Influence of the Processes Parameters on the Properties of The ...

More documents

Recommendations

Info

Chapter 3. Analytical Methods and Designs <strong>of</strong> Experiments median pore diameter. As example for P L LA, a Hg typical distribution <strong>of</strong> pores is presented in Figure 3.12-B [Ho et al., 2004]. 5.1.3 X-ray Microtomography Trater et al. [2005] have investigated <strong>the</strong> use <strong>of</strong> non-invasive 3-D X-ray microtomography (XMT) for microstructure characterization. Moreover, XMT generated images were more conducive to digital image processing than SEM images because <strong>of</strong> ‘razorthin’ depth <strong>of</strong> focus and sharp contrast between solid and void areas. This technique has been widely used for <strong>the</strong> in vivo imaging <strong>of</strong> plants, insects, animals and humans. X-ray microtomography is a non destructive technique that provides a reasonable level <strong>of</strong> resolution (~ 5 – 20 m). The X-ray microtomography approach is an extension <strong>of</strong> <strong>the</strong> computer aided tomography (CT) medical imaging technique. X-rays are directed from a high-power source toward a sample, and a detector on <strong>the</strong> opposite side <strong>of</strong> <strong>the</strong> sample measures <strong>the</strong> intensity <strong>of</strong> <strong>the</strong> transmitted X-rays (see Figure 3.13-A). (A)-Schematic reprensation <strong>of</strong> CT. (B)-Example <strong>of</strong> analysis with P L LA/Silica. Figure 3.13: CT principle and images <strong>of</strong> P L LA/Silica sample. [Collins et al., 2010; Hancock and Mullarney, 2005] A two-dimensional “shadow” image is produced by accurately rastering <strong>the</strong> X-ray beam across <strong>the</strong> sample. The sample <strong>the</strong>n is carefully moved relative to <strong>the</strong> X-ray beam, and <strong>the</strong> process is repeated to produce additional two-dimensional images from various view points. Using a Fourier transform algorithm, <strong>the</strong> two-dimensional images <strong>the</strong>n are combined to generate a complete three-dimensional map <strong>of</strong> <strong>the</strong> sample. The intensity <strong>of</strong> <strong>the</strong> X-rays reaching <strong>the</strong> detector is controlled by <strong>the</strong> sample path length and <strong>the</strong> X-ray attenuation coefficient <strong>of</strong> <strong>the</strong> material that it encounters on that path [Cao et al., 2003]. The varying levels <strong>of</strong> signal intensity provide a gray-scale in <strong>the</strong> images (see Figure 3.13-B) from which information about <strong>the</strong> thickness, and attenuation properties <strong>of</strong> <strong>the</strong> sample can be deduced. 5.2 Scanning Electron Microscopy Observations Electron microscopy is a technique based on <strong>the</strong> principle <strong>of</strong> electron-matter interactions, capable <strong>of</strong> producing high-resolution images <strong>of</strong> <strong>the</strong> surface <strong>of</strong> a sample. A focused electron beam is deflected through electromagnetic lenses, scans <strong>the</strong> surface <strong>of</strong> <strong>the</strong> sample for analysis which, in response, re-emits different types <strong>of</strong> emissions (cf. Figure 3.14). The signals that derive from electron-sample interactions reveal information about <strong>the</strong> sample including external morphology (texture), chemical composition, and pore size, pore structure and orientation <strong>of</strong> materials making up <strong>the</strong> sample. In most applications, data are collected over a selected area <strong>of</strong> <strong>the</strong> surface <strong>of</strong> <strong>the</strong> sample, and a 2-dimensional image is generated that displays spatial variations in <strong>the</strong>se properties. magnification ranging from 20× to approximately 30,000×. - 72 -
