Additive Manufacturing: FEA Based Design and Optimization with Simufact, Abaqus, ANSYS and MSC Apex
FEA & CFD Based Simulation Design Analysis Virtual prototyping MultiObjective Optimization
With additive manufacturing, the design is not constrained by traditional manufacturing requirements and specific number of design parameters. Nonparametric optimization with new technologies such as Artificial Intelligence in coupled with Finite Element method, can be used to produce functional designs with the least amount of material. Additive manufacturing simulations are key in assessing a finished part’s quality. Here at Eneteknograte, dependent of the problem detail, we use advanced tools such as MSC Apex Generative Design, Simufact Additive, Digimat, Abaqus and Ansys.
Additive manufacturing, also known as 3D printing, is a method of manufacturing parts typically from powder or wire using a layer by layer approach. Interest in metal based additive manufacturing processes has taken off in the past few years. The three-major metal additive manufacturing processes in use today are powder bed fusion (PBF), directed energy deposition (DED) and binder jetting processes.
Enteknograte Engineering Team propose special simulation tools for each of these processes. With additive manufacturing, the design is not constrained by traditional manufacturing requirements and specific number of design parameters. Nonparametric optimization with new technologies such as Artificial Intelligence in coupled with Finite Element method, can be used to produce functional designs with the least amount of material.
Additive manufacturing simulations are key in assessing a finished part’s quality. The physics behind the manufacturing process can be accurately recreated in software platforms, and enabling end to end digitalization and so on, factors which will be crucial in the service life of a part.
Our solution’s functionality helps you to answer challenges in Metal AM (Additive Manufacturing) Finite Element Simulation-based design and Optimization:
- Identify the best build orientation
- Determine and compensate final part distortionGenerate and optimize support structures
- Process window pre-scanning tool
- Powder coating
- Melt pool shape and dimensions
- Consolidated material porosity
- Surface roughness
- Thermal history as a function of deposition strategy
- Residual stresses
- Distortion during build process and after release
- Identify manufacturing issues such as cracks, layer offsets, recoater contact
- Predict the influence of several components in the build space
- Identify cold and hot spots due to thermal/thermo-mechanical simulation
- Examine conditions of highly elevated temperatures and pressures – HIP proces
Optimizing the design parameters for additive manufacturing
FEA Based Simulation enable our engineering team to gain insight into the microscale meltpool phenomena by performing full factorial studies with various process parameters for determine the best process parameters for any machine/material combination, and ensures the achievement of the highest integrity parts, as well as the expected microstructure and physical properties:
- Optimize and fine-tune their machine and material parameters.
- Develop new metal powders and metal AM (Additive Manufacturing ) materials and material specifications.
- Determine optimum machine/material parameters.
- Control microstructure and material properties.
- Manufacture using new metal powders faster and more efficiently.
- Reduce the number of experiments needed to qualify components.
- Mitigate risk while accelerating innovation.
- Analyze Porosity and Meltpools.
- Thermal history and microstructure information.
- Determines the percentage of porosity in a part due to lack of fusion.
WE WORK WITH YOU
We pride ourselves on empowering each client to overcome the challenges of their most demanding projects.
Enteknograte offers a Virtual Engineering approach with FEA tools such as MSC Softwrae(Simufact, Digimat, Nastran, MSC APEX, Actran Acoustic solver), ABAQUS, Ansys, and LS-Dyna, encompassing the accurate prediction of in-service loads, the performance evaluation, and the integrity assessment including the influence of manufacturing the components.
