0
Review Article

Interdisciplinary Research on Designing Engineering Material Systems: Results From a National Science Foundation Workshop

[+] Author and Article Information
Raymundo Arroyave

Professor
Department of Materials Science and Eng.,
Texas A&M University,
College Station, TX 77843
e-mail: rarroyave@tamu.edu

Samantha Shields

Teaching, Learning and Culture Department,
Texas A&M University,
College Station, TX 77843

Chi-Ning Chang

Educational Psychology Department,
Texas A&M University,
College Station, TX 77843

Debra Fowler

Director
Center for Teaching Excellence,
Texas A&M University,
College Station, TX 77843

Richard Malak

Department of Mechanical Eng.,
Texas A&M University,
College Station, TX 77843
e-mail: rmalak@tamu.edu

Douglas Allaire

Department of Mechanical Eng.,
Texas A&M University,
College Station, TX 77843
e-mail: dallaire@tamu.edu

1Corresponding author.

Contributed by the Design Engineering Division of ASME for publication in the JOURNAL OF MECHANICAL DESIGN. Manuscript received April 17, 2018; final manuscript received August 6, 2018; published online September 10, 2018. Editor: Wei Chen.

J. Mech. Des 140(11), 110801 (Sep 10, 2018) (9 pages) Paper No: MD-18-1319; doi: 10.1115/1.4041177 History: Received April 17, 2018; Revised August 06, 2018

We present the results from a workshop on interdisciplinary research on design of engineering material systems, sponsored by the National Science Foundation. The workshop was prompted by the need to foster a culture of interdisciplinary collaboration between the engineering design and materials communities. The workshop addressed the following: (i) conceptual barriers between materials and engineering design research communities; (ii) research questions that the interdisciplinary field of materials design should focus on; (iii) processes and metrics to be used to validate research activities and outcomes on materials design; and (iv) strategies to sustain and grow the interdisciplinary field. This contribution presents a summary of the state of the field—elicited through extensive guided discussions between representatives of both communities—and a snapshot of research activities that have emerged since the workshop. Based on the increasing level of sophistication of interdisciplinary research programs on design of materials it is apparent that the field is growing and has great potential to play a key role in a vibrant interdisciplinary materials innovation ecosystem. Sustaining such efforts will contribute significantly to the advancement of technologies that will impact many industries and will enhance society-wide health, security, and economic well-being.

FIGURES IN THIS ARTICLE
<>
Copyright © 2018 by ASME
Your Session has timed out. Please sign back in to continue.

