Skip to content

Characterizing the Deformation Microstructure of an Additively Manufactured 316-L Stainless Steel Alloy

Introduction

Additive manufacturing (AM), also known as 3D printing, is a disruptive new manufacturing approach used to produce metallic parts with improved mechanical properties compared to alternative traditional methods. 3D printing can be achieved on a range of metallic alloys with a variety of processes, including direct metal laser sintering (DMLS), direct metal laser melting, and electron beam melting. Each process influences the material’s microstructure, which in turn determines the mechanical properties. Electron backscatter diffraction (EBSD) is an ideal tool for characterizing the microstructure and gaining a better understanding of the microstructure-property relationships.

Results and Discussion

EBSD is useful to measure many microstructural features, including crystallographic orientation, grain size, grain boundary structure, and phase distribution. An example from an AM 316-L stainless steel sample fabricated using laser powder-bed fusion is shown in Figure 1. Figure 1a shows a combined EBSD image quality and inverse pole figure (IPF) map, where the points are colored relative to their surface normal orientation. Figure 1b depicts a combined EBSD image quality and grain map, where points of similar orientation are grouped and colored randomly to highlight morphology.

Figure 1. a) Inverse pole figure (IPF) orientation map and b) grain map from an additively manufactured 316-L stainless steel sample.
Figure 1. a) Inverse pole figure (IPF) orientation map and b) grain map from an additively manufactured 316-L stainless steel sample.

This data was collected using the Clarity™ EBSD Analysis System. The next-generation direct electron detector eliminates the need for the phosphor screen and optics by directly imaging the diffracted electrons and removes the blurring and distortions these traditional approaches introduce. While this detector can collect the traditional EBSD information shown in Figure 1, the sharpness and high-quality EBSD data collected with the Clarity enables high-fidelity EBSD measurements for improved orientation precision. This performance is important, as alloys made by L-PDB develop microstructures with high residual stresses due to rapid solidification.

These AM steels are known to have exceptional mechanical properties. Specifically, they offer better strength and ductility than many wrought materials with similar compositions. The reasoning for this is the dense dislocation network that exists after printing. The characteristics of this dislocation network vary with build parameters, as well as locally within a single build. The reasons for this are attributed to a variety of factors, including local grain boundary characteristics, cooling rates, retained strains, and geometrical constraints. A challenge with understanding the factors that control these dislocation networks is statistically mapping out the dislocations as a function of microstructure and build parameters. Transmission electron microscopy cannot cover enough area, and X-ray techniques do not have sufficient spatial resolution. High-angular resolution EBSD (HR-EBSD), combined with the Clarity Detector's performance, spans that divide by offering sufficient spatial resolution, orientation precision, and collection automation to allow quantified characterization of these microstructures.

Figure 2. a) IPF orientation map and b) KAM map at higher spatial resolution. c) HR-KAM map calculated using cross-correlation HR-EBSD analysis, showing local defects within the microstructure.
Figure 2. a) IPF orientation map and b) KAM map at higher spatial resolution. c) HR-KAM map calculated using cross-correlation HR-EBSD analysis, showing local defects within the microstructure.

Figure 2 shows data collected from a smaller field of view to highlight the microstructural details extracted with these tools. Figure 2a shows the IPF orientation map of this field of view. Figure 2b depicts the Kernel Average Misorientation (KAM) map calculated from these measured orientations. Figure 2c shows a high-angular resolution KAM map, derived using a cross correlation-based approach. This HR-KAM map indicates significantly reduced noise levels and highlights the local defect structure within the grains. This data is useful to quantify the geometrically necessary dislocation density. Figure 3 shows these results.

Additional EBSD metrics are useful to visualize the cellular structures that develop during rapid solidification and play a vital role in the beneficial properties that develop. Figures 4a and 4b show an EBSD image quality map and a PRIAS™ (center) map from the same area, respectively. In these images, the dislocation cell structure is visible. A combination of the defect information from these images and quantitative microstructure analysis via HR-EBSD provides a route for establishing the structure-property relationships on a local scale in additively manufactured materials.

Figure 3. Geometrically necessary dislocation (GND) density map calculated using high-fidelity EBSD patterns collected with the Clarity detector and HR-EBSD analysis.
Figure 3. Geometrically necessary dislocation density map calculated using high-fidelity EBSD patterns collected with the Clarity Detector and HR-EBSD analysis.

Figure 4. Visualization of a rapid solidification cellular microstructure developed during additive manufacturing using a) EBSD image quality and b) PRIAS center signals.
Figure 4. Visualization of a rapid solidification cellular microstructure developed during additive manufacturing using a) EBSD image quality and b) PRIAS center signals.

Conclusion

Additionally, the stress state associated with these dislocation networks is expected to play an essential role in dictating mechanical properties. Still, it cannot be resolved with X-ray-based techniques due to the spatial resolution of that technique. An HR-EBSD approach is instrumental in better understanding the local stress state in AM materials as a function of microstructure and build parameters and, in turn, help us tune the mechanical properties of these materials. The Clarity Detector provides the high-definition EBSD performance required to optimally capture the key features built into these microstructures.

Acknowledgments

EDAX would like to thank Professor Josh Kacher of the Georgia Tech School of Materials Science and Engineering for providing the sample, assisting with the HR-EBSD analysis, and giving valuable insight into the AM microstructures.