Structure - Processing Linkages in Polyethylene
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Structure - Processing Linkages in Polyethylene
- 1. 1 Structure - Processing Linkages in Polyethylene David Brough Abhiram Kannan Final Presentation ME 8883
- 2. Outline ‣ Motivation & Objective ‣ X Ray Scattering Datasets of Polyethylene ‣ Workflow ‣ Results and Discussions ‣ Future Work ‣ Summary ‣ Acknowledgements
- 3. Motivation Jancar, J. et al. Current issues in research on structure - property relationships in polymer nanocomposites. Polymer 51, 3321–3343 (2010) Hierarchical structural assembly of a material influences the properties on the macroscopic scale
- 4. Motivation & Objective Processing Condition a Processing Condition b Microstructure Set a Microstructure Set b Properties Set a Properties Set b PE Temperature Pressure Isotropic vs Anisotropic Homogeneous vs Heterogeneous Yield Strength Polyethylene (PE)
- 5. X Ray Scattering Data of PE Small Angle X Ray Scattering (SAXS) data is related to spatial statistics 200 µm x 200 µm Lamella Inter Crystalline Amorphous
- 6. Play Film sample is strained continuously while being probed by X rays X Ray Scattering Data of PE
- 7. Bulk Density Processing Condition Film Thickness (µm) 0.912 gms/cc 1 20 30 75 2 20 30 75 0.923 gms/cc 1 20 30 75 2 20 30 75 Workflow spatial statistics dimensionality reduction processing linkage SAXS Data Principal Components Analysis (PCA) Transfer Function Model (TFM)
- 8. Principal Components Analysis • 3200 .tif images across 12 samples (~250 per sample) • Log intensity scaled by mean to account for thickness effects • Scaled images fed to PCA Algorithm • Outputs of PCA Algorithm visualized in D3 Compare :- 1. Effects of Processing Conditions 2. Effects of Density 3. Effects of Thickness
- 9. Transfer Function Model Linkage • For sample 6,10 each Principal Component is fit to a Transfer Function model of order (2,1,5) • Using obtained coefficients, predictions for the remaining samples are made • Comparison of Predicted and True Low Dimensional Trajectories. Model Equation:-
- 10. Summary • Dimensionality Reduction of time resolved data by PCA • Objective comparison between strain derived microstructures of PE • Minimization of User Bias incurred from traditional analysis protocols • Applied method for deriving processing linkages via Transfer Function Model might hold potential
- 11. Future Work • Extraction of spatial statistics by via transformation of SAXS data • Reconstruction of 2 Phase Crystalline - Amorphous Microstructures • Extend Transfer Function Model to incorporate Stress Values • Property Linkage with Crystallinity, Orientation etc. • Additional Length Scales (~0.1 nm) from Wide Angle Scattering Data (WAXS)
- 12. Acknowledgement s • Dr. Surya Kalidindi (GT) • Dr. Hamid Garmestani (GT) • Dr. Tony Fast (GT) • Dr. David Bucknall (GT) • Dr. David Fiscus (ExxonMobil)
Editor's Notes
- Microstructure defined as everything under the micron scale. Structural arrangement at different length scales results in different macroscopic properties.
- The structural arrangement and therefore final properties depend on the processing conditions under which the microstructures are fabricated. For example, under certain conditions of temperature and pressure, PE can form microstructures that result in material properties useful for making toothbrushes. Under different conditions of of temperature and pressure, PE can form microstructures that result in material properties useful for making bullet proof fabrics. Our objective in this project is to link different PE microstructures with their processing conditions and ultimately to their properties.
- What is discretization? What are spatial statistics? What are the methods of dimension reduction? What is the linkage and how do we come up with it? This slide needs work.
- Our data is in fourier space. But it is directly related to the spatial stats in fourier space.
- We use SAXS data from 12 films as input data to dimensionality reduction because of the similarity between SAXS data and 2 pt stats in PE..