- Polymeric composites reinforced with bio-materials have advantages over composites with synthetic reinforcements. Bio-based composites use low-cost and renewable reinforcements, have nonabrasive properties for machining, have improved damping characteristics, and have potential for energy recycling. However, the limited use of bio-based composites is because their mechanical properties are typically much lower than those of synthetic composites.
The objective of this study was to combine state-of-the-art imaging tools with emerging numerical modeling methods for an integrated, multi-level characterization of bio-based reinforcements and their composites. Digital photography (2D) will allow collection of full-field digital images of the surface of sample composites, which will be used for characterization of the morphological structure of fillers (copper wire or wood particle) and of model composites. Mechanical experiments (tension load) on isolated fillers and on model composites will allow imaging of the deformed material. By correlating relative positions of thousands of surface features between consecutive images, digital image correlation (DIC) algorithms can be used to map surface deformation fields and calculate surface strain fields.
Digital imaging methods can only record deformations and strains. The interpretation of those strains in terms of material properties, such as position-dependent modulus of a heterogeneous composite material, requires simultaneous modeling. The modeling must
use morphology-based methods that can handle anisotropy, heterogeneity, and the complex structure of bio-based composites such as wood plastic composites. This research used the material point method (MPM) as a modeling tool. MPM is a particle-based, meshless method for solving problems in computational mechanics. The crucial advantage of MPM over other methods is the relative ease of translating pixels from digital images into material points in the analysis. Thus digital images (2D) used in our experiments were used as direct input to the MPM software, so that the actual morphologies, rather than idealized geometries, were modeled. This procedure removes typical uncertainties connected with idealization of the internal features of modeled materials. It also removes variability of specimen to specimen due to morphology variations.
Full-field imaging techniques and computer modeling methods for analysis of complex materials have developed independently. This research Coupled imaging and modeling and used inverse problem methodology for studying bio-particulate composites. The potential of coupling experiments with morphology-based modeling is a relatively new area. This work studied the morphology and mechanical properties of copper wire (for validation experiments) and wood particles used for reinforcement in polymer composites. The goal was to determine the in situ mechanical and interfacial properties of copper wire and then wood particles. By comparison of DIC results to MPM, the conclusion is MPM simulation works well by simulating 3D composite structure and using Matlab software to do qualitative and quantitative comparisons. Copper validation tests showed that copper wire is too stiff compared to polymer such that the inclusion modulus had low effect on the surface strains (DIC experimental results). Wood particle worked better because modulus of wood is much lower than copper. By qualitative comparison of the wood particle specimens, we could deduce that the in situ properties of wood particles are lower than bulk wood. Quantitative analysis concentrated on small area and got more exact results. In a 90 degree particle quantitative study, MPM simulations were shown to be capable of tracking the structure of wood particle plastic, which involved failure. The entire approach, however, is not very robust. We can get some results for mechanical properties, but it does not seem possible to extract all anisotropic properties from a few DIC tests, as some researcher have suggested.