Published June 2019 | Version v1
Journal article

Modeling the thermomechanical behaviors of particle reinforced shape memory polymer composites

  • 1. Nanjing University of Aeronautics and Astronautics, State Key Laboratory of Mechanics and Control of Mechanical Structures (China)
  • 2. Nanjing Institute of Technology, Jiangsu Key Laboratory of Advanced Structural Materials and Application Technology, School of Materials Science and Engineering (China)
  • 3. Harbin Institute of Technology, National Key Laboratory of Science and Technology on Advanced Composites in Special Environments (China)

Description

Shape memory polymer composites (SMPCs) possess superior thermomechanical properties while keep good shape memory capabilities, so SMPCs attract great research interest from academia and industry. In the paper, a modified thermoviscoelastic finite deformation constitutive model is developed to investigate the thermomechanical properties and shape recovery behaviors of particle reinforced SMPCs. The model is incorporated with the modified Adam–Gibbs model and the Eyring model to describe the structural relaxation and yielding behaviors. Also, a non-Gaussian chain molecular network model is used to capture the hyperelastic properties in the large deformation situation. Then, the constitutive model is employed to estimate the free recovery behaviors of SMPCs with different particle fractions, and the comparison between the simulation results and the test data shows good agreement. Furthermore, the model is applied to predict the constrained recovery properties and uniaxial tensile stress–strain response of the composites. The thermomechanical model provides an efficient method for designing and optimizing particle reinforced SMPCs.

Additional details

Identifiers

Publishing Information

Journal Title
Applied Physics. A, Materials Science and Processing (Print)
Journal Volume
125
Journal Issue
6
Journal Page Range
p. 1-10
ISSN
0947-8396
CODEN
APAMFC

INIS

Country of Publication
Germany
Country of Input or Organization
International Atomic Energy Agency (IAEA)
INIS RN
54067682
Subject category
S36: MATERIALS SCIENCE;
Descriptors DEI
COMPUTERIZED SIMULATION; OPTIMIZATION; POLYMERS; SHAPE MEMORY EFFECT
Descriptors DEC
SIMULATION

Optional Information

Copyright
Copyright (c) 2019 Springer-Verlag GmbH Germany, part of Springer Nature