 식별할 수 있었다. 2018~2024기간 동안 국내 반도체 산업은 점차 전문화되는 추세였으나 2020년에 일시적 으로 계열화 추세를 보였다. 소·부·장 산업은 비교적 전문화되어 있고 전공정·설계는 계열 화되어 있었다. 광역지자체별로 살펴보면 경기·충남·충북 등 전통적 반도체 산업 중심 지의 계열화 수준이 높았으나, 경제복잡성 지수는 경남·부산에서 높게 나타났다. 경남·부산· 광주 등의 경제 복잡성 지수의 약진, 경기·충남·충북의 경제 복잡성 지수 부진은 2023~2024년에 두드러지게 나타났으며, 지역별 기업 영업이익률과도 양의 상관관계를 나타냈다. 2023년 이후 AI반도체, 전력반도체 육성정책과의 관련성에 대한 심층적인 분석이 필요하다. 최종적으로 비수도권 균형발전 관점 반도체 혁신정책의 효율성을 일정 부분 입증할 수 있었다.

High-entropy alloys (HEAs) are alloys that contain multiple principal elements, each in the range of 5–35%. HEAs exhibit excellent properties, however, even with conventional trial-and-error, high-throughput experimentation, and computational materials approaches, exploring their vast compositional space remains highly challenging. Accordingly, data-driven machine learning and generative-model-based inverse design methods are increasingly essential. In this study, we propose a generative-model-enabled HEA inverse design framework aimed at improving ultimate tensile strength (UTS). We first compiled 501 HEA data points from published literature and performed statistical analyses to understand their characteristics. Next, we tuned the hyperparameters of XGBoost and random forest (RF) models via Bayesian optimization, compared their performance with that of a deep neural network (DNN), and selected XGBoost as the optimal predictive model. In the subsequent stage, we trained a PyTorch-based variational autoencoder (VAE) on data from regions of the latent space associated with high-UTS probability. We randomly sampled 1,000 latent vectors, decoded them to generate candidate alloy compositions, and evaluated these candidates using the optimized XGB model. Finally, Shapley additive explanations (SHAP) interpretability analysis and a network plot were used to quantify the contributions and interactions of each feature variable, thereby assessing the physical plausibility of the model-suggested compositions.

This study examined process–structure relationships in laser powder bed fusion of Al0.1CoCrFeNi + Cu composites, focusing on densification, elemental distribution, and solidification cracking. Mechanically mixed Al0.1CoCrFeNi and Cu powders were processed across a range of laser powers (100–250 W) and scan speeds (200–800 mm/s). Increased volumetric energy density (VED) improved densification, with a plateau near 200 J/mm3 yielding ~96% relative density; however, this value was still below application-grade thresholds. At low VED, insufficient thermal input and short melt pool residence times promoted Cu segregation, while higher VED facilitated improved elemental mixing. Elemental mapping showed partial co-segregation of Ni with Cu at low energies. Solidification cracks were observed across all processing conditions. In high VED regimes, cracking exhibited a minimal correlation with segregation behavior and was primarily attributed to steep thermal gradients, solidification shrinkage, and residual stress accumulation. In contrast, at low VED, pronounced Cu segregation appeared to exacerbate cracking through localized thermal and mechanical mismatch.

High-entropy alloys (HEAs) exhibit complex phase formation behavior, challenging conventional predictive methods. This study presents a machine learning (ML) framework for phase prediction in HEAs, using a curated dataset of 648 experimentally characterized compositions and features derived from thermodynamic and electronic descriptors. Three classifiers—random forest, gradient boosting, and CatBoost—were trained and validated through cross-validation and testing. Gradient boosting achieved the highest accuracy, and valence electron concentration (VEC), atomic size mismatch (δ), and enthalpy of mixing (ΔHmix) were identified as the most influential features. The model predictions were experimentally verified using a non-equiatomic Al30Cu17.5Fe17.5Cr17.5Mn17.5 alloy and the equiatomic Cantor alloy (CoCrFeMnNi), both of which showed strong agreement with predicted phase structures. The results demonstrate that combining physically informed feature engineering with ML enables accurate and generalizable phase prediction, supporting accelerated HEA design.

