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White Paper

Predict fatigue life for additive manufacturing with Simcenter 3D

Reading time: 24 minutes


Predicting the fatigue life of additive manufacturing (AM) materials is challenging, especially for structurally loaded safety-critical components. Manufacturers know which simulation methods and tools to use to properly include fatigue analysis in their engineering processes with more established materials. However, with AM, it's different. Fortunately, Simcenter engineers have designed a novel strategy to predict the fatigue performance of AM parts more accurately, faster, and cheaper.

Read the white paper and discover how Simcenter 3D software can optimize both the design and the manufacturing process to achieve a reliable AM-based product by:

  • Predicting the AM-induced local material conditions
  • Predicting the influence of the AM-induced local material conditions on fatigue properties with machine learning
  • Performing efficient durability analysis considering the AM-induced local conditions

Accurately predict fatigue life for additive manufactured components

The three categories of mechanisms that make up the simulation process for AM fatigue failure prediction are geometry, loading, and material. The endless possible combinations of material conditions are a significant challenge for engineers. It becomes even more difficult on the component level because engineers also need to consider many AM-induced local fatigue-influencing factors. Simcenter 3D software provides solutions to all these challenges to ultimately achieve an efficient simulation-based approach to predict fatigue life accurately.

Introduce machine learning to predict fatigue properties

The fatigue performance of additive manufactured parts largely depends on the structure's local artifacts. These can lead to a non-uniform distribution of certain local properties such as porosity and surface roughness. With Simcenter 3D software, manufacturers can characterize the local material conditions introduced by the AM process use these in a machine-learning algorithm to create a material model for fatigue that considers the local needs. This enables them to finally study the global durability behavior of the printed part based on accurate predictions.

Implement Simcenter 3D software for a durability simulation method

The final step is to add material properties to a durability simulation analysis that is conducted on the part level. Simcenter 3D software can include the above-mentioned machine-learning material model for predicting fatigue properties. It can start from either experimental or simulated data. And by having manufacturing details in the simulation, the process can lead to spectacular improvements. Accurate durability predictions allow engineers to achieve enormous productivity gains by limiting time-consuming, high-quality printing to critical locations only.

Download the white paper to learn more about our unique, validated durability simulation method added to Simcenterâ„¢ 3D software, allowing manufacturers to print durable parts much faster and cheaper.