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Coupling Advanced Imaging Analysis and Morphology Based Modeling for Integrated Characterization of Micromechanics of Wood and Wood-Based Composites Lech Muszyński & John Nairn Department of Wood Science & Engineering

Wood is a natural composite Non-homogeneous Cellular Anisotropic Has a complex, multi-level structure Highly hygroscopic J. Harrington

Wood-based composites add another level of material organization resulting from the manufacturing process It is not just chemistry! Morphology matters!

Wood-based composites Regardless of the scale of the specimen considered, there are features of wood anatomy that affect the specimen behavior under load They vary from specimen to specimen, from tree to tree, and may vary dramatically between species (e.g., differences between softwoods and hardwoods) Haygreen, J.G., J.L. Bowyer (1996)

The Purpose: Moving to the next level A deeper understanding of complex bio-based composites is necessary for further advancement of bio-based materials and better utilization of solid wood Adequate experimental and analytical methods are needed to deal with the complexity of the internal structure N i l d li i t d t b f Numerical modeling is expected to be of some help

Modeling? Accurate models are critical for prototyping and developing new, advanced bio-based materials as well as for improving properties of those already present on the market

Numerical modeling is expected to be of some help Rapid prototyping Virtual testing of hypotheses Pre-screening new ideas & formulations Focusing on the most promising i formulations Cost effective when: physical tests are too complex, impractical, or Optimization takes resources Robert M. Skillen too expensive (as is trial-and-error method on an industrial scale)

Integrated approach Inverse problem methodology Empirical observations on relevant levels of material organization Multi-scale modeling morphology-based FEM, MPM Material concepts micro-mechanics mechanics failure mechanisms

Digital images Digital photography (2D) and d computed d tomography (3D) provide images of heterogeneous materials, which are then available for quantitative analysis y

Images carry information We should be able to visualize count measure visible features Measure and model their effect on the properties of the material/composite

Modeling Modeling approaches commonly deal with wood anatomical features by simplifying the problem to an idealized geometry or by homogenization of specimen inhomogeneities. In many cases, however, these approaches are not sufficient i

Modeling: Material Point Method (MPM) Morphology based approach developed for solving problems in dynamic solid mechanics, an alternative ti to dynamic FEA: Sulsky et al. 1994; Sulsky et al. 1995; Sulsky and Schreyer 1996 Zhou 1998 Nairn 2007 DIC output format matches the data format used in MPM

Coupling Imaging g and Modeling Full-field imaging techniques and computer modeling approaches for analysis of complex materials are available, but they have developed largely independently Coupling imaging i to modeling and using inverse problem methodology, makes it possible to investigate correlations between morphological structure, micro-mechanics, and governing g failure mechanisms in ways that would be impossible by either method alone

Coupling Imaging g and Modeling Inverse problem methodology allows determination of material parameters through correlating full field measurements with theoretical results from morphology based models A. Experiments & Imaging D. Angle Mask C. MPM Modeling Material Property Input B. Experimental ε yy (DIC) E. MPM Calculations Adjust Properties Close collaboration of modelers and experimentalists is known to increase chances for success Done Yes Difference (Δ) Δ Prop. No Sensitivity Analysis based on Lecompte et al. 2005

Two cameras allow resolution of the third dimension

Digital Volume Correlation (DVC) An extension of twodimensional DIC DVC method uses high-resolution CT to image naturally occurring internal material textures, providing unique patterns for volumetric displacement tracking. courtesy of Dr. B.K. Bay (OSU) http://me.oregonstate.edu/research/brg/dvc.htm (2 of 2) [2/15/2007 8:08:56 PM]

Optical measurement of deformations & strains 0.5 hour 20 hours 30 hours 40 hours -0.12 0.0 0 hour 10 hours 20 hours 25 hours 30 hours -0.12 0.0

