3D printed cellular solid outperforms traditional stochastic foam in long-term mechanical response

Abstract

3D printing of polymeric foams by direct-ink-write is a recent technological breakthrough that enables the creation of versatile compressible solids with programmable microstructure, customizable shapes, and tunable mechanical response including negative elastic modulus. However, in many applications the success of these 3D printed materials as a viable replacement for traditional stochastic foams critically depends on their mechanical performance and micro-architectural stability while deployed under long-term mechanical strain. To predict the long-term performance of the two types of foams we employed multi-year-long accelerated aging studies under compressive strain followed by a time-temperature-superposition analysis using a minimum-arc-length-based algorithm. The resulting master curves predict superior long-term performance of the 3D printed foam in terms of two different metrics, i.e., compression set and load retention. To gain deeper understanding, we imaged the microstructure of both foams using X-ray computed tomography, and performed finite-element analysis of the mechanical response within these microstructures. This indicates a wider stress variation in the stochastic foam with points of more extreme local stress as compared to the 3D printed material, which might explain the latter's improved long-term stability and mechanical performance.

Figure 1

Microstructures of two different foam materials made out of filled polydimethylsiloxane (PDMS) elastomers: (a) an open-cell stochastic foam; and (b) an additively manufactured (AM) foam with the face-centered-tetragonal (FCT) lattice structure, the diameter of each cylindrical strut being 250 μm.

Figure 2. Compression set (top left) and load retention (bottom left) of an open-cell stochastic foam made from a PDMS elastomer.

The left figures are actual measurements taken as a function of time over a period of 2 years for compression set and 8.5 years for load retention. The right figures are obtained by TTS-shifting the isotherms along the log-time axis so as to obtain a single master-curve with the minimum arc-length. The dashed curves (TTS Prediction) are smooth fits to the master curve, and used for prediction purposes.

Figure 3. Compression set (top left) and load retention (bottom left) of AM FCT–a 3D printed PDMS foam of the face-centered tetragonal structure.

The left figures are actual measurements taken as a function of time over a period of 1 year. The right figures are obtained by TTS-shifting the isotherms along the log-time axis so as to obtain a single master-curve with the minimum arc-length. The dashed curves (TTS Prediction) are smooth fits to the master curve, and used for prediction purposes.

Figure 4. TTS-predicted compression set (left figure) and load retention (right figure) for stochastic and AM FCT foams under ambient conditions over a period of 100 years.

The AM foam is clearly superior in both properties, except for load retention at very early times.

Figure 5

X-ray CT images of stochastic foam (a,b) and AM FCT foam (c,d) made out of PDMS elastomer. (a) 3D image of the outer surface of a rectangular specimen of a stochastic foam; (b) 2D image of a typical cross-section of the stochastic foam specimen; (c,d) side-top and end-cross-section views of a 8-layer AM FCT foam sample. Mesh representation of such images are used to perform finite-element simulation of stress distribution reported in this work.

Figure 6. Stress distribution in a typical slice of: (left) AM FCT foam; and (right) stochastic foam, both under 15% compressive strain.

The presence of many high stress points is clearly evident in the stochastic foam. The presence of these high-stress points over an extended period of time is likely responsible for higher compression set and lower load retention in the stochastic foam as compared to the AM FCT foam. The stress scale bar is in units of 10⁵ Pa.