Faculty Publications
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Item Prediction of strain rate sensitivity of high density polyethylene using integral transform of dynamic mechanical analysis data(Elsevier Ltd, 2016) Zeltmann, S.E.; Bharath Kumar, B.R.; Doddamani, M.R.; Gupta, N.Recent interest in understanding the effect of strain rate on mechanical properties has motivated this study to develop a correlation between frequency domain dynamic mechanical analysis (DMA) results and elastic modulus values that are obtained from a separate set of elaborate tensile tests conducted over a wide range of strain rates. Using the time-temperature superposition principle and the integral relations of viscoelasticity, the DMA results are converted into a time-domain relaxation function in order to predict the strain-rate dependent modulus. The transformation technique is validated with experimental results for high density polyethylene (HDPE) resin and is found to be accurate over a wide range of strain rates. Cross correlation between DMA results and tensile test results over a wide range of strain rates can help in substantially reducing the requirement for tests that are needed to characterize the material behavior with respect to strain rates, temperature and loading frequency. © 2016 Elsevier LtdItem Prediction of modulus at various strain rates from dynamic mechanical analysis data for polymer matrix composites(Elsevier Ltd, 2017) Zeltmann, S.E.; Prakash, K.A.; Doddamani, M.; Gupta, N.Understanding and modeling the behavior of polymers and composites at a wide range of quasi-static and high strain rates is of great interest to applications that are subjected to dynamic loading conditions. The Standard Linear Solid model or Prony series frameworks for modeling of strain rate dependent behavior are limited due to simplicity of the models to accurately represent a viscoelastic material with multiple relaxations. This work is aimed at developing a technique for manipulating the data derived from dynamic mechanical analysis to obtain an accurate estimate of the relaxation modulus of a material over a large range of strain rate. The technique relies on using the time-temperature superposition principle to obtain a frequency-domain master curve, and integral transform of this material response to the time domain using the theory of viscoelasticity. The relaxation function obtained from this technique is validated for two polymer matrix composites by comparing its predictions of the response to uniaxial strain at a prescribed strain rate to measurements taken from a separate set of tension experiments and excellent matching is observed. © 2017 Elsevier LtdItem Extracting elastic modulus at different strain rates and temperatures from dynamic mechanical analysis data: A study on nanocomposites(Elsevier Ltd, 2019) Xu, X.; Koomson, C.; Doddamani, M.; Behera, R.K.; Gupta, N.Viscoelastic nature of polymers makes their properties strongly dependent on temperature and strain rate. Characterization of material properties over a wide range of strain rates and temperatures requires an expensive and time consuming experimental campaign. While viscoelastic properties of materials are widely tested using dynamic mechanical analysis (DMA) method, the frequency dependent component of the measured properties is underutilized due to a lack of correlation between frequency, temperature, and strain rate. The present work develops a method that can extract elastic modulus over a range of strain rates and temperatures from the DMA data for nanocomposites. Carbon nanofiber (CNF) reinforced high-density polyethylene (HDPE) matrix nanocomposites are taken as the study material. Four different compositions of CNF/HDPE nanocomposites are tested using DMA from 40 to 120 °C at 1–100 Hz frequency. First, time-temperature superposition (TTS) principle is used to develop an extrapolation for the results beyond the test parameter range. Then the TTS curve is transformed to a time domain relaxation function using integral relations of viscoelasticity. Finally, the strain rate sensitive elastic modulus is extracted and extrapolated to room temperature. The transform results are validated with tensile test results and the error found to be below 13.4% in the strain rate range 10?5 to 10?3 for all four nanocomposites. Since the materials are tested with the aim of finding a correlation among the test methods, the quality of the material is not a study parameter and the transform should yield accurate results for any material regardless of composition and quality. © 2018 Elsevier LtdItem Tailoring composite materials for nonlinear viscoelastic properties using artificial neural networks(SAGE Publications Ltd, 2021) Xu, X.; Elgamal, M.; Doddamani, M.; Gupta, N.Polymer matrix composites exhibit nonlinear viscoelastic behavior over a wide range of temperatures and loading frequencies, which requires an elaborate experimental characterization campaign. Methods are now available to accelerate the characterization process and recover elastic modulus from storage modulus (E?). However, these methods are limited to the linear viscoelastic region and need to be expanded to nonlinear viscoelastic problems to enable materials design. The present work aims to build a general machine learning based architecture to accelerate the characterization and materials design process for nonlinear viscoelastic materials using the E? results. To expand outside the linear viscoelastic region, general relations of viscoelasticity are first developed so the master relation of E? considering nonlinear viscoelasticity can be transformed to time domain relaxation function. The transform starts with building the master relation by optimizing the artificial neural network (ANN) formulation using Kriging model and genetic algorithm. Then the master relation is transformed to a relaxation function, which can be used to predict the stress response with a given strain history and to further extract the elastic modulus. The transform is tested on high density polyethylene matrix syntactic foams and the accuracy is found by comparing the predicted materials properties with those obtained from tensile tests. The good agreements indicate the transform can predict the elastic modulus under a wide range of temperatures and strain rates for any composition of the composite and can be used for material design problems. © The Author(s) 2020.