High-throughput stochastic tensile performance of additively manufactured stainless steel by Salzbrenner, Bradley C., Rodelas, Jeffrey M., Madison, Jonathan D., Jared, Bradley H., Swiler, Laura P., Shen, Yu-Lin, Boyce, Brad L.

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Automated high-throughput tensile testing reveals stochastic process parameter sensitivity by 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.

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The third Sandia fracture challenge: predictions of ductile fracture in additively manufactured metal by Kramer, Sharlotte L. B., Jones, Amanda, Mostafa, Ahmed, Ravaji, Babak, Tancogne-Dejean, Thomas, Roth, Christian C., Bandpay, Maysam Gorji, Pack, Keunhwan, Foster, John T., Behzadinasab, Masoud, Sobotka, James C., McFarland, John M., Stein, Jeremy, Spear, Ashley D., Newell, Pania, Czabaj, Michael W., Williams, Bruce, Simha, Hari, Gesing, Mark, Gilkey, Lindsay N., Jones, Christopher A., Dingreville, Remi, Sanborn, Scott E., Bignell, John L., Cerrone, Albert R., Keim, Vincent, Nonn, Aida, Cooreman, Steven, Thibaux, Philippe, Ames, Nicoli, Connor, Devin O., Parno, Matthew, Davis, Brett, Tucker, Joseph, Coudrillier, Baptiste, Karlson, Kyle N., Ostien, Jakob T., Foulk, James W., Hammetter, Christopher I., Grange, Spencer, Emery, John M., Brown, Judith A., Bishop, Joseph E., Johnson, Kyle L., Ford, Kurtis R., Brinckmann, Steffen, Neilsen, Michael K., Jackiewicz, Jacek, Ravi-Chandar, K., Ivanoff, Thomas, Salzbrenner, Bradley C., Boyce, Brad L.

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Engineering the Quantum Scientific Computing Open User Testbed by Clark, Susan M., Lobser, Daniel, Revelle, Melissa C., Yale, Christopher G., Bossert, David, Burch, Ashlyn D., Chow, Matthew N., Hogle, Craig W., Ivory, Megan, Pehr, Jessica, Salzbrenner, Bradley, Stick, Daniel, Sweatt, William, Wilson, Joshua M., Winrow, Edward, Maunz, Peter

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Extreme‐Value Statistics Reveal Rare Failure‐Critical Defects in Additive Manufacturing by Boyce, Brad L., Salzbrenner, Bradley C., Rodelas, Jeffrey M., Swiler, Laura P., Madison, Jonathan D., Jared, Bradley H., Shen, Yu‐Lin

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Corroborating tomographic defect metrics with mechanical response in an additively manufactured precipitation-hardened stainless steel by Madison, Jonathan D., Underwood, Olivia D., Swiler, Laura P., Boyce, Brad L., Jared, Bradley H., Rodelas, Jeff M., Salzbrenner, Bradley C.

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Automated high-throughput tensile testing reveals stochastic process parameter sensitivity by 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.

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High-throughput stochastic tensile performance of additively manufactured stainless steel by Salzbrenner, Bradley C., Rodelas, Jeffrey M., Madison, Jonathan D., Jared, Bradley Howell, Swiler, Laura Painton, Shen, Yu -Lin, Boyce, Brad L.

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Engineering the Quantum Scientific Computing Open User Testbed by Clark, Susan M., Lobser, Daniel, Revelle, Melissa C., Yale, Christopher G., Bossert, David, Burch, Ashlyn D., Chow, Matthew N., Hogle, Craig W., Ivory, Megan, Pehr, Jessica, Salzbrenner, Bradley, Stick, Daniel, Sweatt, William, Wilson, Joshua M., Winrow, Edward, Maunz, Peter

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High Throughput Stochastic Tensile Performance of Additively Manufactured Stainless Steels by Salzbrenner, Bradley, Boyce, Brad Lee, Carroll, Jay, Rodelas, Jeffrey, Shen, Yu-Lin, Madison, Jonathan D, Jared, Bradley Howell, Swiler, Laura Painton

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Engineering the Quantum Scientific Computing Open User Testbed (QSCOUT): Design details and user guide by Clark, Susan M, Lobser, Daniel, Revelle, Melissa, Yale, Christopher G, Bossert, David, Burch, Ashlyn D, Chow, Matthew N, Hogle, Craig W, Ivory, Megan, Pehr, Jessica, Salzbrenner, Bradley, Stick, Daniel, Sweatt, William, Wilson, Joshua M, Winrow, Edward, Maunz, Peter

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ThermoPore: Predicting part porosity based on thermal images using deep learning by Pak, Peter, Ogoke, Francis, Polonsky, Andrew, Garland, Anthony, Bolintineanu, Dan S., Moser, Dan R., Arnhart, Mary, Madison, Jonathan, Ivanoff, Thomas, Mitchell, John, Jared, Bradley, Salzbrenner, Brad, Heiden, Michael J., Barati Farimani, Amir

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