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이병주 연구실
포항공과대학교 신소재공학과
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이병주 연구실

포항공과대학교 신소재공학과 이병주 교수

이병주 연구실은 재료 상변태와 상평형 열역학에 기반한 전산재료과학을 중심으로, CALPHAD·phase-field·원자단위 시뮬레이션 등을 활용해 합금의 미세조직과 물성을 예측하고, 고엔트로피·중엔트로피 합금, 내열강, 극저온 구조재료, 금속 적층제조용 소재를 설계하며, 최근에는 인공지능과 데이터 기반 기법을 접목해 소재 및 공정의 최적화와 역설계까지 확장하는 신소재 연구를 수행하고 있다.

대표 연구 분야
연구 영역 전체보기
재료 상변태 열역학과 전산모사 thumbnail
재료 상변태 열역학과 전산모사
연구 성과 추이
표시된 성과는 수집된 데이터 기준으로 산출되며, 일부 차이가 있을 수 있습니다.

5개년 연도별 논문 게재 수

102총합

5개년 연도별 피인용 수

2,195총합
주요 논문
3
논문 전체보기
1
article
|
hybrid
·
인용수 1
·
2025
Ultrahigh Strength with Suppressed Flow Instability at Liquid Helium Temperature via Coherent Nanoprecipitation in a Medium‐Entropy Alloy
Min Young Sung, Tae Jin Jang, Sang Yoon Song, Chang‐Yong Lee, Junho Lee, Young‐Kyun Kim, Sang‐Ho Oh, Byeong‐Joo Lee, Alireza Zargaran, Se‐Ho Kim, Young Sang Na, Seok Su Sohn
IF 19
Advanced Functional Materials
Abstract Metallic materials for aerospace and liquid hydrogen technologies need to maintain high strength and ductility under cryogenic conditions. However, conventional strengthening strategies typically increase defect density and promote strain localization, resulting in a strength–ductility trade‐off. This limitation becomes more critical at ultralow temperatures, where it facilitates discontinuous plastic flow and abrupt stress drops, substantially increasing the risk of premature failure. Here, a Co 36 Ni 46 Mo 11 Al 7 medium‐entropy is developed, exhibiting an exceptional combination of tensile strength (2.1 GPa), high ductility (48%), and remarkably low stress drops of ≈99 MPa at 4.2 K. This balance is enabled by two key mechanisms: enhanced lattice friction through compositional tuning and the introduction of coherent L1 2 nanoprecipitates. These features effectively impede dislocation motion while promoting Hirth lock formation, thereby suppressing strain localization. Crucially, cryogenic loading–unloading–reloading tests, rarely performed at 4.2 K, reveal low back stress, directly indicating minimal dislocation accumulation despite the high strength. The findings highlight how dislocation–precipitate interactions can decouple strength from back stress accumulation, enabling a rare combination of ultrahigh strength and suppressed discontinuous plastic flow. This approach establishes a robust alloy design strategy for overcoming the long‐standing conflict between strength, ductility, and mechanical stability in cryogenic environments.
https://doi.org/10.1002/adfm.202515593
Materials science
Alloy
Instability
Helium
Liquid helium
Thermodynamics
Composite material
Mechanics
Atomic physics
Physics
2
article
|
gold
·
인용수 27
·
2023
Material-agnostic machine learning approach enables high relative density in powder bed fusion products
Jaemin Wang, Sang Guk Jeong, Eun Seong Kim, Hyoung Seop Kim, Byeong‐Joo Lee
IF 15.7
Nature Communications
This study introduces a method that is applicable across various powder materials to predict process conditions that yield a product with a relative density greater than 98% by laser powder bed fusion. We develop an XGBoost model using a dataset comprising material properties of powder and process conditions, and its output, relative density, undergoes a transformation using a sigmoid function to increase accuracy. We deeply examine the relationships between input features and the target value using Shapley additive explanations. Experimental validation with stainless steel 316 L, AlSi10Mg, and Fe60Co15Ni15Cr10 medium entropy alloy powders verifies the method's reproducibility and transferability. This research contributes to laser powder bed fusion additive manufacturing by offering a universally applicable strategy to optimize process conditions.
