Research

Research Interest

• Multiscale modeling and digital twin in materials science and engineering: This research aims to predict the deformation and damage behavior of metallic materials using microstructure-based multiscale modeling. Crystal plasticity frameworks are employed to capture the effects of crystallographic orientation, slip systems, and deformation twinning on macroscopic responses. The models are validated through experimental techniques such as EBSD and XRD. This approach enables physically informed predictions under various loading and temperature conditions and supports material and process design. 

• Ultra-thin metallic sheet (stainless steel and titanium) for fuel cell application: This research focuses on the mechanical behavior and formability of ultra-thin metallic sheets, particularly stainless steel and titanium, for fuel cell applications. Special attention is given to anisotropic deformation, surface roughening, and fracture behavior associated with thickness reduction. The study also aims to optimize material properties and forming conditions for the fabrication of high-performance bipolar plates. Ultimately, this work contributes to the development of durable and efficient fuel cell systems. 

• Machine Learning: This research explores the use of machine learning techniques for process optimization and performance prediction in sheet metal forming. Data-driven models are developed to predict forming limits, thickness variation, and fracture behavior based on experimental and simulation datasets. Various algorithms and hyperparameter optimization strategies are investigated to enhance prediction accuracy. The integration of physics-based and data-driven approaches enables efficient and robust process design. 

• Fracture behavior of metallic sheet materials: This research investigates the fracture behavior of metallic sheet materials under various loading paths and deformation conditions. Key topics include forming limit diagrams (FLDs), crack initiation and propagation, and damage evolution. A combination of experimental characterization and finite element analysis is used to develop predictive models. The ultimate goal is to enhance the reliability and safety of forming processes for advanced sheet materials.