Dynamic mechanical analysis (DMA) is commonly used to test the viscoelastic properties of materials. However, the frequency-dependent component of the measured properties in such techniques remains underutilized due to the lack of correlation between frequency, temperature, and strain rate. Recent studies have used a time-temperature superposition (TTS) based method to develop a frequency to time domain transformation and validated it for tensile loading conditions. This transform is very useful in reducing the experimental effort required to measure material modulus over a wide range of temperatures and strain rates. However, this transform is not yet validated on an important class of materials, closed-cell foams. The present work is focused on understanding the validity of such transforms on A polymethacrylimide based close-cell structural foam, Rohacell®. Torsional properties of these foams are important for underwater structural applications but neither these foams have been studied for such properties nor the transforms have been tested for their validity for such loading conditions. DMA was conducted under tension and torsion over 30-210°C and 0.1–10 Hz. The transformed results validated against tensile test results revealed an error <6.63% in the strain rate range of to s-1. Moreover, the shear modulus is also transformed from frequency to time domain using the same scheme. Combining machine learning, the results yield an error <4.71% in the shear strain rate of and s-1. These results show the validity of the transformation method as well as present valuable information about the mechanical properties of Rohacell.
DintilhacNLewandowskiSGevauxL, et al.Manufacturing process and annealing influence on the dynamic mechanical properties and physical structure of LLDPE thin films. Polymer2024; 302: 127070.
2.
Serrano-ArocaÁLlorens-GámezM. Dynamic mechanical analysis and water vapour sorption of highly porous poly(methyl methacrylate). Polymer2017; 125: 58–65.
3.
QazviniNTMohammadiN. Dynamic mechanical analysis of segmental relaxation in unsaturated polyester resin networks: effect of styrene content. Polymer2005; 46(21): 9088–9096.
4.
Fernández-BlázquezJPBelloAPérezE. Dynamic mechanical analysis of the two glass transitions in a thermotropic polymer. Polymer2005; 46(23): 10004–10010.
5.
DunkerleyEKoernerHVaiaRA, et al.Structure and dynamic mechanical properties of highly oriented PS/clay nanolaminates over the entire composition range. Polymer2011; 52(4): 1163–1171.
6.
LiQJinWShettyA, et al.Dynamic mechanical analysis of cementitious composites: test method optimization and materials characterization. J Mater Civ Eng2023; 35(8): 04023230.
7.
SharmaSShankarVJoshiYM. Viscoelasticity and rheological hysteresis. Journal of Rheology2023; 67(1): 139–155.
8.
CollocaMDorogokupetsGGuptaN, et al.Mechanical properties and failure mechanisms of closed-cell PVC foams. Int J Crashworthiness2012; 17(3): 327–336.
9.
LuongDDPinisettyDGuptaN. Compressive properties of closed-cell polyvinyl chloride foams at low and high strain rates: experimental investigation and critical review of state of the art. Compos B Eng2013; 44(1): 403–416.
10.
TschoeglNWKnaussWEmriI. Poisson's ratio in linear viscoelasticity – a critical review. Mech Time-Dependent Mater2002; 6: 3–51.
11.
Rodríguez AgudoJAHaeberleJMüller-PabelM, et al.Characterization of the temperature and frequency dependency of the complex Poisson’s ratio using a novel combined torsional-axial rheometer. Journal of Rheology2023; 67(6): 1221–1250.
12.
GuptaNShunmugasamyVC. High strain rate compressive response of syntactic foams: trends in mechanical properties and failure mechanisms. Mater Sci Eng, A2011; 528(25): 7596–7605.
13.
Müller-PabelMMeuchelböckJKochI, et al.On the determination of viscoelastic properties of EPP foam in dependence on pre-strain and loading direction. J Cell Plast2023; 60(2): 81–96.
14.
VirágÁDJuhászZKossaA, et al.Combining oscillatory shear rheometry and dynamic mechanical analysis to obtain wide-frequency master curves. Polymer2024; 295: 126742.
15.
ChoiJYYanamandraKShettyA, et al.Simultaneous measurement of elastic constants from dynamic mechanical analysis with digital image correlation. Polymer2022; 242: 124562.
16.
LinPLiuJZhaoZ, et al.Origin of mechanical stress and rising internal energy during fast uniaxial extension of SBR melts. Polymer2017; 124: 68–77.
17.
TsengY-CHsiehY-CChinN-Y, et al.Synthesis, thermal properties and rheological behaviors of novel poly(ethylene glycol) segmented poly(arylene ether)s. Polymer2020; 196: 122426.