High-entropy alloys (HEAs) incorporating low-melting-point elements (Mg and Al) and high-melting-point elements (Ti, Cr, and V) were fabricated via mechanical alloying and spark plasma sintering. Sintering temperatures were varied to investigate phase behavior and microstructural evolution. X-ray diffraction was used to identify phase structures, scanning electron microscopy to analyze microstructures, X-ray fluorescence to determine elemental composition, and a gas pycnometer to measure density. Micro-Vickers hardness testing was conducted to evaluate mechanical properties. Mechanical-alloyed HEAs exhibited a body-centered cubic (BCC) phase and lamellar structures with element-enriched regions. Sintering introduced additional BCC and Laves phases, while higher temperatures promoted Mg liquid-phase sintering, increasing density and hardness. This study highlights the effects of sintering on HEAs containing elements with differing melting points to optimize their properties.

In recent years, high-entropy alloys (HEAs) have attracted considerable attention in materials engineering due to their unique phase stability and mechanical properties compared to conventional alloys. Since the inception of HEAs, CoCrFeMnNi alloys have been widely investigated due to their outstanding strength and fracture toughness at cryogenic temperatures. However, their lower yield strength at room temperature limits their structural applications. The mechanical properties of HEAs are greatly influenced by their processing methods and microstructural features. Unlike traditional melting techniques, powder metallurgy (PM) provides a unique opportunity to produce HEAs with nanocrystalline structures and uniform compositions. The current review explores recent advances in optimizing the microstructural characteristics in CoCrFeMnNi HEAs by using PM techniques to improve mechanical performance. The most promising strategies include grain refinement, dispersion strengthening, and the development of heterogeneous microstructures (e.g., harmonic, bimodal, and multi-metal lamellar structures). Thermomechanical treatments along with additive manufacturing techniques are also summarized. Additionally, the review addresses current challenges and suggests future research directions for designing advanced HEAs through PM techniques.

Additive manufacturing makes it possible to improve the mechanical properties of alloys through segregation engineering of specific alloying elements into the dislocation cell structure. In this study, we investigated the mechanical and microstructural characteristics of CoNi-based medium-entropy alloys (MEAs), including the refractory alloying element Mo with a large atomic radius, manufactured via laser-powder bed fusion (L-PBF). In an analysis of the printability depending on the processing parameters, we achieved a high compressive yield strength up to 653 MPa in L-PBF for (CoNi)85Mo15 MEAs. However, severe residual stress remained at high-angle grain boundaries, and a brittle μ phase was precipitated at Mo-segregated dislocation cells. These resulted in hot-cracking behaviors in (CoNi)85Mo15 MEAs during L-PBF. These findings highlight the need for further research to adjust the Mo content and processing techniques to mitigate cracking behaviors in L-PBF-manufactured (CoNi)85Mo15 MEAs.

High-entropy alloys (HEAs) represent a revolutionary class of materials characterized by their multi-principal element compositions and exceptional mechanical properties. Powder metallurgy, a versatile and cost-effective manufacturing process, offers significant advantages for the development of HEAs, including precise control over their composition, microstructure, and mechanical properties. This review explores innovative approaches integrating powder metallurgy techniques in the synthesis and optimization of HEAs. Key advances in powder production, sintering methods, and additive manufacturing are examined, highlighting their roles in improving the performance, advancement, and applicability of HEAs. The review also discusses the mechanical properties, potential industrial applications, and future trends in the field, providing a comprehensive overview of the current state and future prospects of HEA development using powder metallurgy.

We investigated the microstructure of an FeCrMnNiCo alloy fabricated by spark plasma sintering under different sintering temperatures (1000–1100°C) and times (1–600 s). All sintered alloys consisted of a single face-centered cubic phase. As the sintering time or temperature increased, the grains of the sintered alloys became partially coarse. The formation of Cr7C3 carbide occurred on the surface of the sintered alloys due to carbon diffusion from the graphite crucible. The depth of the layer containing Cr7C3 carbides increased to ~110 μm under severe sintering conditions (1100°C, 60 s). A molten zone was observed on the surface of the alloys sintered at higher temperatures (>1060°C) due to severe carbon diffusion that reduced the melting point of the alloy. The porosity of the sintered alloys decreased with increasing time at 1000°C, but increased at higher temperatures above 1060°C due to melting-induced porosity formation.

The pressure sensor had been widely used to effectively monitor the flow status of the water distribution system for ensuring the reliable water supply to urban residents for providing the prompt response to potential issues such as burst and leakage. This study aims to present a method for evaluating the performance of pressure sensors in an existing water distribution system using transient data from a field pipeline system. The water distribution system in Y District, D Metropolitan City, was selected for this research. The pressure data was collected using low-accuracy pressure sensors, capturing two types of data: daily data with 1Hz and high-frequency recording data (200 Hz) according to specific transient events. The analysis of these data was grounded in the information theory, introducing entropy as a measure of the information content within the signal. This method makes it possible to evaluate the performance of pressure sensors, including identifying the most sensitive point from daily data and determining the possible errors in data collected from designated pressure sensors.

