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

Area of Science:

• Materials Science
• Mechanical Engineering
• Polymer Science

Background:

• Traditional elastomeric foams are widely used but have limitations.
• 3D printing offers controlled micro-structures with potential for improved performance.
• Characterizing long-term behavior of 3D printed foams is crucial for structural applications.

Purpose of the Study:

• To develop a constitutive model for 3D printed close-packed foam structures under compression, considering thermal aging.
• To accurately predict the evolution of stress-strain behavior, compression set, and load retention over time.

Main Methods:

• Utilized the Ogden hyperfoam strain-energy functional within the Tobolsky two-network scheme.
• Developed a thermal-age-aware constitutive model for 3D printed foam.
• Applied time-temperature superposition to predict long-term changes and quantify uncertainty.

Main Results:

• The model accurately describes experimentally measured stress-strain response, compression set, and load retention.
• Predictions for long-term changes were enabled through time-temperature superposition.
• Identified a single dominant aging mechanism at the molecular/network level based on Arrhenius activation barrier.

Conclusions:

• The developed model provides a robust framework for predicting the long-term mechanical performance of 3D printed foams.
• This enables the design of more reliable and durable components for demanding applications.
• Understanding the aging mechanism facilitates material optimization and performance enhancement.