Current Projects

Digital-Twin-Driven Codesign and 4D Printing of Intelligent Systems

Personnel: Moonsung Park  (Ph.D. student)

Nature provides examples of soft structures that can stretch, deform, heal, and transform shape. Inspired by these systems, this project develops a new physical–digital-twin framework to design and 4D print multifunctional soft materials, including hydrogels, shape-memory polymers, and elastomers with programmable mechanical strength and shape-morphing behaviors. Using dynamic projection grayscale lithography, we 4D print thin films that contain engineered cellular domains microscale patterns that locally tune stiffness, strain distribution, and deformation. These printed “physical twins” are paired with digital twins, multiphysics simulation, machine learning models, and data driven inverse design, to predict how the printed material will stretch, bend, morph into 3D shapes, or respond to heat, light, or solvents. By letting physical twins and digital twins continuously inform each other, the project moves beyond trial-and-error manufacturing toward predictive, uncertainty aware design. We aim to create soft material systems that can adapt shape, enhance mechanical performance, program sequential motion for soft robotics and actuation, and enable generalizable design rules for future autonomous 4D-printing platforms. This research integrates advanced manufacturing, machine learning, and multiphysics simulations into. digital-physical-twin-guided frameworks for applications in soft robotic grippers, adaptive biomedical devices, stretchable electronics, reconfigurable infrastructure, and any intelligent systems that change with external stimuli.

AI-Driven 4D Printing of Shape Morphing Actuators

Personnel: Haotian Li and Moonsung Park  (Ph.D. student)

We developed an AI-driven inverse design framework that integrates finite element (FE) simulation, generative machine learning, and halftone-based 4D printing. Rather than relying on trial-and-error fabrication, the system automatically computes the optimal halftone parameters needed to deliver a target actuation behavior. We construct large training datasets using FE simulations of halftone-encoded heterostructures with varied pattern distributions. Time-resolved strain mapping captures mechanical responses at both local and global scales. Surrogate forward neural networks and generative inverse models link user-defined mechanical targets to manufacturable design parameters, enabling rapid prediction and fabrication of actuators with tailored properties. In addition to mechanical tuning, halftone-regulated growth deformations, such as thermoresponsive shrinkage, are used to program 3D shape transformations. A deep residual neural network, trained on FE-generated deformation datasets, performs both forward shape prediction and inverse mapping to generate halftone patterns for a prescribed final geometry, without requiring complex derivative calculations.