High-entropy alloys (HEAs) have been reported to have better properties than conventional materials; however, they are more expensive due to the high cost of their main components. Therefore, research is needed to reduce manufacturing costs. In this study, CoCrFeMnNi HEAs were prepared using metal injection molding (MIM), which is a powder metallurgy process that involves less material waste than machining process. Although the MIM-processed samples were in the face-centered cubic (FCC) phase, porosity remained after sintering at 1200°C, 1250°C, and 1275°C. In this study, the hot isostatic pressing (HIP) process, which considers both temperature (1150°C) and pressure (150 MPa), was adopted to improve the quality of the MIM samples. Although the hardness of the HIP-treated samples decreased slightly and the Mn composition was significantly reduced, the process effectively eliminated many pores that remained after the 1275°C MIM process. The HIP process can improve the quality of the alloy.

The emergence of ferrous-medium entropy alloys (FeMEAs) with excellent tensile properties represents a potential direction for designing alloys based on metastable engineering. In this study, an FeMEA is successfully fabricated using laser powder bed fusion (LPBF), a metal additive manufacturing technology. Tensile tests are conducted on the LPBF-processed FeMEA at room temperature and cryogenic temperatures (77 K). At 77 K, the LPBF-processed FeMEA exhibits high yield strength and excellent ultimate tensile strength through active deformation-induced martensitic transformation. Furthermore, due to the low stability of the face-centered cubic (FCC) phase of the LPBFprocessed FeMEA based on nano-scale solute heterogeneity, stress-induced martensitic transformation occurs, accompanied by the appearance of a yield point phenomenon during cryogenic tensile deformation. This study elucidates the origin of the yield point phenomenon and deformation behavior of the FeMEA at 77 K.

High-entropy alloys (HEAs) are characterized by having five or more main elements and forming simple solids without forming intermetallic compounds, owing to the high entropy effect. HEAs with these characteristics are being researched as structural materials for extreme environments. Conventional refractory alloys have excellent hightemperature strength and stability; however, problems occur when they are used extensively in a high-temperature environment, leading to reduced fatigue properties due to oxidation or a limited service life. In contrast, refractory entropy alloys, which provide refractory properties to entropy alloys, can address these issues and improve the hightemperature stability of the alloy through phase control when designed based on existing refractory alloy elements. Refractory high-entropy alloys require sufficient milling time while in the process of mechanical alloying because of the brittleness of the added elements. Consequently, the high-energy milling process must be optimized because of the possibility of contamination of the alloyed powder during prolonged milling. In this study, we investigated the hightemperature oxidation behavior of refractory high-entropy alloys while optimizing the milling time.

This research examines the effect of adding aluminum on the structural, phasic, and magnetic properties of CoCrFe NiMnAlx high-entropy alloys. To this aim, the arc-melt process was used under an argon atmosphere for preparing cast samples. The phasic, structural, and magnetic properties of the samples were characterized by x-ray diffraction (XRD), scanning electron microscopy (SEM), and vibrational magnetometry (VSM) analyses. Based on the results, the addition of aluminum to the compound caused changes in the crystalline structure, from FCC solid solution in the CoCrFeNiMn sample to CoCrFeNiMnAl BBC solid solution. It was associated with changes in the magnetic property of CoCrFeNiMnAlx high-entropy alloys, from paramagnetic to ferromagnetic. The maximum saturation magnetization for the CoCrFeNiMnAl casting sample was estimated to be around 79 emu/g. Despite the phase stability of the FCC solid solution with temperature, the solid solution phase formed in the CrCrFeNiMnAl high-entropy compound was not stable, and changed into FCC solid solution with temperature elevation, causing a reduction in saturation magnetization to about 7 emu/g.

High-entropy alloys (HEAs) are attracting attention because of their excellent properties and functions; however, they are relatively expensive compared with commercial alloys. Therefore, various efforts have been made to reduce the cost of raw materials. In this study, MIM is attempted using coarse equiatomic CoCrFeMnNi HEA powders. The mixing ratio (powder:binder) for HEA feedstock preparation is explored using torque rheometer. The block-shaped green parts are fabricated through a metal injection molding process using feedstock. The thermal debinding conditions are explored by thermogravimetric analysis, and solvent and thermal debinding are performed. It is densified under various sintering conditions considering the melting point of the HEA. The final product, which contains a small amount of non-FCC phase, is manufactured at a sintering temperature of 1250oC.