Robustness of the algorithm: Accuracy & precision Is best evaluated on images of undeformed specimens: when the expected deformations and strains are zero, every non-zero value is an error Error values across the FOV and their variability from one image to another may be evaluated statistically U [μm] V [μm] ε x [] ε y [] Mean 1.28 1.41 3.35e-6 5.35e-5 Stdev 2.93 3.08 4.75e-4 4.15e-4 Min -17.1-13.8-4.59e-2-2.31e-3 Max 13.3 18.8 2.43e-3 2.22e-3

Accuracy & precision will depend on: Equipment: Resolution of the cameras Quality of the optics Robustness of the DIC algorithm Operator: Lighting & spec surface quality Thorough calibration Smart selection of many calculation parameters Attention to detail

Accuracy & precision The random noise, bias and system resolution may be assessed from images of an unloaded specimen: average and standard deviation of the displacement and strain readings for the entire field of view are measures of random noise and bias in a single time step repeating this procedure for several time steps ensure that the noise is stable repeating this procedure for specimen being shifted of a known distance will confirm the robustness of the calibration

Accuracy and precision Standard deviations of displacements in absence in undeformed d ROI Standard deviations of strains in absence in undeformed d ROI displacem ment, mm 1.0E-2 9.0E-3 8.0E-3 7.0E-3 6.0E-3 50E3 5.0E-3 4.0E-3 3.0E-3 2.0E-3 stdev(u) stdev(v) stdev(w) strains, mm/mm 8.0E-4 7.0E-4 6.0E-4 5.0E-4 40E4 4.0E-4 3.0E-4 2.0E-4 stdev(exx) stdev(eyy) 1.0E-3 1.0E-4 0.0E+0 0.0E+0 1 2 3 4 5 6 1 2 3 4 5 6 Image # Image #

Full-field Optical Measurement of Deformations in Early Stages of Drying Oregon White Oak and Western Hemlock Lumber Ho-Yang Kang Lech Muszyński Michael R. Milota

Experimental setup

Output data: strains In most cases: Major* principal strains (ε 1 ) are approximately correlated to the radial direction of wood Minor* principal p strains (ε 2 ) are approximately correlated to the tangentialti direction of wood *) Note: terms minor and major refer to signed strain values, so for negative strains ε 1 < ε 2

Swelling in radial direction Western hemlock at 20% MC (calculated sample average) Note: The swollen zone receding away from the surface Note: The strain concentration ce o in the leading edge of the specimen

Principal strains for early and latewood Deformations observed since the beginning of drying Swelling observed for the radial direction

Free shrinkage of a thin section x 10-3 2 Work in progress x 10-3 2 0 0-2 -2-4 -4-6 -6-8 -8-10 -10-12 -12-14 -14-16 -16-18 -18 Radial Tangential

Free shrinkage of a thin section Specimen size: 150 mm x 85 mm Area of interest: ~ 105 mm x 75 mm 740 x 525 pixels Subset size: 19 Step size: 1 Shrinkage/Sw welling 0.2% 0.0% 0.2% 0.4% 0.6% 0.8% 1.0% mean(e1) mean(e2) 1.2% 1.4% 0 10 20 30 40 50 60 Elapsed time, min

Morphology Based Modeling of Micro-Mechanics and Failure Mechanisms in Bio-Materials with Polymer Matrices John A. Nairn Lech Muszyński Farzana Hussain (grad student) CSREES/USDA NRI CGP # 2008-01500

Wood-Plastic Composites (WPCs) Heterogeneous dispersed particulate composites comprised of 3 phases: Particles: Wood or other natural fibers Matrix: Thermoplastics 1 Polystyrene (PS) Polyethylene (PE) Polypropylene (PP) Polyvinyl chloride (PVC) Additives composites.wsu.edu/ s ed navy/navy1/materials.html

