News

Our paper “Process–defect–property relationships in LPBF-fabricated AlSi10Mg thin-walled honeycomb structures under compression” has been published in Journal of Materials Research and Technology (IF 7.3, JCR Top 9.8%, Metallurgy & Metallurgical Engineering). · “Process–defect–property relationships in LPBF-fabricated AlSi10Mg thin-walled honeycomb structures under compression” 논문이 Journal of Materials Research and Technology (IF 7.3, JCR 상위 9.8%, Metallurgy & Metallurgical Engineering 분야)에 게재되었습니다.

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Prof. Kang-Hyun Lee awarded an Individual Basic Research Program (R&D) grant from the Ministry of Science and ICT as PI: “Foundation Model for Reconstruction and Inverse Design of Material Microstructures via Generative AI-Based Dimensionality Expansion.” 50,000,000 won per year. · 이강현 교수, 개인기초연구(과기정통부)(R&D) 연구책임자로 “생성형 AI 기반 차원 확장을 통한 재료 미세구조 재구성·역설계 파운데이션 모델” 연구과제 수주. 연 50,000,000 won.

Our paper “A unified physics-based framework for vibration prediction and design optimization of functionally graded trapezoidal thin plates” accepted for publication in Composite Structures (IF 7.8, JCR Top 4.7%, Materials Science, Composites), with Prof. Kang-Hyun Lee as corresponding author. · “A unified physics-based framework for vibration prediction and design optimization of functionally graded trapezoidal thin plates” 논문이 Composite Structures (IF 7.8, JCR 상위 4.7%, Materials Science, Composites 분야)에 게재 승인됨. 이강현 교수 교신저자.

Our paper “Aeroplastic design of turbine disks under transient thermal loads” accepted for publication in Aerospace Science and Technology (IF 6.4, JCR Top 11.9%, Engineering, Aerospace) — an MIT–Lehigh collaboration. · “Aeroplastic design of turbine disks under transient thermal loads” 논문이 Aerospace Science and Technology (IF 6.4, JCR 상위 11.9%, Engineering, Aerospace 분야)에 게재 승인됨. MIT–Lehigh 공동 연구.

Attended the ASME International Design Engineering Technical Conferences (IDETC-CIE 2026) in Houston, TX, August 23–26, 2026, and presented our work on generative AI–based microstructure reconstruction and design. Also reconnected with postdoctoral advisor Prof. Faez Ahmed (MIT) and got together with members and alumni of the DeCoDE Lab. · 2026년 8월 23–26일 미국 휴스턴에서 열린 ASME IDETC-CIE 2026에 참가하여 생성형 AI 기반 미세조직 복원 및 설계 연구를 발표. 박사후연구원 지도교수인 Faez Ahmed 교수(MIT) 및 DeCoDE Lab 구성원·동문들과 재회.

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Gave an invited talk at the 2026 Annual Spring Conference of the Korean Society of Manufacturing Technology Engineers (KSMTE): “Multiphysics modeling and inverse design of process–structure–property linkages in metal additive manufacturing.” July 15, 2026. · 한국생산제조학회(KSMTE) 2026 춘계학술대회에서 “금속적층제조 공정-구조-물성 연계 다물리 모델링 및 역설계” 주제로 초청 강연 진행. 2026년 7월 15일.

Prof. Kang-Hyun Lee featured in Soongsil University news for securing an MIT-led international research project on generative AI for aerospace materials and components design. · 이강현 교수, 미국 MIT 주관 ‘항공우주 소재·부품 설계 생성형 AI 개발’ 연구과제 수주 — 숭실대 뉴스 보도.

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Our paper “Modelling the hierarchical microstructure of L-PBF Ti–6Al–4V: a multiphysics framework for texture-induced mechanical anisotropy” published in Virtual and Physical Prototyping. · “Modelling the hierarchical microstructure of L-PBF Ti–6Al–4V: a multiphysics framework for texture-induced mechanical anisotropy” 논문이 Virtual and Physical Prototyping에 게재됨.