18.
ZeltmannSEBharath KumarBRDoddamaniM, et al.Prediction of strain rate sensitivity of high density polyethylene using integral transform of dynamic mechanical analysis data. Polymer2016; 101: 1–6.
19.
XuXGuptaN. Determining elastic modulus from dynamic mechanical analysis data: reduction in experiments using adaptive surrogate modeling based transform. Polymer2018; 157: 166–171.
20.
PritchardRHTerentjevEM. Oscillations and damping in the fractional Maxwell materials. J Rheol2017; 61(2): 187–203.
21.
XuXKoomsonCDoddamaniM, et al.Extracting elastic modulus at different strain rates and temperatures from dynamic mechanical analysis data: a study on nanocomposites. Compos B Eng2019; 159: 346–354.
22.
XuXGuptaN. Use of machine learning methods in syntactic foam design. In: Encyclopedia of Materials: Plastics and Polymers. Elsevier, 2022, pp. 460–473.
23.
XuXGuptaN. Determining elastic modulus from dynamic mechanical analysis: a general model based on loss modulus data. Materialia2018; 4: 221–226.
24.
ChoiJYYanamandraKShettyA, et al.Measurement of viscoelastic constants and Poisson’s ratio of carbon fiber reinforced composites using in-situ imaging. J Reinforc Plast Compos2022; 42(13-14): 638–647.
25.
XuXElgamalMDoddamaniM, et al.Tailoring composite materials for nonlinear viscoelastic properties using artificial neural networks. J Compos Mater2021; 55(11): 1547–1560.
26.
XuXGuptaN. Artificial neural network approach to predict the elastic modulus from dynamic mechanical analysis results. Adv Theory Simul2019; 2(4): 1800131.
27.
XuXElgamalMGuptaN. Artificial neural network approach to tailor composite materials with nonlinear viscoelasticity. In: 35th Annual American Society for Composites Technical Conference, ASC 2020. DEStech Publications, 2020.
28.
XuX. Machine learning approach to characterize elastic, viscoelastic, relaxation and creep behavior of materials. New York University Tandon School of Engineering, 2020.
29.
XuXGuptaN. Application of radial basis neural network to transform viscoelastic to elastic properties for materials with multiple thermal transitions. J Mater Sci2019; 54(11): 8401–8413.
30.
Rodríguez-SánchezAEPlascencia-MoraHAcevedo-AlvaradoM. Neural network-driven interpretability analysis for evaluating compressive stress in polymer foams. J Cell Plast2024; 60(4): 237–258.
31.
Reddy MadhiralaVMadduNKShuklaA, et al.Deep learning for predicting stress and instantaneous modulus in closed-cell PVC foams. J Cell Plast2025; 61(5): 319–334.
32.
Rodríguez-SánchezAEPlascencia-MoraH. Modeling hysteresis in expanded polystyrene foams under compressive loads using feed-forward neural networks. J Cell Plast2023; 59(4): 269–292.
Flores-JohnsonEALiQMMinesRAW. Degradation of elastic modulus of progressively crushable foams in uniaxial compression. J Cell Plast2008; 44(5): 415–434.
35.
Standardization, IOf., Plastics - Determination of tensile properties - part 2: test conditions for moulding and extrusion plastics, in ISO 527-2. 2012.
36.
MarkovitzH. Theory of viscoelasticity. An introduction. J Colloid Interface Sci1984; 98(1): 292.
37.
AnagnostouDChatzigeorgiouGChemiskyY, et al.Hierarchical micromechanical modeling of the viscoelastic behavior coupled to damage in SMC and SMC-hybrid composites. Compos B Eng2018; 151: 8–24.
38.
WangJWangHChenX, et al.Experimental and numerical study of the elastic properties of PMI foams. J Mater Sci2010; 45: 2688–2695.
39.
ArezooSTagarielliVLPetrinicN, et al.The mechanical response of Rohacell foams at different length scales. J Mater Sci2011; 46(21): 6863–6870.
40.
IshikawaMIrokawaCKogoY, et al.Time-dependent deformation of ROHACELL-core CFRP sandwich panels. Adv Compos Mater2016; 25(3): 271–286.
41.
PoxonS. The mechanical response of low to high density Rohacell foams. Oxford University, 2013.
42.
ChenCPAndersonWBLakesRS. Relating the properties of foam to the properties of the solid from which it is made. Cell Polym1994; 13(1): 16–32.
