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Harnessing Legacy Data to Educate Data-Enabled Structural Materials Engineers

Published online by Cambridge University Press:  24 February 2020

Jennifer L.W. Carter ,
Amit K. Verma  and
Nishan M. Senanayake
Jennifer L.W. Carter*
Affiliation:
Case Western Reserve University, Department of Materials Science and Engineering, Cleveland OH, 44106, USA, [email protected]
Amit K. Verma
Affiliation:
Case Western Reserve University, Department of Materials Science and Engineering, Cleveland OH, 44106, USA, [email protected] Carnegie Mellon University, Department of Materials Science and Engineering, Pittsburgh, PA, 15213, USA
Nishan M. Senanayake
Affiliation:
Case Western Reserve University, Department of Materials Science and Engineering, Cleveland OH, 44106, USA, [email protected]
*
*(Email: [email protected])
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Abstract

Data-driven materials design informed by legacy data-sets can enable the education of a new workforce, promote openness of the scientific process in the community, and advance our physical understanding of complex material systems. The performance of structural materials, which are controlled by competing factors of composition, grain size, particle size/distribution, residual strain, cannot be modelled with single-mechanism physics. The design of optimal processing route must account for the coupled nature of the creation of such factors, and requires students to learn machine learning and statistical modelling principles not taught in the conventional undergraduate or graduate level Materials Science and Engineering curricula. Therefore, modified curricula with opportunities for experiential learning are paramount for workforce development. Projects with real-world data provide an opportunity for students to establish fluency in the iterative steps needed to solve relevant scientific and engineering process design questions.

Keywords

educationinformaticsalloysteel
Type
Articles
Information
MRS Advances , Volume 5 , Issue 7: BROADER IMPACT , 2020 , pp. 319 - 327
Copyright
Copyright © Materials Research Society 2020

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References

References:

Greene, L., Lubensky, T., Tirrell, M., Chaikin, P., Ding, H., Faber, K., and Jones, B., Frontiers of Materials Research: A Decadal Survey (No. DOE-NASEM-16257) (2019).CrossRefGoogle Scholar
Dimiduk, D. M., Holm, E. A., and Niezgoda, S. R., Integr Mater Manuf Innov 7 (3), 157–172 (2018).CrossRefGoogle Scholar
Holdren, J. P., “Materials Genome Initiative for Global Competitiveness,” National Science and Technology Council, Washington, D.C., (2011).Google Scholar
Rowe, M., “Boomers Retire, Knowledge Goes with Them,” EETimes (2018). Available at: https://www.eetimes.com/boomers-retire-knowledge-goes-with-them/. [Accessed: 08-Dec-2019].Google Scholar
The Minerals Metals & Materials Society (TMS), Advanced Computation and Data in Materials and Manufacturing: Core Knowledge Gaps and Opportunities. Pittsburgh (2018).Google Scholar
Himanen, L., Geurts, A., Foster, A. S., and Rinke, P., Advanced Science 6(21), 2019.Google Scholar
Schenck, P. K., Klamo, J. L., Bassim, N. D., Burke, P. G., Gerbig, Y. B., and Green, M. L., Thin Solid Films 517(2), 691–694 (2008).CrossRefGoogle Scholar
Ding, S. et al, Nature Materials 13(5), 494–500 (2014).CrossRefGoogle Scholar
Potyrailo, R., Rajan, K., Stoewe, K., Takeuchi, I., Chisholm, B., and Lam, H., ACS Comb. Sci. 13(6), 579–633 (2011).CrossRefGoogle Scholar
Rajan, K., Electron Backscatter Diffraction in Materials Science, 189–199 (2009)CrossRefGoogle Scholar
Kanno, S., Imamura, Y., and Hada, M., Phys. Rev. Materials 3(7), 075403(2019).CrossRefGoogle Scholar
Ahmad, Z., Xie, T., Maheshwari, C., Grossman, J. C., and Viswanathan, V., ACS Cent. Sci. 4(8), 996–1006 (2018).CrossRefGoogle Scholar
Balachandran, P. V., Kowalski, B., Sehirlioglu, A., and Lookman, T., Nature Communications 9(1), 1668 (2018).CrossRefGoogle Scholar
Miracle, D., Majumdar, B., Wertz, K., and Gorsse, S., Scripta Materialia 127, 195–200 (2017).CrossRefGoogle Scholar
“ASTM E606/E606M: Standard Method for Strain-Controlled Fatigue Testing,” ASTM International, 2012.Google Scholar
E28 Committee, “ASTM-E139: Test Methods for Conducting Creep, Creep-Rupture, and Stress-Rupture Tests of Metallic Materials,” ASTM International, 2011.Google Scholar
Engineering | Data USA.” [Online]. Available at: https://datausa.io/profile/cip/engineering#employment. (Accessed: 08-Dec-2019).Google Scholar
Bureau, U. C., “Data,” Census.gov. Available: https://www.census.gov/data.html. (Accessed: 08-Dec-2019).Google Scholar
Ellis, D. L., Carter, J. L. W., and Ferry, M. H., Materials Science and Engineering: A 640, 1–15, (2015).CrossRefGoogle Scholar
Verma, A. K., Hawk, J. A., Bruckman, L. S., French, R. H., Romanov, V. N., and Carter, J. L. W., Metallurgical and Materials Transactions A 50 (7), 3106–3120 (2019).CrossRefGoogle Scholar
Clinton, J. A., Morrison, R. L., and Carter, J. L. W., Metal and Mat Trans A 48(7), 3220–3230 (2017).CrossRefGoogle Scholar
Burwell, S. M., VanRoekel, S., Park, T., and Mancini, D. J., “Open Data Policy-Managing Information as an Asset,” Executive Office of the President: Office of Management and Budget, Memorandum, 2013.Google Scholar
Vought, R. T., “Phase 1 Implementation of the Foundations for Evidence-Based Policymaking Act of 2018: Learning Adendas, Personnel, and Planning Guidance,” Executive Office of the President: Office of Management and Budget, Memorandum, 2019.Google Scholar
National Academy of Engineering, “NAE Grand Challenges for Engineering.”. Available at: http://www.engineeringchallenges.org/challenges.aspx. (Accessed: 11-Dec-2019).Google Scholar
Puchala, B., Tarcea, G., Marquis, Emmanuelle. A., Hedstrom, M., Jagadish, H. V., and Allison, J. E., JOM 68(8), 2035–2044, (2016).CrossRefGoogle Scholar
Cardenas, D. M., “An Implementation of Electronic Laboratory Notebooks (ELN) Using a Learning Management System Platform in an Undergraduate Experimental Engineering Course,” presented at the 121st ASEE Annual Conference & Exposition, 16(2014).Google Scholar
Bowman, J. S., Emerson, S. L., and Darnovsky, M., The Practical SQL Handbook: Using Structured Query Language, 3rd ed. (Addison-Wesley Longman Publishing Co., Boston, MA,1996).Google Scholar
Hadoop, : The Definitive Guide.Google Scholar
Börner, K., Bueckle, A., and Ginda, M., PNAS 116(6), 1857–1864 (2019).CrossRefGoogle Scholar
Emerson, J.W. et al. , Journal of Computational and Graphical Statistics 22(1),79–91(2013).CrossRefGoogle Scholar
Hinton, G. and Sejnowski, T. J., Eds., Unsupervised Learning: Foundations of Neural Computation, 1st edition. (MIT press, 1999).CrossRefGoogle Scholar
DellaPietra, S., DellaPietra, V., and Lafferty, J., IEEE Trans. Pattern Anal. Mach. Intell. 19(4), 380–393 (1997).CrossRefGoogle Scholar
Dalgaard, P., Introductory Statistics with R, 2nd ed.Springer, 2008.CrossRefGoogle Scholar
Weston, L. and Stampfl, C., Phys. Rev. Materials 2(8), 085407 (2018).CrossRefGoogle Scholar
Zhou, Q., Tang, P., Liu, S., Pan, J., Yan, Q., and Zhang, S.-C., Proc Natl Acad Sci U S A, vol. 115, no. 28, pp. E6411–E6417, Jul. 2018.CrossRefGoogle Scholar
Kolb, M. et al., “On the grain boundary strengthening effect of boron in γ/γ′ Cobalt-base superalloys,” Acta Materialia, vol. 145, pp. 247–254, Feb. 2018.CrossRefGoogle Scholar
Verma, A. K. et al., Materials Science and Engineering: A 793 (19), 138142 (2019).CrossRefGoogle Scholar
Huang, W.H. et al., netSEM: Network Structural Equation Modelling. 2018.Google Scholar
Verma, A. K., French, R. H., and Carter, J. L. W., Integrating Materials and Manufacturing Innovation 6(4), 279–287 (2017).CrossRefGoogle Scholar
Narendra, P. M. and Fukunaga, K., IEEE Transactions on Computers 9 (C–26), 917–922 (1977).CrossRefGoogle Scholar
Breiman, L., Friedman, J., Stone, C. J., and Olshen, R. A., Classification and Regression Trees, 1st ed. (Boca Raton: Chapman and Hall/CRC, 1984).Google Scholar
Liaw, A. and Wiener, M., R news, 2(3), 18-22(2002).Google Scholar
Gastelum, J. C. V., Strachan, A., and Desai, S., Machine Learning for Materials Science: Part 1. (2019).Google Scholar
Gastelum, J. C. V. and Strachan, A., Citrine Tools for Materials Informatics. (2019).Google Scholar
R: The R Stats Package.”. Available at: http://stat.ethz.ch/R-manual/R-patched/library/stats/html/00Index.html. (Accessed: 19-Jan-2016).Google Scholar
Calculating the PRESS statistic in R,” ecology & stats, 18-Jun-2013.Google Scholar