Summary:
In the field of design for additive manufacturing (AM), our research has focused on establishing process-driven design guidelines and process-aware design optimization and design exploration techniques. We have established a framework, based on machine learning classification algorithms, to evaluate the manufacturability of architected materials and structures. Specifically, we account for anticipated AM-induced variability in geometric features and mechanical properties and use that information to determine whether the performance of a candidate architecture is robust to AM-induced variability.
In collaboration with Sandia National Laboratory and Lawrence Livermore National Laboratory, we have explored the size- and orientation-dependence of mechanical properties of architected lattice materials. The findings help researchers understand the large errors often observed in mechanical property predictions of additively manufactured lattice structures, and even more importantly, how to correct for them. In ongoing work, we are currently incorporating that information into next-generation process-aware design tools for additively manufactured lattice structures. These approaches allow designers to take into account the orientation- and size-dependent mechanical properties observed in lattice structures from the very beginning of the design process, thereby realizing more defect-tolerant lattice structures and additively manufactured parts.
As part of an America Makes project, our research group established one of the first publicly available statistical databases of design allowables (minimum feature sizes, tolerances, etc.) for an AM process. The design guidelines enable designers to build AM process-specific capabilities and constraints into their parts as they design them. Together with collaborators at a local service bureau, we designed a series of test parts to investigate the geometric accuracy and resolution of parts fabricated with selective laser sintering (SLS). We fabricated hundreds of test parts to build a statistical database of geometric accuracy and resolution measurements as a function of machine, material, part orientation, and other important parameters. For the first time, designers can statistically tolerance AM parts using publicly available information.
Collaborators:
Michael Haberman, UT Austin, Applied Research Laboratories
Desi Kovar, UT Austin
Preston Wilson, UT Austin
David Bourell, UT Austin
Sandia National Laboratories
HRL Laboratories
Lawrence Livermore National Laboratory
Funding:
Sandia National Laboratories, DARPA, NSF, America Makes, NSF
Related Publications:
Process-Aware Design for AM
Wiest, T., C.C. Seepersad, M.R. Haberman, 2022, “Robust Design of an Asymmetrically Absorbing Willis Acoustic Metasurface Subject to Manufacturing-Induced Dimensional Variations,” Journal of the Acoustical Society of America, vol. 151, pp. 216-231.
Sharpe, C., C.C. Seepersad, 2021, “Lattice Structure Optimization with Orientation-Dependent Material Properties,” Journal of Mechanical Design, Vol. 143, pp. 091708 (10 pages).
Zhang, J., C. Sharpe, C.C. Seepersad, 2020, “Stress-Constrained Design of Functionally Graded Lattice Structures with Spline-Based Dimensionality Reduction,” Journal of Mechanical Design, Vol. 142, No. 9, pp. 091702 (13 pages).
Dressler, A.D., E.W. Jost, J.C. Miers, D.G. Moore, C.C. Seepersad, B.L. Boyce, 2019, “Heterogeneities Dominate Mechanical Performance of Additively Manufactured Metal Lattice Struts,” Additive Manufacturing, Vol. 28, pp. 692-703.
Morris, C., L. Bekker, C. Spadaccini, M. Haberman, C.C. Seepersad, 2019, “Tunable Mechanical Metamaterial with Constrained Negative Stiffness for Improved Quasi-static and Dynamic Energy Dissipation,” Advanced Engineering Materials, Vol. 21, No. 7, p. 1900163.
Morris, C., J. Cormack, M.R. Haberman, M. Hamilton, C.C. Seepersad, 2018, “Ultrasonic Characterization of the Complex Young’s Modulus of Polymer Parts Fabricated with Microstereolithography,” Rapid Prototyping Journal, Vol. 24, No. 7, pp. 1193-1202.
Designer's Guides for Selective Laser Sintering
Allison, J., Sharpe, C. and Seepersad, C.C., 2019, “Powder Bed Fusion Metrology for Additive Manufacturing Design Guidance,” Additive Manufacturing, Vol. 25, pp.239-251.
Allison, J., C. Sharpe, C.C. Seepersad, 2017, “A Test Part for Evaluating the Accuracy and Resolution of a Polymer Powder Bed Fusion Process,” Journal of Mechanical Design, in press.
Seepersad, C.C., T. Govett, K. Kim, M. Lundin, D. Pinero, 2012, "A Designer's Guide for Dimensioning and Tolerancing SLS Parts," Solid Freeform Fabrication Symposium, (D. L. Bourell, J. J. Beaman, R. H. Crawford, H. L. Marcus, and C. C. Seepersad, Eds.), Austin, TX.
Telenko, C. and C.C. Seepersad, 2012, "A Comparison of the Energy Efficiency of Selective Laser Sintering and Injection Molding of Nylon Parts," Rapid Prototyping Journal, Vol. 18, No. 6, pp. 472-481.