Age-aware constitutive materials model for a 3D printed polymeric foam

• Published in: Scientific reports Vol. 9; no. 1; pp. 15923 - 8
• Main Authors: Maiti, A., Small, W., Lewicki, J. P., Chinn, S. C., Wilson, T. S., Saab, A. P.
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 04.11.2019; Nature Publishing Group
• Subjects: 639/301/1023/303; 639/301/923/1028; Aging; Compression; Elastomers; Foams; Humanities and Social Sciences; MATERIALS SCIENCE; multidisciplinary; Science; Science (multidisciplinary); Stochasticity
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-019-52298-z

Traditional open or closed-cell stochastic elastomeric foams have wide-ranging applications in numerous industries: from thermal insulation, shock absorbing/gap-filling support cushions, packaging, to light-weight structural and positional components. Recent developments in 3D printing technologies by direct ink-write have opened the possibility of replacing stochastic foam parts by more controlled printed micro-structures with superior stress-distribution and longer functional life. For successful deployment as mechanical support or structural components, it is crucial to characterize the response of such printed materials to long-term external loads in terms of stress-strain behavior evolution and in terms of irreversible structural and load-bearing capacity changes over time. To this end, here we report a thermal-age-aware constitutive model for a 3D printed close-packed foam structure under compression. The model is based on the Ogden hyperfoam strain-energy functional within the framework of Tobolsky two-network scheme. It accurately describes experimentally measured stress-strain response, compression set, and load retention for various aging times and temperatures. Through the technique of time-temperature-superposition the model enables the prediction of long-term changes along with the quantification of uncertainty stemming from sample-to-sample variation and measurement noise. All aging parameters appear to possess the same Arrhenius activation barrier, which suggests a single dominant aging mechanism at the molecular/network level.