Yang Xiang

Mechanical metamaterials exhibit unusual mechanical properties that originate from their topological design. Origami graphene may provide a platform for constructing novel carbon nanostructures. In the current study, a novel composite material that incorporates aligned graphene with localized origami shapes into the copper matrix is designed. Unlike many auxetic materials that have void phase, the proposed layer‐by‐layer assembled composite material appears to be solid in the microscopic scale. Based on the molecular dynamics (MD) simulations, it is observed that depending on the origami shapes, the in‐plane Poisson's ratio of origami graphene reinforced copper (100) composites can be tuned from positive to negative and, more importantly, it is discovered that the Young's modulus of the graphene/Cu composites can also be tuned from positive to negative in certain in‐plane directions.

Negative Poisson’s ratio (NPR), also known as “auxetic”, is a highly desired property in a wide range of future industry applications. By employing molecular dynamics (MD) simulation, metal matrix nanocomposites reinforced by graphene sheets are studied in this paper. In the simulation, single crystal copper with crystal orientation 1   1   0 is selected as the matrix and an embedded-atom method (EAM) potential is used to describe the interaction of copper atoms. An aligned graphene sheet is selected as reinforcement, and a hybrid potential, namely, the Erhart-Albe potential, is used for the interaction between a pair of carbon atoms. The interaction between the carbon atom and copper atom is approximated by the Lennard-Jones (L-J) potential. The simulation results showed that both graphene and copper matrix possess in-plane NPRs. The temperature-dependent mechanical properties of graphene/copper nanocomposites with in-plane NPRs are obtained for the first time.

A temperature-dependent intrinsic property of monolayer graphene, the negative Poisson’s ratio (NPR), is investigated in the present study. The classical molecular dynamics (MD) method is employed and the Erhart-Albe hybrid potential, i.e. the combination of the reactive empirical bond order (REBO) and the Tersoff potentials, is used for the graphene sheet in the numerical simulation. In the simulation process, the graphene sheet is assumed to be free standing with in-plane periodical boundary condition and under an ambient temperature up to 1000 K. Our study shows that the graphene NPR is decreased with the increase of temperature. Besides, we also perform the simulation of the graphene negative temperature expansion coefficient (NTEC) as an indirect validation of the present MD model. The characteristics of the nonlinear variations for both the NPR and the NTEC of a pristine graphene sheet are investigated. Our MD results at low temperature (0.1 K) further prove the intrinsic and anisotropic property of NPR for graphene.

In this study, three case studies were conducted to develop a reliable finite element-based numerical model of Chain-die Forming (CDF) with AHSS. These simulations demonstrated the effectiveness of this forming process while capturing the mechanical behaviours of AHSS. The numerical modelling and simulations serve as Computer Aided Engineering (CAE) tools which determined tool geometries and control unwanted spring back. An automotive martensitic steel DOCOL 1400M from SAAB was adopted for this research as a typical AHSS material, which has a yielding strength of 1,150 MPa and a tensile strength of 1400-1600 MPa. There were three case studies involved (a) Case Study ? 1: Forming AHSS 60? section; (b) Case Study ? 2: Forming AHSS 90? section; and (c) Case Study ? 3: Forming top-hat section. All three cases were conducted to achieve desired profiles in minimum number of passes without defects. Validation and verification of the CDF process were further demonstrated based on these case studies. According to the numerical and experimental results obtained, Chain-die Forming (CDF) can be considered as an affordable, sustainable and environmental friendly manufacturing process.