Background
Additive manufacturing (AM) allows for the creation of highly complex parts, but the rough surfaces produced during the process can significantly reduce the fatigue life of these components. This issue is critical in industries such as aerospace and automotive, where parts experience repeated stress over time. Surface treatments, like polishing, can improve fatigue performance, but they are often impractical for parts with complex or inaccessible geometries. There is a strong demand for fatigue prediction methods that account for surface roughness without requiring additional post-processing, improving the reliability and adoption of AM parts in critical applications.
Invention Description
Researchers at Arizona State University have developed a crack growth-based methodology for assessing the fatigue life of additively manufactured components. This innovative approach models surface roughness as an equivalent notch, accounting for stress concentration and utilizing an asymptotic stress intensity factor interpolation method. By employing a time-based sub-cycle fatigue crack growth model, it can accurately predict crack initiation and propagation under both uniaxial and multiaxial loading conditions. The method has been validated against experimental data for various materials, providing a practical solution for predicting fatigue life without the need for surface treatments. This technology can be used for applications that require AM to produce high-performance parts with rough surfaces.
Potential Applications
- Automotive
- Aerospace
- Heavy machinery and industrial equipment
- Energy sector
Benefits and Advantages
- Accurate fatigue life prediction
- Applicability to complex geometries
- Handles multiaxial loading
Related Publication: Crack growth-based life prediction for additively manufactured metallic materials considering surface roughness