Integrated design of negative stiffness honeycomb structures considering performance and operational degradation
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Abstract
This study introduces an integrated framework for conceptualizing the design of negative stiffness honeycomb (NSH) structures, specifically considering the durability and performance of their unit cells. Unlike conventional energy-absorbing structures that rely on plastic deformation, NSH offers a promising alternative for reusable energy absorption (EA) and high initial stiffness, making it suitable for a wide range of engineering applications. The research considers the variability in characteristics of NSH based on the shape of the configured negative stiffness beam (NSB), selecting a single curved-beam unit cell as the focal point. Extensive testing, including quasi-static and cyclic compression tests, is conducted on NSH unit cell fabricated using polylactic acid/polyhydroxy alkenoate (PLA/PHA) filament, to analyze performance under stress and to assess degradation over time. Central to the study is the use of multi-objective optimization (MOO) to explore the trade-off between performance and operational durability, thereby emphasizing the significance of degradation in the design process. The results demonstrate the potential for NSH structures, particularly in terms of their reusability and efficiency, highlighting the viability of incorporating durability considerations in the early stages of design, especially for structures intended for additive manufacturing processes.
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Negative Stiffness Honeycomb, Multi-Objective Optimization, Additive Manufacturing, Design Considering Degradation
Correa, D.M., Klatt, T., Cortes, S., Haberman, M., Kovar, D. & Seepersad, C. (2015), Negative stiffness honeycombs for recoverable shock isolation, Rapid Prototyping Journal, Vol. 21 No. 2, pp. 193-200. Doi: 10.1108/RPJ-12-2014-0182
Correa, D.M., Seepersad, C.C. & Haberman, M.R. (2015), Mechanical design of negative stiffness honeycomb materials. Integr Mater Manuf Innov 4, 165–175. doi: 10.1186/s40192-015-0038-8
Deb, K., Agrawal, S., Pratap, A., & Meyarivan, T. (2000). A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II. In Parallel Problem Solving from Nature PPSN VI: 6th International Conference, September 18–20, Paris, France. doi:10.1007/3-540-45356-3_83
Debeau, D. A., Seepersad, C. C., & Haberman, M. R. (2018). Impact behavior of negative stiffness honeycomb materials. Journal of Materials Research, 33(3), 290–299. doi:10.1557/jmr.2018.7
Gonabadi, H., Yadav, A. & Bull, S.J. (2020). The effect of processing parameters on the mechanical characteristics of PLA produced by a 3D FFF printer. Int J Adv Manuf Technol 111, 695–709. doi:10.1007/s00170-020-06138-4
Ha, C. S., Lakes, R. S., & Plesha, M. E. (2019). Cubic negative stiffness lattice structure for energy absorption: Numerical and experimental studies. International Journal of Solids and Structures, 178, 127-135. doi: 10.1016/j.ijsolstr.2019.06.024.
Klatt, T. Michael, H. Seepersad, C.C. (2013), Selective Laser Sintering of Negative Stiffness Mesostructures for Recoverable, Nearly-Ideal Shock Isolation, 2013 cInternational SFF Symposium, August 12-14, Austin, doi:10.26153/tsw/15653
Kim, N. H., An, D., & Choi, J. H. (2017). Prognostics and health management of engineering systems. Switzerland: Springer International Publishing.
Letcher, T, & Waytashek, M. (2014) "Material Property Testing of 3D-Printed Specimen in PLA on an Entry-Level 3D Printer." Proceedings of the ASME 2014 International Mechanical Engineering Congress and Exposition. November 14–20, Montreal, Quebec, Canada. doi:10.1115/IMECE2014-39379
Liu, F., Jiang, X., Wang, X., & Wang, L. (2020). Machine learning-based design and optimization of curved beams for multistable structures and metamaterials. Extreme Mechanics Letters, 41, 101002. doi: 10.1016/j.eml.2020.101002
Liu, Y., Jiang, W., Hu, W., Ren, L., Deng, E., Wang, Y., Song, C. & Feng, Q. (2023). Compressive strength and energy absorption characteristics of the negative stiffness honeycomb cell structure. Materials Today Communications, 35, 105498. doi: 10.1016/j.mtcomm.2023.105498
Matthews, J., Klatt, T., Morris, C., Seepersad, C. C., Haberman, M. & Shahan, D. (2016). Hierarchical Design of Negative Stiffness Metamaterials Using a Bayesian Network Classifier. ASME. J. Mech. Des. 138(4): 041404. doi:10.1115/1.4032774
Morris, C., Bekker, L., Haberman, M. R. & Seepersad, C. C. (2018). Design Exploration of Reliably Manufacturable Materials and Structures With Applications to Negative Stiffness Metamaterials and Microstereolithography. ASME. J. Mech. Des. 140(11): 111415. doi:10.1115/1.4041251
Rasmussen, C. E. & Williams, C. K. (2006). Gaussian processes for machine learning. Cambridge, MA: MIT press.
Shahan, D. W. & Seepersad, C. C. (2012). Bayesian Network Classifiers for Set-Based Collaborative Design. ASME. J. Mech. Des. 134(7): 071001. doi:10.1115/1.4006323
Shan, S., Kang, S.H., Raney, J.R., Wang, P., Fang, L., Candido, F., Lewis, J.A. & Bertoldi, K. (2015), Multistable Architected Materials for Trapping Elastic Strain Energy. Adv. Mater., 27: 4296-4301. doi:10.1002/adma.201501708
Scott, D. W. (2015). Multivariate density estimation: theory, practice, and visualization. John Wiley & Sons.
Tan, X., Chen, S., Zhu, S., Wang, B., Xu, P., Yao, K., & Sun, Y. (2019). Reusable metamaterial via inelastic instability for energy absorption. International Journal of Mechanical Sciences, 155, 509-517. doi: 10.1016/j.ijmecsci.2019.02.011
Wang, B., Tan, X., Zhu, S., Chen, S., Yao, K., Xu, P., Wang, L., Wu, H. & Sun, Y. (2019). Cushion performance of cylindrical negative stiffness structures: Analysis and optimization. Composite Structures, 227, 111276. doi: 10.1016/j.compstruct.2019.111276
Xu, H. (2020). Constructing Oscillating Function-Based Covariance Matrix to Allow Negative Correlations in Gaussian Random Field Models for Uncertainty Quantification. ASME. J. Mech. Des. 142(7): 074501. doi:10.1115/1.4046067
Zhakatayev, A., Kappassov, Z., & Varol, H. A. (2020). Analytical modeling and design of negative stiffness honeycombs. Smart Materials and Structures, 29(4), 045024. doi:10.1088/1361-665X/ab773a
Zouaoui, M., Gardan, J., Lafon, P., Makke, A., Labergere, C., & Recho, N. (2021). A finite element method to predict the mechanical behavior of a pre-structured material manufactured by fused filament fabrication in 3D printing. Applied Sciences, 11(11), 5075. doi:10.3390/app11115075
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