Current research J.A. Nairn, L. Muszyński (PIs) (2008): Morphology Based Modeling of Micro-Mechanics and Failure Mechanisms in Bio-Materials with Polymer Matrices, CSREES/USDA NRI CGP # 2008-01500, ($397,311, 36 months) J. Simonsen, L. Muszyński, ń W. Tze, S. Ramaswamy (PIs) (2008): Replacing petroleum-based polymers with a novel reinforced biopolymer system, invited, CSREES/USDA NRI CGP #2008-01522, ($496,711, 36 months)

Morphology matters: Fibers (fibres) & particles There is no shortage of theories and models for short fiber (fibre) thermoplastic composites They may or may not apply to wood flour particles used in WPCs urba ana.mie.uc.ed du/yliu/soft tware/

Focus on the internal bond Internal bond determines the load transfer between the particle and the matrix, and consequently the mechanical properties of the composite Th h f i l b d The strength of internal bond determines whether the particles act as merely filler or as reinforcement

Objective & Approach Objective: to establish an effective analytical methods for quantitative morphological characterization of WPCs examine morphology of the interface/interphase between the wood particles and the polymer matrix Approach combine advanced imaging g tools (X-ray CT, ESM and conventional microcopy) with MPM modeling and statistical analysis

Images carry information We should be able to visualize count measure features Measure and model their effect on the Measure and model their effect on the properties of the composite

The refining process is not designed for precision http://www.atritor.com/indipics/pulveriseren.html www.feedmachinery.com/glossary/

urbana.mie.uc.edu/yliu/software/ Morphology matters: Particles vs. fibers (fibres)

http://www.phoenix-xray.com/en/applications/materials science/products.php Morphology matters: Particles vs. fibers (fibres)

Morphology Matters: Particles vs. fibers (fibres) Pozgaj & al.

What are the particles like?

What are the particles like? SEM micrographs of individual wood flour particles (scale bar=100 μm)

Particle characteristics 0.8 measured particle sizes idth, mm 0.6 0.4 Measurements Particles: A B C D A w 0.2 B 0 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2 length, mm C based on digital image analysis (DIA) of 40 micrographs

Particle characteristics 0.8 measured particle sizes idth, mm 0.6 0.4 Measurements Particles: A B C D w 0.2 0 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2 length, mm D based on digital image analysis (DIA) of 40 micrographs

Particle characteristics Major mm Minor mm Aspect ratio Area mm 2 max 192 1.92 061 0.61 23.1 080 0.80 median 0.77 0.27 2.8 0.22 mean 0.77 0.28 3.2 0.23 St. dev. 0.31 0.13 1.8 0.14 500 Aspect ratios 400 Areas 400 300 coun nt 300 200 100 cou unt 200 100 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0 5.5 6.0 6.5 0 aspect ratio 7 & more 0 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40 area, mm2 0.45 0.50 0.65 0.70 & more

Particle characteristics idth, mm 0.8 0.6 0.4 Major Minor Aspect Area measured particle sizes mm mm ratio mm 2 max 192 1.92 061 0.61 23.1 080 0.80 median 0.77 0.27 2.8 0.22 Measurements Particles: A B C D Median mean 0.77 0.28 3.2 0.23 St. dev. 0.31 0.13 1.8 0.14 w 0.2 0 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2 length, mm ~40 particles in each micrograph D

Internal structure A B (Region A) Peripheral cells Crushed (possibly densified) Thorn open May account for as much as 56% of the cross section area (Region B) Inner cells Almost intact cell structure t

Micromechanical characterization A snapshot with Vic2D full-field strain map Single wood flour particle Area of interest Work in progress

Summary Numerical models for wood and wood- based composites need to address: variability and complex morphology (for particles it is not identical with clear wood) changes in particle morphology due to the compounding process the presence of an extensive interphase

Acknowledgements Collaborators: J. Simonsen (OSU) B. Bay (OSU) Y. Geng (WSE) A. Sevrain (France) H.L. Frandsen (DUT) J. Madhusudan (IIT) H. Pathak (IIT) S. Gulati (IIT)