Answers to AM Challenges in Industrial Applications

A new study to make fatigue performance more predictable

Under the title »Additive Fatigue 2.0«, , Fraunhofer IAPT is conducting a new study on how to make fatigue performance more predictable. The study focuses on phenomenon-driven fatigue design for metal AM using titanium.

Post-processing is a major source of uncertainty in fatigue performance and cost in additive manufacturing. The new study on additive fatigue links critical influences and key phenomena to the fatigue properties of LB-PBF metal components to improve predictability and define reliable process chains.

Investigating fatigue beyond mere surface finishing

Instead of focusing only on surface finishing methods, the new »Additive Fatigue 2.0« study examines the most relevant drivers influencing the fatigue performance along the whole post-processing chain. The study investigates the fundamental mechanisms behind fatigue life for LB-PBF metal components.

The influence of...

Heat treatment (In Process, HIP and stress-relief) on alpha case build-up, hardness and fatigue properties

Compacting the material surface with shot peening on subsurface residual stress and fatigue life

Geometry (thin vs. bulky) on the final mechanical properties

 

Early bird benefits

Ensure the test matrix reflects your use cases: Contribute to the study design by

  • prioritizing materials,
  • surface states, and
  • load cases that match your actual production applications.

Early access and exclusive discussions with the study authors:

  • Receive and apply preliminary findings long before the full study is released.
  •  Discuss the results with our AM experts.

Available Studies

Completed benchmarks ready for immediate purchase

 

Surface Finishing

Benchmark of the Relevant Surface Finishing Processes for Metal AM Components

 

Download Free Sample

Mechanical Properties

Influence of Different Surface Finishing Methods on Mechanical Properties of Metal AM Components

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Process Monitoring

A comparison of process monitoring and their capabilities for detecting defects and production issues.

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