Invited Speakers

Invited Speakers

Prof. Seung-Kyun Kang
Seoul National University, Korea

Biography: Prof. Seung-Kyun Kang is an Associate Professor in the Department of Materials Science and Engineering at Seoul National University (SNU), a position he has held since 2021. He joined SNU in 2019 as an Assistant Professor. In addition to his academic role, he serves as the Head of the Material Analysis Center at the Research Institute of Advanced Materials (RIAM) at SNU. Prof. Kang earned his B.S. in Materials Science and Engineering from Seoul National University in 2006, graduating with honors and completing his studies in just six semesters. He went on to obtain his Ph.D. in Materials Science and Engineering from the same institution in 2012, focusing on the mechanical properties evaluation of multiscale materials. Following his Ph.D., Prof. Kang pursued postdoctoral research under the mentorship of Professor John A. Rogers, specializing in advanced bioelectronics and materials for biomedical applications. He conducted his research at the University of Illinois at Urbana-Champaign from 2012 to 2016 and at Northwestern University from 2016 to 2017. From 2017 to 2019, he was an Assistant Professor in the Department of Bio and Brain Engineering at KAIST. He serves on the Executive Advisory Board for Advanced Sensor Research and the Editorial Board of the Journal of Functional Biomaterials. He is also an Associate Editor for the Journal of the Korean Ceramic Society, playing a pivotal role in advancing research in materials science and bioengineering. Prof. Kang has authored over 80 peer-reviewed research papers, with notable publications in Nature, Nature Medicine, Nature Electronics, Science Advances, and Advanced Materials. His multidisciplinary research spans materials science, electronics, mechanics, and bioengineering, with a particular focus on applications in wearable, implantable, and bioresorbable medical devices, neuromorphic devices, and soft robotics.

Speech title "Advances in Biodegradable Metal Materials for Flexible and Stretchable Electronics"

Abstract-Biodegradable electronics have garnered significant attention as a core technology for future sustainable solutions, ranging from minimally invasive medical devices to eco-friendly zero-waste electronics. At the heart of biodegradable electronics lies the development of biodegradable metallic materials, which serve as the foundation for achieving electrical conductivity. Recent advances in this field highlight the critical role of metallic materials, including alloy design for controlled biodegradation and the engineering of amorphous metal electrodes for flexible and stretchable devices. This presentation explores various technical aspects of biodegradable metallic materials, such as their oxidation behavior, mechanical property control, and alloy design strategies. Additionally, the potential of flexible and stretchable devices based on these materials is demonstrated through innovative strain sensor technology. By reinterpreting traditional crack mechanics, we introduce a novel approach to utilizing metal cracks as highly sensitive strain sensors. This comprehensive overview underscores the importance of biodegradable metallic materials in advancing sustainable electronics and their diverse applications.

Dr. Jong-hyoung Kim
Pukyong National University, Korea

Biography: Prof. Jong-hyoung Kim is an Assistant Professor in the Department of Materials Science and Engineering at Pukyong National University (PKNU), a position he has held since September 2024. Prof. Kim earned his B.S. and Ph.D. in Materials Science and Engineering from Seoul National University in 2012 and 2019, respectively. His doctoral research focused on evaluating mechanical properties and structural integrity of metallic materials, particularly using instrumented indentation technique. Following his Ph.D., from 2019 to 2024, Prof. Kim conducted postdoctoral research at Harvard University John A. Paulson School of Engineering and Applied Sciences under Prof. Joost J. Vlassak, specializing in small-scale mechanical testing and thin film mechanics. Prof. Kim has been active in the ISO (International Organization for Standardization) TC164 SC3 Hardness Testing subcommittee since 2017 and remains a committee member. Prof. Kim has significantly contributed to establishing more than four standards/codes in ISO, ASME BPV Code, and GB standards. His research output includes over 20 peer-reviewed papers. Prof. Kim’s multidisciplinary research spans materials science, mechanics, and semiconductors, with a particular focus on developing local mechanical characterization technique at multi-scale using artificial neural networks. He also conducts further analysis for the reliability of materials and components especially considering environmental effect based on fracture mechanics with experimental measurement data.

Speech title "Evaluation of Tensile Properties from Indentation Parameters using an artificial neural network-based model"

Abstract-The instrumented indentation technique is a widely adopted method for assessing mechanical properties such as hardness, elastic modulus, and yield strength. Its ability to continuously measure penetration depth and applied force makes it particularly valuable for in-situ mechanical testing, especially at the nanoscale, enabling multi-purpose evaluations. However, challenges persist in accurately determining material properties due to phenomena such as plastic pileup and sink-in effects, which complicate the calibration of the contact depth—a critical factor for reliable material property assessments using indentation. In our study, we leverage machine learning (ML) to predict tensile properties, including elastic modulus and yield strength, directly from indentation parameters. To address the limitation of insufficient experimental data for model training, we generated a comprehensive dataset through finite element analysis (FEA) simulations. Recognizing the influence of geometric imperfections in indenter tips on indentation responses, we integrate transfer learning and data normalization techniques. These strategies enhance the robustness of the model, improving prediction accuracy for the targeted mechanical properties.