Experimental characterization and thermoviscoelastic modeling of strain and stress recoveries of an amorphous polymer network
نویسندگان
چکیده
An acrylate polymer network was submitted to thermomechanical shape memory cycles. The set of experiments characterized the material stress-free strain recovery and the strain-constrained stress recovery in uniaxial tension. Experimental parameters like temperature of strain fixation, amount of strain and heating rate, were varied in order to provide a relatively complete set of experimental data. A model combining the amorphous polymer viscoelasticity and its time-temperature superposition property was used to predict the shape memory behavior of the acrylate polymer network. All the model parameters were characterized using classical tests for mechanical characterization of polymers, which do not include shape memory tests. Model predictions obtained by finite element simulations compared very well to the experimental data and therefore the model relevance for computer assisted application design was assessed.
منابع مشابه
Prediction of the Effect of Polymer Membrane Composition in a Dry Air Humidification Process via Neural Network Modeling
Utilization of membrane humidifiers is one of the methods commonly used to humidify reactant gases in polymer electrolyte membrane fuel cells (PEMFC). In this study, polymeric porous membranes with different compositions were prepared to be used in a membrane humidifier module and were employed in a humidification test. Three different neural network models were developed to investigate several...
متن کاملCharacteristic Points of Stress-Strain Curve at High Temperature
Determination of critical points on hot stress-strain curve of metals is crucial in thermo-mechanical processes design. In this investigation a mathematical modeling is given to illustrate the behavior of metal during hot deformation processes such as hot rolling. The critical strain for the onset of dynamic recrystallization has been obtained as a function of strain at the maximum stress. In a...
متن کاملLeast Squares Support Vector Machine for Constitutive Modeling of Clay
Constitutive modeling of clay is an important research in geotechnical engineering. It is difficult to use precise mathematical expressions to approximate stress-strain relationship of clay. Artificial neural network (ANN) and support vector machine (SVM) have been successfully used in constitutive modeling of clay. However, generalization ability of ANN has some limitations, and application of...
متن کاملSand2015-xxxxr Ldrd Project Number: 169249 Ldrd Project Title: Modeling the Coupled Chemo-thermo- Mechanical Behavior of Amorphous Polymer Networks Project Team Members
Amorphous polymers exhibit a rich landscape of time-dependent behavior including viscoelasticity, structural relaxation, and viscoplasticity. These time-dependent mechanisms can be exploited to achieve shape-memory behavior, which allows the material to store a programmed deformed shape indefinitely and to recover entirely the undeformed shape in response to specific environmental stimulus. The...
متن کاملFinite Element Modeling of Strain Rate and Grain Size Dependency in Nanocrystalline Materials
Nanocrystalline materials show a higher strain-rate sensitivity in contrast to the conventional coarse-grained materials and a different grain size dependency. To explain these phenomenon, a finite element model is constructed that considers both grain interior and grain boundary deformation of nanocrystalline materials. The model consist of several crystalline cores with different orientations...
متن کامل