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Prof. Kang-Hyun Lee, Co-PI on the Advanced Manufacturing Convergence Talent Development Program: “Physics-Guided Generative AI Framework for Multi-Objective, Multi-Constraint Optimal Design of Defense Components.” 2 years, 97,000,000 won total. · Prof. Kang-Hyun Lee, 첨단제조융합인재육성지원 사업 Co-PI: “Physics-guided 생성형 AI 기반 다목적·다중제약 방산 부품 최적설계 프레임워크 개발.” 2년, 총 97,000,000 won.

Prof. Kang-Hyun Lee, Participating Researcher on “Development of a Full-Lifecycle Materials Data Hub for Ultra-High-Temperature Heat-Resistant Ceramics for Space Launch Vehicles, Based on Fast and Reliable Computation–Experiment Integration.” 30,000,000 won. · Prof. Kang-Hyun Lee — “우주발사체용 초고온 내열 세라믹 소재를 위한 고속·고신뢰성 계산-실험 융합 기반 전주기 소재 데이터 HUB 개발 및 실증 연구” — 30,000,000 won.

Mechanical Engineering Seminar, Seoul National University (Dept. of Mechanical Engineering), hosted by Prof. Howon Lee: “Multiphysics Modeling of Process–Structure–Property Linkages and Inverse Design for Metal Additive Manufacturing.” June 1, 2026 (Mon) 14:00–15:00. · 서울대학교 기계공학부 세미나: “Multiphysics Modeling of Process–Structure–Property Linkages and Inverse Design for Metal Additive Manufacturing.” 2026년 6월 1일 (월) 14:00–15:00.

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Prof. Kang-Hyun Lee, Research Affiliate, Department of Mechanical Engineering, Massachusetts Institute of Technology (MIT) — effective June 1, 2026. · Prof. Kang-Hyun Lee, 매사추세츠공과대학교(MIT) 기계공학과 Research Affiliate — 2026년 6월 1일부.

BK21 FOUR Special Seminar Series, Seoul National University (Dept. of Aerospace Engineering): “Generative AI-Based Design Optimization of Next-Generation Aerospace Materials and Components.” May 27, 2026 (Wed) 16:00. · 서울대학교 항공우주공학과 BK21 FOUR 전문가 초청 세미나: “Generative AI-Based Design Optimization of Next-Generation Aerospace Materials and Components.” 2026년 5월 27일 (수) 16:00.

Seminar poster (PDF)

Gave an invited talk at the 2026 Spring Conference of the CAE and Applied Mechanics Division, The Korean Society of Mechanical Engineers (KSME): “Latent Diffusion Model-Based 2D-to-3D Microstructure Reconstruction and Inverse Design.” Sono Calm Yeosu, May 6–9, 2026. · 대한기계학회 CAE 및 응용역학부문 2026년 춘계학술대회에서 “Latent Diffusion Model 기반 2차원-3차원 미세조직 재구성 및 역설계” 주제로 초청 발표 진행. 소노캄 여수, 2026년 5월 6–9일.

IDMM Lab officially launched at the School of Mechanical Engineering, Soongsil University. We are actively recruiting graduate students and undergraduate researchers — see Join Us.

Prof. Kang-Hyun Lee joined the School of Mechanical Engineering at Soongsil University as Assistant Professor, after a postdoctoral appointment at MIT.

Gave an invited talk at the Materials Research Society (MRS) Fall Meeting: “Multiobjective Inverse Design of Compositionally Graded Blisks for Reusable Turbomachinery Using Physics-Guided Diffusion Models.” The work is part of the DARPA METALS program — a collaborative effort on generative AI–driven design frameworks for additive manufacturing of functionally graded materials (FGMs). · MRS Fall Meeting에서 “Multiobjective Inverse Design of Compositionally Graded Blisks for Reusable Turbomachinery Using Physics-Guided Diffusion Models” 주제로 초청 발표 진행. DARPA METALS 프로그램의 일환으로 기능성 경사 소재(FGM) 적층제조용 생성형 AI 기반 설계 프레임워크를 다룸.

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