References

Olson, G. B. , 1997, “ Computational Design of Hierarchically Structured Materials,” Science, 277(5330), pp. 1237–1242. [CrossRef]
Olson, G. B. , 2000, “ Designing a New Material World,” Science, 288(5468), pp. 993–998. [CrossRef]
Seepersad, C. , Fernandez, M. , Panchal, J. , Choi, H. , Allen, J. , McDowell, D. , and Mistree, F. , 2004, “ Foundations for a Systems-Based Approach for Materials Design,” AIAA Paper No. 2004-4300.
McDowell, D. L. , Panchal, J. , Choi, H.-J. , Seepersad, C. , Allen, J. , and Mistree, F. , 2009, Integrated Design of Multiscale, Multifunctional Materials and Products, Butterworth-Heinemann, Burlington, MA.
Horstemeyer, M. F. , 2000, “ From Atoms to Autos: A New Design Paradigm Using Microstructure-Property Modeling—Part 1: Monotonic Loading Conditions,” Sandia National Laboratories, Albuquerque, NM, Technical Report No. SAND2000-8662. https://digital.library.unt.edu/ark:/67531/metadc742598/
Horstemeyer, M. F. , McDowell, D. L. , and Fan, J. , 2000, “ From Atoms to Autos: A New Design Paradigm Using Microstructure-Property Modeling—Part 2: Cyclic Fatigue,” Sandia National Laboratories, Albuquerque, NM, Technical Report No. SAND2000-8661. https://prod.sandia.gov/techlib-noauth/access-control.cgi/2000/008661.pdf
Holdren, J. P. , and ad hoc Group of the National Science and Technology Council, 2011, Materials Genome Initiative for Global Competitiveness, National Science and Technology Council OSTP, National Science and Technology Council, Washington, DC.
Scott, T. , Walsh, A. , and Anderson, B. , O'Connor, A., 2018, “ Economic Analysis of National Needs for Technology Infrastructure to Support the Materials Genome Initiative,” Technology Partnerships Office, Office of Innovation and Industry National Institute of Standards and Technology U.S. Department of Commerce Gaithersburg, MD, accessed June 19, 2018 https://mgi.nist.gov/sites/default/files/uploads/\\MGI\_Econ\_Analysis.pdf
Allison, J. , 2011, “ Integrated Computational Materials Engineering: A Perspective on Progress and Future Steps,” JOM, 63(4), pp. 15–18. [CrossRef]
Glamm, R. J. , Rosenbladt, D. M. , Pripstein, E. D. , and Cotton, J. D. , 2015, “ Recent Progress in Implementation of ICME for Metallic Materials in the Airframe Industry,” AIAA Paper No. AIAA 2015-0199.
Howe, D. , Goodlet, B. , Weaver, J. , and Spanos, G. , 2016, “ Insights From the 3rd World Congress on Integrated Computational Materials Engineering,” JOM, 68(5), pp. 1378–1384. [CrossRef]
OMara, J. , Meredig, B. , and Michel, K. , 2016, “ Materials Data Infrastructure: A Case Study of the Citrination Platform to Examine Data Import, Storage, and Access,” JOM, 68(8), pp. 2031–2034. [CrossRef]
Hill, J. , Mulholland, G. , Persson, K. , Seshadri, R. , Wolverton, C. , and Meredig, B. , 2016, “ Materials Science With Large-Scale Data and Informatics: Unlocking New Opportunities,” MRS Bull., 41(5), pp. 399–409. [CrossRef]
Kalidindi, S. R. , Brough, D. B. , Li, S. , Cecen, A. , Blekh, A. L. , Congo, F. Y. P. , and Campbell, C. , 2016, “ Role of Materials Data Science and Informatics in Accelerated Materials Innovation,” MRS Bull., 41(8), pp. 596–602. [CrossRef]
Borrego, M. , and Newswander, L. K. , 2010, “ Definitions of Interdisciplinary Research: Toward Graduate-Level Interdisciplinary Learning Outcomes,” Rev. Higher Educ., 34(1), pp. 61–84. [CrossRef]
Choi, H. , McDowell, D. L. , Allen, J. K. , Rosen, D. , and Mistree, F. , 2008, “ An Inductive Design Exploration Method for Robust Multiscale Materials Design,” ASME J. Mech. Des., 130(3), p. 031402. [CrossRef]
Xu, H. , Li, Y. , Brinson, C. , and Chen, W. , 2014, “ A Descriptor-Based Design Methodology for Developing Heterogeneous Microstructural Materials System,” ASME J. Mech. Des., 136(5), p. 051007. [CrossRef]
Hazelrigg, G. A. , 2003, “ Validation of Engineering Design Alternative Selection Methods,” Eng. Optim., 35(2), pp. 103–210. [CrossRef]
Seepersad, C. C. , Pedersen, K. , Emblemsvg, J. , Bailey, R. , Allen, J. K. , and Mistree, F. , 2006, “ The Validation Square: How Does One Verify and Validate a Design Method?,” Decision Making in Engineering Design, American Society of Mechanical Engineers, New York, pp. 303–314.
Olewnik, A. T. , and Lewis, K. , 2005, “ On Validating Engineering Design Decision Support Tools,” Concurrent Eng., 13(2), pp. 111–122. [CrossRef]
Binder, W. R. , and Paredis, C. J. , 2017, “ Optimization Under Uncertainty Versus Algebraic Heuristics: A Research Method for Comparing Computational Design Methods,” ASME Paper No. DETC2017-68371.
Massoni, B. , and Campbell, M. I. , 2018, “ Optimizing Cutting Planes for Advanced Joining and Additive Manufacturing,” ASME J. Manuf. Sci. Eng., 140(3), p. 031001. [CrossRef]
Galvan, E. , Malak, R. J. , Gibbons, S. , and Arroyave, R. , 2017, “ A Constraint Satisfaction Algorithm for the Generalized Inverse Phase Stability Problem,” ASME J. Mech. Des., 139(1), p. 011401. [CrossRef]
