Keynote Speakers

Keynote Speakers

Keynote Speakers
Prof. Byeng D. Youn
Seoul National University, Korea

Optimization-Based Statistical Model Validation (OSMV) for Digital Twin Models

Biography

Biography

Prof. Byeng Dong YOUN is the Professor of Mechanical Engineering Department at Seoul National University (SNU), the founder and CEO of OnePredict Inc. (onepredict.com), the Fellow of the PHM Society, and the President of the Korean Society of PHM. Before joining SNU, he used to be the Assistant Professor in the Department of Mechanical Engineering at the University of Maryland, College Park. He is currently the Reliability Advisory Committee Chair, Hyundai Motors and the Future-Tech Advisory Committee Member of LG Electronics. He earned Ph.D. from the University of Iowa in 2001. His current research includes prognostics and health management (PHM), engineering design under uncertainty, and energy harvester design. His dedication and efforts in research and development have garnered substantive peer recognition resulting in many notable awards including the Prime Minister Award (2019), the Research Award from the Korean Society of Design Optimization (2019), the Shin Yang Academic Award from Seoul National University (2017), the 9-time winner of Global PHM Data Challenges including PHM Society Data Challenge Winners (2014, 2015, 2017), the IEEE PHM Competition Winner (2014), the Young Faculty Development Award from the U.S. Nuclear Regulatory Commission (2009), the ASME IDETC Best Paper Awards (2001, 2008), the ISSMO/Springer Prize for a Young Scientist (2005), the Silver Medal of Samsung HumanTech (2002), etc. He has about 400 publications (over 120 journal articles, one textbook (Springer), over 300 international conference proceedings, and four book chapter) in the area of PHM, reliability analysis and design, and energy harvesting. He also serves as an Editor of many notable journals including International Journal of Precision Engineering and Manufacturing (IJPEM), Structural and Multidisciplinary Optimization (SMO), Journal of Mechanical Science and Technology (JMST), and JMST Advances. His research has been supported by the National Research Foundation (NRF) in Korea, Korea Electric Power Corporation (KEPCO), Samsung Electronics, U.S. Army, Hyundai Motors, LG Electronics, General Motors, and so on.

Abstract

Digital twin, a replicate of a real physical system in a cyber space, has become a key technology to support engineering decisions in analysis, design, and management. Compared with conventional computer-aided engineering (CAE) models, a digital twin model is characterized with proactive control and management of a real system in response to the changes in environments (i.e., temperature) and operations (i.e., degradation). These characteristics are namely ‘adaptation’ and ‘evolution’, respectively. The prerequisite of the characteristics is the validation of a digital twin model under the uncertainty in environmental and operational conditions. This talk covers the framework of optimization-based statistical model validation (OSMV) comprehensively, aiming to improve the prediction accuracy of digital twin models. This study considers three classes of the digital twin models as: (a) whitebox model (or physics-based model), 2) blackbox model (or data-driven model), and 3) greybox model (physics+data-driven model). Some case studies will be presented to demonstrate the effectiveness of the proposed OSMV framework.


Prof. Akihiro Takezawa
Hiroshima University, Japan

Lattice distribution optimizations for additive manufactured functional structures

Biography

Biography

Akihiro Takezawa earned his Bachelor of Engineering Degree in Precision Engineering from Kyoto University in 2003 and his Master셲 Degree in Precision Engineering from Kyoto University in 2005. He also received his Doctor of Philosophy Degree in Precision Engineering from Kyoto University in 2009. He is now an Associate Professor in the Graduate School of Engineering at Hiroshima University.

Abstract

A “lattice structure,” which is a geometry with inner air holes, is a characteristic geometry realized using additive manufacturing (AM). An advantage of the AM lattice structure is variable lattice shape and performance depending on the location like functionally graded material. Since the original concept of topology optimization (TO) is a volume fraction distribution optimization of composites comprising a material and air, basic TO algorithms can be easily applied to lattice density distribution optimization. This key note lecture will discuss the overview of the basic lattice distribution optimization methodology and its recent applications for designing functional structures including the speaker셲 works, optimization of liquid cooling structure and minimization of thermal distortion of metal AM process.


