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DOE OSTI · 1581976

Automated high-throughput tensile testing reveals stochastic process parameter sensitivity

Abstract

The mechanical properties of additively manufactured metals tend to show high variability, due largely to the stochastic nature of defect formation during the printing process. This study seeks to understand how automated high throughput testing can be utilized to understand the variable nature of additively manufactured metals at different print conditions, and to allow for statistically meaningful analysis. This is demonstrated by analyzing how different processing parameters, including laser power, scan velocity, and scan pattern, influence the tensile behavior of additively manufactured stainless steel 316L utilizing a newly developed automated test methodology. Microstructural characterization through computed tomography and electron backscatter diffraction is used to understand some of the observed trends in mechanical behavior. Specifically, grain size and morphology are shown to depend on processing parameters and influence the observed mechanical behavior. In the current study, laser-powder bed fusion, also known as selective laser melting or direct metal laser sintering, is shown to produce 316L over a wide processing range without substantial detrimental effect on the tensile properties. Ultimate tensile strengths above 600 MPa, which are greater than that for typical wrought annealed 316L with similar grain sizes, and elongations to failure greater than 40% were observed. As a result, it is demonstrated that this process has little sensitivity to minor intentional or unintentional variations in laser velocity and power.

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BibTeXRIS

Heckman, Nathan M., Ivanoff, Thomas A., Roach, Ashley M., Jared, Bradley H., Tung, Daniel J., Brown-Shaklee, Harlan J., Huber, Todd, Saiz, David J., Koepke, Josh R., Rodelas, Jeffrey M., Madison, Jonathan D., Salzbrenner, Bradley C., Swiler, Laura P., Jones, Reese E., Boyce, Brad L.. 2019-11-09. Automated high-throughput tensile testing reveals stochastic process parameter sensitivity. https://doi.org/10.1016/j.msea.2019.138632

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36 MATERIALS SCIENCE↗