Nellippallil, A. B. , Shukla, R. , Ardham, S. , Goh, C.-H. , Allen, J. K. , and Mistree, F. , 2017, “ Exploration of Solution Space to Study Thermo-Mechanical Behavior of AA5083 Al-Alloy During Hot Rolling Process,” ASME Paper No. DETC2017-68173.
Xue, D. , Balachandran, P. V. , Hogden, J. , Theiler, J. , Xue, D. , and Lookman, T. , 2016, “ Accelerated Search for Materials With Targeted Properties by Adaptive Design,” Nat. Commun., 7, p. 11241. [CrossRef] [PubMed]
Bessa, M. , Bostanabad, R. , Liu, Z. , Hu, A. , Apley, D. W. , Brinson, C. , Chen, W. , and Liu, W. K. , 2017, “ A Framework for Data-Driven Analysis of Materials Under Uncertainty: Countering the Curse of Dimensionality,” Comput. Methods Appl. Mech. Eng., 320, pp. 633–667. [CrossRef]
Jin, R. , Chen, W. , and Simpson, T. W. , 2001, “ Comparative Studies of Metamodelling Techniques Under Multiple Modelling Criteria,” Struct. Multidiscip. Optim., 23(1), pp. 1–13. [CrossRef]
Yeten, B. , Castellini, A. , Guyaguler, B. , and Chen, W. , 2005, “ A Comparison Study on Experimental Design and Response Surface Methodologies,” SPE Reservoir Simulation Symposium, The Woodlands, TX, Jan. 31–Feb. 2.
Galvan, E. , Hsiao, C. , Vermillion, S. , and Malak, R. , 2015, “ A Parallel Approach for Computing the Expected Value of Gathering Information,” SAE Int. J. Mater. Manuf., 8(2), pp. 271–282. https://www.jstor.org/stable/26268713
Moore, R. A. , Romero, D. A. , and Paredis, C. J. , 2014, “ Value-Based Global Optimization,” ASME J. Mech. Des., 136(4), p. 041003. [CrossRef]
Messer, M. , Panchal, J. H. , Krishnamurthy, V. , Klein, B. , Yoder, P. D. , Allen, J. K. , and Mistree, F. , 2010, “ Model Selection Under Limited Information Using a Value-of-Information-Based Indicator,” ASME J. Mech. Des., 132(12), p. 121008. [CrossRef]
Sadoughi, M. K. , Li, M. , Hu, C. , and Mackenzie, C. A. , 2017, “ High-Dimensional Reliability Analysis of Engineered Systems Involving Computationally Expensive Black-Box Simulations,” ASME Paper No. DETC2017-68273.
Hajikolaei, K. H. , and Wang, G. G. , 2014, “ High Dimensional Model Representation With Principal Component Analysis,” ASME J. Mech. Des., 136(1), p. 011003. [CrossRef]
Sivapuram, R. , Dunning, P. D. , and Kim, H. A. , 2016, “ Simultaneous Material and Structural Optimization by Multiscale Topology Optimization,” Struct. Multidiscip. Optim., 54(5), pp. 1267–1281. [CrossRef]
Hasan, M. F. , First, E. L. , and Floudas, C. A. , 2017, “ Discovery of Novel Zeolites and Multi-Zeolite Processes for p-Xylene Separation Using Simulated Moving Bed (Smb) Chromatography,” Chem. Eng. Sci., 159, pp. 3–17. [CrossRef]
Lee, Y. H. , Schuh, J. K. , Ewoldt, R. H. , and Allison, J. T. , 2018, “ Simultaneous Design of Non-Newtonian Lubricant and Surface Texture Using Surrogate-Based Optimization,” AIAA Paper No. AIAA 2018-1906.
Pfeifer, S. , Wodo, O. , and Ganapathysubramanian, B. , 2018, “ An Optimization Approach to Identify Processing Pathways for Achieving Tailored Thin Film Morphologies,” Comput. Mater. Sci., 143, pp. 486–496. [CrossRef]
Hartl, D. J. , Kiefer, B. , Schulte, R. , and Menzel, A. , 2017, “ Computationally-Efficient Modeling of Inelastic Single Crystal Responses Via Anisotropic Yield Surfaces: Applications to Shape Memory Alloys,” Int. J. Solids Struct., 136–137, pp. 38–59.
Friedrich, L. , Collino, R. , Ray, T. , and Begley, M. , 2017, “ Acoustic Control of Microstructures During Direct Ink Writing of Two-Phase Materials,” Sens. Actuators A: Phys., 268, pp. 213–221. [CrossRef]
Cang, R. , Xu, Y. , Chen, S. , Liu, Y. , Jiao, Y. , and Ren, M. Y. , 2017, “ Microstructure Representation and Reconstruction of Heterogeneous Materials Via Deep Belief Network for Computational Material Design,” ASME J. Mech. Des., 139(7), p. 071404. [CrossRef]
Xiao, W. , Li, Y. , and Wang, P. , 2018, “ Uncertainty Quantification of Atomistic Materials Simulation With Machine Learning Potentials,” AIAA Paper No. AIAA 2018-2166.
Cecen, A. , Dai, H. , Yabansu, Y. C. , Kalidindi, S. R. , and Song, L. , 2017, “ Material Structure-Property Linkages Using Three-Dimensional Convolutional Neural Networks,” Acta Mater., 146, pp. 76–84. [CrossRef]
Fowler, D. A. , Arroyave, R. , Ross, J. , Malak, R. , and Banerjee, S. , 2017, “ Looking Outwards From the Central Science: An Interdisciplinary Perspective on Graduate Education in Materials Chemistry,” Educational and Outreach Projects From the Cottrell Scholars Collaborative Undergraduate and Graduate Education, Vol. 1, ACS Publications, Washington, DC, pp. 65–89.
Chang, C.-N. , Semma, B. , Pardo, M. L. , Fowler, D. , Shamberger, P. , and Arroyave, R. , 2017, “ Data-Enabled Discovery and Design of Energy Materials (D3EM): Structure of an Interdisciplinary Materials Design Graduate Program,” MRS Adv., 2(31–32), pp. 1693–1698. [CrossRef]

