Simulation for Additive Manufacturing

Simulation for Additive Manufacturing

Additive manufacturing is not a new technology – it was introduced in the manufacturing industry in late 80s for very niche applications. Stereolithography, a variant of additive manufacturing, was introduced in 1986 for rapid prototyping applications; however, its true potential remained hidden for a long time. Additive manufacturing primarily refers to methods of creating a part or a tool using a layered approach. As a still-evolving technology, it now covers a family of processes such as material extrusion, material jetting, direct energy deposition, power bed fusion, and more.

Additive manufacturing expands design possibilities by eliminating many manufacturing constraints. Contrary to rapid prototyping and 3D printing, there has been a shift of focus to functional requirements in additive manufacturing; however, these functional requirements may deviate from what is expected due to many factors typical of an additive manufacturing process.

  • Change in material properties: Mechanical and thermal properties of a manufactured part differ from raw material properties. This happens due to material phase change which is typical to most additive manufacturing applications.
  • Cracking and failure: The process itself generates lots of heat that produces residual stresses due to thermal expansion. These stresses can cause cracks in material during manufacturing.
  • Distortion: Thermal stresses can lead to distortion that can make the part unusable.

The additive manufacturing process is not certifiable yet, which is a major barrier in widespread adoption of these processes commercially. The ASTM F42 committee is working on defining AM standards with respect to materials, machines, and process variables.

The role of Simulation in additive manufacturing

  • Functional design: The first objective is to generate a suitable design that meets functional requirements, then subsequently improve the design through optimization methodologies that work in parallel with simulation.
  • Generate a lattice structure: Many of the parts manufactured through AM have a lattice structure instead of a full continuum. One objective of simulation in AM is to generate a lattice structure and optimize it using sizing optimization.
  • Calibrate material: As mentioned before, the material properties of a final part can differ substantially from that of the raw material. The next objective is to capture the phase transformation process through multi-scale material modeling.
  • Optimize the AM process: Unwanted residual stresses and distortions can develop in the process. It is necessary to accurately capture these physical changes to minimize the gap between the as-designed and as-manufactured part specs.
  • In service performance: Evaluate how the manufactured part will perform under real life service loads with respect to stiffness, fatigue, etc.

 

Now let’s discuss each of these objectives in more detail, with respect to SIMULIA.

Functional design can be optimized using various methodologies available in TOSCA, such as topology and shape optimization. Topology optimization is a non-parametric approach that primarily uses element density as a design variable to conceptualize a lightweight design through material removal that still satisfies functional requirements. The optimized functional design can further be improved to optimize the AM process by using shape optimizers that operate upon surface nodes to eliminate stress hot spots.

Lattice structure can be optimized using the newly introduced lattice sizing feature in TOSCA 2016. This feature allows the user to take an initial lattice structure into TOSCA and then define radii of each lattice as a design variable with a lower and upper bound.

Optimization of the AM process requires the simulation of the AM process itself to compute the gap between the “as designed” and “as manufactured” part with respect to residual stresses, altered properties, and distortion. The starting point for such simulation is the utilization of a welding process simulation from Abaqus, with some changes for the AM process. The welding simulation takes into account weld passes, temperature dependent material properties, and thermal and structural BCs. This data can be used to predict residual stresses, distortion as well as failure due to fracture. One key difference between AM and welding is that simulation has to take into account heat flow across a surface that is still evolving.

How various simulation steps are executed

The sequence of steps to be followed is similar to any other FEA process that includes pre-processing, solving, and post processing. However some major aspects typical for AM have to be followed that are mentioned below.

  • As this is a layered process simulation, the CAD model has to be sliced into a number of layers based on the number of parameters such as layer thickness or element size. For a complex CAD model, this can be a daunting task. Abaqus CAE has a plug-in for additive manufacturing to make this task easier.
  • Progressive material addition is a very non-trivial step of AM from a pre-processing perspective. The entire FEM is not used at once – rather, SIMULIA uses a machine code neutral format to capture the laser paths from various machine codes. Elements in the model are initially in a “quiet” state. According to the translated machine code, elements are progressively transformed from “quiet” to “active” state.
  • The part is subjected to local heating from laser flux energy. At the same time part cools down due to convection and possibly radiation, which are surface phenomena. The tricky part is to define these thermal BCs on a surface that is constantly evolving, but the enhancements in the solver for AM capture this behavior.
  • A phenomenal change in material property takes place. In the case of composites, fiber direction is changed and in case of metals, solidification creates micro level property transformation that needs to be captured. This is made possible by the incorporation of BIOVIA brand products into SIMULIA that are specialized in micro level modeling.

Just like additive manufacturing itself, the simulation methods for AM processes are evolving as well. The objective is to bring end to end digital technology for additive manufacturing that addresses all aspects of AM for realistic simulation.

Ankur Kumar

Simulation Specialist at Tata Technologies
Ankur has 11 years of experience in Computer Aided Engineering and has obtained the SIMULIA Design Sight, EPP and Support certifications from Dassault Systemes.

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Ankur Kumar

Simulation Specialist at Tata Technologies
Ankur has 11 years of experience in Computer Aided Engineering and has obtained the SIMULIA Design Sight, EPP and Support certifications from Dassault Systemes.