function problem. While designers work to achieve
optimum shapes to utilize the full potential of Additive
Manufacturing, machine process specialists work
on improving machine parameters for building better
parts, which in turn affects the design. To achieve
optimum results, Additive Manufacturing processes
must be treated as multi-scale multiphysics problems.
For example, it is essential to examine the physics
behind a material’s melting/ solidification behavior at
millisecond increments and micrometers in length, as
well as how processes variations affect production
parts over the course of hours. These types of
modeling and analysis are beyond the capabilities of
traditional simulation software. We need to address
the challenges by using a multi-scale multi-physics
cross-function platform to digitally connect end-to-end from the beginning design – to the end
product – amongst different experts in the organization including
designers, machine process specialists and analysts.
Let’s look at a turbine blade use case (Figure 2). A multi-physics
platform, such as Dassualt Systems’ 3DEXPERIENCE, can enable
engineers to prepare their turbine blade for the build process in
a virtual machine environment. The platform allows them to orient
the part properly on the build tray, generate support structures,
nest the parts, and minimize support structure using geometric
optimization tools. The virtual build preparation can exported to a
printer in a readable file format such as .stt, or it sent to a virtual
printing environment to study the interaction between the design
and the physics of the print process. This helps to optimize
the part/assembly design and machine process
parameters for best build quality.
Engineers can use the multi-physics simulation
platform allows to model the virtual print process
using either thermo-mechanical or eigenstrain
methods. The outcomes could result in performing
a detailed thermo-mechanical analysis to predict
complex physics – such as buckling and failure –
or performing a direct stress analysis based on a
pre-defined eigenstrain library which will eventually
become a faster approach once the user builds
up their library. Figure 3 shows the equivalent
results from thermo-mechanical and eigenstrian
simulations on the same turbine blade design.
Distortion results could be directly mapped back
to create new compensated designs with the
Figure 4. Material modelling from atoms to parts.
Figure 2. Turbine blade: Real Print vs. Numerical Model.
Figure 3. Distortion prediction of turbine blade print: thermo-mechanical vs.
eigenstrain pattern based.