Additively manufactured stainless steels have become increasingly popular due to their desirable properties, but their mechanical behavior in structural parts is not yet fully understood. Specifically, the impact of columnar microstructures on fatigue behavior is still unclear. A typical directed energy deposition (DED)‐fabricated 316L stainless steel microstructure consists of distinct zones with equiaxed and columnar grains. To answer the question of how these zones of a DED‐fabricated 316L stainless steel microstructure affect the local mechanical behavior individually, such as the fatigue strength, stress/strain distribution, and fatigue life, crystal plasticity simulations are conducted to investigate the influence of microstructure on local mechanical behavior such as fatigue strength, stress/strain distribution, and fatigue life. The simulations find that columnar microstructures exhibit better fatigue strength than equiaxed structures when the load is parallel to the major axis of the columnar grains, but the strength decreases when the load is perpendicular. This study also uses machine learning to predict fatigue life, which shows good agreement with crystal plasticity modeling. The study suggests that the combined crystal plasticity–machine learning approach is an effective way to predict the fatigue behavior of additively manufactured components.
A Data-Driven Framework to Select a Cost-Efficient Subset of Parameters to Qualify Sourced Materials
The quality of powder processed for manufacturing can be certified by hundreds of different variables. Assessing the impact of all these different metrics on the performance of additively manufactured engineered products is an invaluable, but time intensive specification process. In this work, a comprehensive, generalizable, data-driven framework was implemented to select the optimal powder processing and microstructure variables that are required to predict the target property variables. The framework was demonstrated on a high-dimensional dataset collected from selective laser melted, additively manufactured, Inconel 718. One hundred and twenty-nine powder quality variables including particle morphology, rheology, chemical composition, and build composition, were assessed for their impact on eight microstructural features and sixteen mechanical properties. The importance of each powder and microstructure variable was determined by using statistical analysis and machine learning models. The trained models predicted target mechanical properties with an R2 value of 0.9 or higher. The results indicate that the desired mechanical properties can be achieved by controlling only a few critical powder properties and without the need for collecting microstructure data. This framework significantly reduces the time and cost of qualifying source materials for production by determining an optimal subset of experiments needed to predict that a given source material will lead to a desired outcome. This general framework can be easily applied to other material systems.
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- Award ID(s):
- 1552716
- NSF-PAR ID:
- 10341483
- Date Published:
- Journal Name:
- Integrating Materials and Manufacturing Innovation
- ISSN:
- 2193-9764
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1007/s40192-022-00266-3