Prof. Il Yong Kim
Queens University, Canada

DfAM (Design for Additive Manufacturing): Current and Future

Biography

Biography

Dr. Il Yong Kim is a Professor in the Department of Mechanical and Materials Engineering at Queen셲 University, Kingston, Canada. His research interest is design and topology optimization with applications in automotive and aerospace systems, along with DfAM (Design for Additive Manufacturing). KIM received a number of awards, including the Early Researcher Award in Canada, the Experienced Humboldt Fellowship in Germany, the Research Excellence Award at Queen셲, and many paper awards at major scientific conferences. KIM is actively collaborating with global, multi-national companies in the automotive and aerospace industries, including General Motors, General Dynamics, Magna, Bombardier Aerospace, Pratt & Whitney, and Safran Landing Systems.

Abstract

Additive manufacturing (AM) has significantly more design freedom than traditional manufacturing methods, providing opportunities for better performance and lower weight. However, there are a number of challenges in the use of AM, such as time, cost, support structures, interior void regions, and parts division (or consolidation).

The keynote presentation will show the state-of-the-art in DfAM (Design for Additive Manufacturing) based on advanced multi-disciplinary topology optimization, and discuss future direction. Practical DfAM projects in the industry will be presented.


Prof. Ji-hong ZHU
Northwestern Polytechnical University, China

Structural Optimization Achieving Shape Preserving Design

Biography

Biography

ZHU Ji-Hong got the Doctor셲 degree in LTAS, Universit챕 de Li챔ge, Belgium, in 2008. He became professor in Northwestern Polytechnical University in 2012, Visiting Professor of Queen Mary University of London in 2017. He is also the editor of Structural and Multidisciplinary Optimization and Chinese Journal of Aeronautics.

His research subjects are optimization design and additive manufacturing for aeronautical and aerospace structural systems. He has published more than 70 Journal papers and 2 books. He has gained the Chinese State Natural Science Award, Science and Technology Award of Shaanxi Province, French CADLM Young Researcher Award for Intelligent Optimization, CJK-OSM Young Scientist Award, Young Scholar Award for Computational Mechanics of China and Young Scholar Award of Chinese Society of Aeronautics and Astronautics etc.

Abstract

Local warping deformation is one of the key factors leading to structural damages and functional failures.
The purpose of this work is to demonstrate a shape preserving topology optimization design approach to suppress the warping deformation of structural local domains. As structural deformation consists of rigid body motion and warping deformation, we propose using the elastic strain energy of the local domain to measure the warping deformation. Constraint on the local strain energy is then introduced into a standard compliance based topology optimization to obtain the shape preserving effect. Moreover, in the cases of shape preserving for multiple key points and cavities, i.e. when the local strain energies are unavailable, the idea of Artificial Weak Element (AWE) is introduced to measure and constrain the local warping deformation.

Then the shape preserving optimization is extended to arrive at the desired deformation behavior within local structural domains by distinguishing and suppressing specific deformation in a certain direction. Compared to the standard shape preserving design, particular orthotropic material properties are defined for the AWE according to the given direction of the desired deformation, which formulate Orthotropic Artificial Weak Elements (AWEort). By introducing the constraint on the strain energy of the AWEort into a standard compliance based topology optimization, the deformation in the corresponding direction is suppressed, resulting in the desired deformation behavior.

This work further extends the shape preserving design approach from linear load cases into geometrically nonlinear problems. Based on an integrated deformation energy function, an improved warpage formulation is proposed to measure the geometrical distortion during large deformations. Structural complementary elastic work is assigned as the objective function. The average distortion calculated as the integrated deformation energy accumulated in the incremental loading process is accordingly constrained to obtain warpage control. In the numerical implementation, an energy interpolation scheme is utilized to alleviate numerical instability in low stiffness regions.