Keynote Speakers

Abstract: Vibration and noise control have been realized by using phononic crystals and acoustic metamaterials. However, these methods have been always suffering from some fundamental limitations including narrow working bandwidth and large volume. How to realize broadband vibration and noise control in a small footprint has been a challenge. Recently, as a booming branch of metamaterials, a new kind of artificial structure named metasurface has provided feasible solutions. As a 2D mapping of metamaterial, metasurface has enabled extraordinary wavefront manipulation with compact and lightweight structure of sub-wavelength scale. In this work, we have proposed a series of ultrathin metasurfaces for extraordinary elastic-wave manipulations, including omnidirectional isolation, “one-way” propagation and highly-efficient routing. The present work extends the strategy for wave and vibration control in elastodynamics and acoustics.

Biography: Dr. Bing Li is a full professor in the School of Aeronautics at Northwestern Polytechnical University. His current research interests include dynamics of elastic/mechanical metamaterials/metasurfaces, wave mechanics, vibration and noise control, nondestructive testing. He has published >100 scientific papers in top-tier peer-reviewed journals and renowned international conferences such as Nature Communications, Compos. Sci. Tech., Compos. Part A, Compos. Part B, Phys. Rev. Appl., Phys. Rev. B, J. Sound Vib., etc., >20 issued or pending patents, >40 invited presentations. Dr. Li is an Associate Editor/Editor Board Member/Topical Advisory Panel Member for five scientific journals. He has received a series of awards and honors, including Distinguished Expert of Chinese “Oversea Young Talents Program” (2020), The Youth Talent Program of Northwestern Polytechnical University (2018), the Fellow of IAAM (2023), Science and Technology Award of Shaanxi Higher Education (First place, 2021, 2025), etc.

Abstract: 3D printing technology facilitates the manufacturing of fiber-reinforced thermoplastic composite components with complex geometries, providing exceptional design freedom that overcomes the shape limitations inherent in conventional manufacturing approaches. However, due to the transient, low-pressure nature of thermoplastic composite 3D printing, the fabricated parts frequently exhibit poor interlayer adhesion, high porosity, and substantial warpage deformation, resulting in significantly compromised mechanical performance relative to traditionally manufactured counterparts. To date, there exists a critical gap in composite structural design methodologies and processing techniques specifically adapted for 3D printing characteristics.
To overcome these challenges, this report systematically addresses three key aspects:
(1) This report details several innovative topology optimization methods for 3D-printed composite structures approaches: manufacturing-constrained topology optimization frameworks specifically developed for 3D-printed composites, and a novel multi-level optimization strategy for designing load-bearing and functional composite structures.
(2) This report demonstrates an advanced multi-axis robotic 3D printing system capable of processing both continuous and short fiber-reinforced composites. The research establishes a comprehensive methodology for printing pressure measurement and calculation, while systematically investigating the effects of key process parameters on both printing pressure and product quality. Furthermore, a coupled thermo-mechanical model of the printing process is discussed.
(3) The report discusses several advanced applications of 3D Printing Technology, including bio-inspired interface-toughening structures, process-controlled bioinspired structures with alternating strong-weak layers, and induction-heated lattice-reinforced thin-walled structures.

Biography: Prof. Kunkun Fu is currently working as Deputy Dean of the School of Aerospace Engineering and Applied Mechanics at Tongji University, P.R. China. He was awarded a national early-career scientist fellowship in 2018 after he worked as a Postdoctoral Research Fellow at the University of Sydney and the University of New South Wales Canberra. Prof. Fu’s major research interests include the failure mechanics of composite structures and additive manufacturing of fiber reinforced composites. He has strong expertise in numerical modelling of composite structures in various engineering areas in particularly under extreme conditions such as lightning strike and impact. So far, he has published over 100 peer-reviewed journal publications and served as the editorial members of several journals, the committee member of the Chinese Society of Theoretical and Applied Mechanics, the executive committee member of Shanghai Society of Theoretical and Applied Mechanics, and the committee member of Shanghai Society of Composite Materials.

Abstract: Data-driven methods based on machine learning (ML) are increasingly being used for constitutive modeling of advanced aerospace materials, including metallic alloys and fiber-reinforced composites. However, their reliance on extensive datasets has hindered further development. To address this limitation, we propose an innovative mechanics-informed ML approach to predict the elastoplastic behavior and anisotropic features using small training datasets. For elastoplastic metallic materials, a novel strain reconfiguration strategy is proposed to improve the learning capability and predictability of the data-driven model, along with a two-step training method. A compatible numerical implementation algorithm is developed to incorporate the data-driven approach into a finite element calculation. This method is applied to learn and predict the mechanical response of Ti-6Al-4V titanium alloy under multiple loading conditions, including tension, compression, shear and impact loads. For orthotropic composites, we establish a decomposition and equivalence method for stress-strain tensors. Two independent artificial neural networks are employed to capture the deviatoric behavior and the volumetric-fiber coupling behavior, respectively. Additionally, an incremental algorithm is introduced to map the one-dimensional scalar stress back to the three-dimensional stress tensor. The proposed model is validated using a dataset generated by direct numerical simulation of a representative volume element (RVE) of composites, and further applied to the simulation of textile composites. The consistency between the constitutive model and the data highlights the advantages of the proposed approaches: integrating mechanics with ML significantly enhances predictive accuracy, even with limited data.

Biography: Dr. Zhang is Professor and Ph.D. supervisor for the School of Civil Aviation, Northwestern Polytechnical University. His research direction lies in the fields of multi-scale mechanics of composite materials, impact dynamics, and strength of aerospace engines structures. Dr. Zhang has awarded more than 20 scientific research projects (5 from NSFC). He is recipient of the National High-level Talent Youth Program, Shaanxi Youth Scientist Award and National Outstanding Young Scholar in Explosive Mechanics et al. Dr. Zhang has published more than 150 journal papers, with Google Citations over 5400. He serves as editorial board member for several scientific journals, e.g. Compos Struct, Acta Mechan Sin, Chin J Aeronaut, J Aero Eng et al., and active members of the Chinese Society for Composite Materials and The Chinese Society of Theoretical and Applied Mechanics.