https://doi.org/10.1038/s41467-023-42319-x
Fusion
Computer science
Materials science
Artificial intelligence
Nanotechnology
3
article
|
gold
·
인용수 66
·
2023
Doubled strength and ductility via maraging effect and dynamic precipitate transformation in ultrastrong medium-entropy alloy
Hyun Jung Chung, Won Seok Choi, Hosun Jun, Hyeon-Seok Do, Byeong‐Joo Lee, Pyuck‐Pa Choi, Heung Nam Han, Won‐Seok Ko, Seok Su Sohn
IF 15.7
Nature Communications
Demands for ultrahigh strength in structural materials have been steadily increasing in response to environmental issues. Maraging alloys offer a high tensile strength and fracture toughness through a reduction of lattice defects and formation of intermetallic precipitates. The semi-coherent precipitates are crucial for exhibiting ultrahigh strength; however, they still result in limited work hardening and uniform ductility. Here, we demonstrate a strategy involving deformable semi-coherent precipitates and their dynamic phase transformation based on a narrow stability gap between two kinds of ordered phases. In a model medium-entropy alloy, the matrix precipitate acts as a dislocation barrier and also dislocation glide media; the grain-boundary precipitate further contributes to a significant work-hardening via dynamic precipitate transformation into the type of matrix precipitate. This combination results in a twofold enhancement of strength and uniform ductility, thus suggesting a promising alloy design concept for enhanced mechanical properties in developing various ultrastrong metallic materials.
https://doi.org/10.1038/s41467-023-35863-z
Materials science
Intermetallic
Alloy
Ductility (Earth science)
Maraging steel
Toughness
Ultimate tensile strength
Strengthening mechanisms of materials
Grain boundary
Precipitation hardening
정부 과제
30
과제 전체보기
1
2024년 6월-2028년 12월
|1,425,000,000
인공지능기술 및 데이터를 활용한 우주항공용 초내열 비철 금속소재의 HUB구축
인공지능기술 및 데이터를 활용한 우주항공용 초내열 비철 금속소재의 HUB 구축
초내열합금
인공지능
데이터베이스
고온 기계적 물성
내산화성
2
2024년 6월-2028년 12월
|750,000,000
인공지능기술 및 데이터를 활용한 우주항공용 초내열 비철 금속소재의 HUB구축
인공지능기술 및 데이터를 활용한 우주항공용 초내열 비철 금속소재의 HUB 구축
초내열합금
인공지능
데이터베이스
고온 기계적 물성
내산화성
3
주관|
2022년 7월-2023년 7월
|10,000,000
2NNMEAM+Qeq interatomic potential을 활용한 Al-Al2O3 계 연구
• compile and use the new 2NNMEAM+Qeq potential in the Large-scale Atomic/Molecular Massively Parallel Simulator (LAMMPS) software • utilize the corresponding parameter calibration and optimization toolset for the 2NNMEAM+Qeq potential • to adjust the Al-O potential parameters using an extensive DFT database for amorphous alumina for a wide range of compositions and densities
원자단위전산모사
최신 특허
특허 전체보기
상태출원연도과제명출원번호상세정보
등록2021기상 합성으로 생성된 분말 크기 예측 방법1020210006651
등록2020유전 알고리즘을 활용한 머신러닝 역 예측 방법1020200054415
등록2019고엔트로피 합금을 이용한 클래드 및 그 제조방법1020190030076
전체 특허

기상 합성으로 생성된 분말 크기 예측 방법

상태
등록
출원연도
2021
출원번호
1020210006651

유전 알고리즘을 활용한 머신러닝 역 예측 방법

상태
등록
출원연도
2020
출원번호
1020200054415

고엔트로피 합금을 이용한 클래드 및 그 제조방법

상태
등록
출원연도
2019
출원번호
1020190030076