Projects (Since Ph.D in NUS)

High-fidelity coupled CFD-FEM simulation on DED and LPBF

– corporation with ANSYS

Linked the high-fidelity molten pool dynamics model with the finite element solver for thermal stress simulation.

High-density dislocations in additively manufactured metals

– corporation with Shanghai Jiaotong University

Demonstrated that thermal stresses are the origin of high-density dislocations in additively manufactured metals using the coupled CFD-FEM simulation.

Physically informed data-driven prognostic model for the temperature prediction in AM

– corporation with Singapore A*STAR

High accuracy and significantly faster computation speed for the track-scale AM modeling based on the data-driven prediction (CNN, GPR, QR) of the isotherms and the isotherm-reconstructed temperature attribution on FEM model.

Pattern-oriented Strain Attribution in Part-scale Modeling of Additive Manufacturing

– corporation with Singapore A*STAR

Efficient large-scale AM modeling approaches with sound physical basis, where the layer-wise element progressive activation is applied with base strain pattern attributed onto the sample layers.

Hybrid Autonomous Manufacturing, Moving from Evolution to Revolution (HAMMER)

– corporation with The OHIO State University and so on.

High-fidelity English wheel simulation: distortion analysis of the sheet metal under rolling of two wheel of different size and curvature.

Micro-Casting of Titanium Alloy in Self-boiling Molds

Heat transfer analysis of liquid materials in multiple self-boiling molds for micro-casting.

DED on sheet metal forming

– corporation with Nissan

Deformation prediction using bulking analysis under the residual stress pattern achieved by track-scale DED simulation.

(AID4Greenest) - AI powered characterization and modelling for green steel technology

Funded by European Union

– corporation with Fraunhofer, IWM and IMDEA et al.

Fortran-based self-developed Lagamine coding platform for implementation of H. Morch visco-plasticity law & Development of the mean field creep model on Gitlab using Python.