activity
20222024
most citedMachine learning-informed structuro-elastoplasticity predicts ductility of disordered solids

2 citations · 3 across the 5 of their papers we have counts for

collaborators

5 papers

cond-mat.soft2024

Combined thermal and particle shape effects on powder spreading in additive manufacturing via discrete element simulations

Sudeshna Roy, Hongyi Xiao, Vasileios Angelidakis +1

The thermal and mechanical behaviors of powders are important for various additive manufacturing technologies. For powder bed fusion, capturing the temperature profile and the pack…

cond-mat.soft20231 cited

DEM simulation of the powder application in powder bed fusion

Vasileios Angelidakis, Michael Blank, Eric J. R. Parteli +4

The packing behavior of powders is significantly influenced by various types of inter-particle attractive forces, including adhesion and non-bonded van der Waals forces [1, 2, 3, 4…

cond-mat.mtrl-sci2023

Structural fluctuations in thin cohesive particle layers in powder-based additive manufacturing

Sudeshna Roy, Hongyi Xiao, Vasileios Angelidakis +1

Producing dense and homogeneous powder layers with smooth free surface is challenging in additive manufacturing, as interparticle cohesion can strongly affect the powder packing st…

cond-mat.soft20232 cited

Machine learning-informed structuro-elastoplasticity predicts ductility of disordered solids

Hongyi Xiao, Ge Zhang, Entao Yang +5

All solids yield under sufficiently high mechanical loads. Below yield, the mechanical responses of all disordered solids are nearly alike, but above yield every different disorder…

cond-mat.soft2022

Modeling Stratified Segregation in Periodically Driven Granular Heap Flow

Hongyi Xiao, Zhekai Deng, Julio M. Ottino +2

We present a continuum approach to model segregation of size-bidisperse granular materials in unsteady bounded heap flow as a prototype for modeling segregation in other time varyi…