Figures

Grahic Jump Location
Fig. 1

Network representation of workshop participants: blue nodes correspond to capabilities of participants, while yellow nodes indicate needs or capability gaps. The relative size of the nodes corresponds to the frequency at which each of the different capabilities/capability gaps was stated by the participants.

Grahic Jump Location
Fig. 2

Flowchart of a proposed approach for evaluating design methodologies [21]

Grahic Jump Location
Fig. 3

The adaptive design loop of Xue et al. [25], used under CC-BY 4.0

Grahic Jump Location
Fig. 4

Notional depiction of multiscale topology optimization [34]. Reprinted by permission of Springer, Structural and Multidisciplinary Optimization, Copyright 2016.

Grahic Jump Location
Fig. 5

Three microstructures with clearly contrasting architectural features and their spatial statistics [42] (Reprinted with permission from Elsevier and Acta Materialia, copyright 2017)

Grahic Jump Location
Fig. 6

The three core disciplines in the interdisciplinary graduate program, D3EM, at Texas A&M University

Tables

Errata

Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging and repositioning the boxes below.

Related Journal Articles
Related eBook Content
Topic Collections

Sorry! You do not have access to this content. For assistance or to subscribe, please contact us:

  • TELEPHONE: 1-800-843-2763 (Toll-free in the USA)
  • EMAIL: asmedigitalcollection@asme.org
Sign In