Chapter 3. Analytical Methods and Designs <strong>of</strong> Experiments Figure 3.14: Schematic representation <strong>of</strong> interactions beam on specimen surface. 5.2.1 Bases <strong>of</strong> Image Analysis SCION ® Image was originally developed for <strong>the</strong> National Institutes <strong>of</strong> Health, a federal government agency. Basic image properties like contrast, brightness and gamma can be optimized. For particle analysis <strong>the</strong> most important property is <strong>the</strong> grey level <strong>of</strong> <strong>the</strong> image which can be segmented to reduce different shades [Niemistö, 2006]. Segmentation means <strong>the</strong> separation <strong>of</strong> different parts <strong>of</strong> <strong>the</strong> image; a foreground objects like particles from a background <strong>of</strong> image. In <strong>the</strong> pore size and shape analysis segmentation has to be done with very high accuracy because <strong>the</strong> area <strong>of</strong> <strong>the</strong> particle is dependent <strong>of</strong> <strong>the</strong> accuracy <strong>of</strong> segmentation and <strong>the</strong> results <strong>of</strong> analysis have to be reliable [Niemistö, 2006]. Segmentation can base on ei<strong>the</strong>r discontinuity or similarity <strong>of</strong> intensity values. Discontinuity methods find abrupt changes in <strong>the</strong> intensity and separate various regions on that way. Methods <strong>of</strong> similarity needs predefined criteria <strong>of</strong> <strong>the</strong> intensity value and separate regions based on that. Thresholding, clustering, region growing, region merging and region splitting are methods which are included in <strong>the</strong> category <strong>of</strong> similarity methods [Niemistö, 2006]. Thresholding is a central method <strong>of</strong> segmentation due to its simple and intuitive properties. It separates bright foreground objects on a dark background and can be defined as: (3.13) where f(x) = grey level <strong>of</strong> <strong>the</strong> point x, f T (x) = <strong>the</strong> respective point in <strong>the</strong> thresholded image and T is <strong>the</strong> threshold. If a pixel in f T gets value 1, it is called a foreground (or object point) and if it gets value 0, it is called a background. Threshold T can be <strong>the</strong> same for <strong>the</strong> whole image (global threshold) or <strong>the</strong>re can be different thresholds in different parts <strong>of</strong> <strong>the</strong> image (local threshold) [Niemistö, 2006]. The transition between <strong>the</strong> object point and <strong>the</strong> background may be so unsteady that a human can’t decide where <strong>the</strong> borders between <strong>the</strong> object and <strong>the</strong> background exactly go. Many papers are published on <strong>the</strong> automatic selection <strong>of</strong> <strong>the</strong> threshold since 1960’s. The most commonly used method is created by Otsu [1979]. That method maximizes <strong>the</strong> class variance <strong>of</strong> <strong>the</strong> grey levels between <strong>the</strong> objects and <strong>the</strong> background and minimizes <strong>the</strong> intra-class variance. Usually threshold is selected from a histogram <strong>of</strong> <strong>the</strong> image. If <strong>the</strong> histogram is bimodal threshold should be selected between <strong>the</strong> modes because supposedly a one mode represents <strong>the</strong> foreground and <strong>the</strong> o<strong>the</strong>r one represents <strong>the</strong> - 73 -
Page 1 and 2:
Institut National Polytechnique de
Page 4:
iii Dedicated to My Fathe</
Page 7 and 8:
pleased I was to work with all memb
Page 10 and 11:
Publications and Conferences The wo
Page 12 and 13:
Nomenclature and Abbreviations Abbr
Page 14:
ρ p Pellet density Splitting (Bra
Page 17 and 18:
Chapter 2 .........................
Page 19 and 20:
6.2.1 Surface Tensions of</
Page 21 and 22:
4.1 Effect of <str
Page 23 and 24:
3.2 Experiments on PLA/Waxes Scaffo
Page 26 and 27:
List of Figures Fi
Page 28 and 29:
Figure 4.28: Incremental Intrusion
Page 30 and 31:
Figure 6.36: Micrographs of
Page 32 and 33:
Figure 8.32: Slice images o
Page 34 and 35:
List of Tables Tab
Page 36 and 37:
Table 8.1: Polymers with different
Page 38 and 39:
iological performance. Biocompatibi
Page 40 and 41:
Thesis Introduction Chapter 1 Chapt
Page 42 and 43:
Chapter 1. Polylactide Based Bio-Ma
Page 44 and 45:
Page 46 and 47:
Page 48 and 49:
Page 50 and 51:
Page 52 and 53:
Page 54 and 55:
Page 56 and 57:
Page 58 and 59: Chapter 1. Polylactide Based Bio-Ma
Page 64 and 65: Chapter 2. Processes</stron
Page 98 and 99: Chapter 3. Analytical Methods and D
Page 102 and 103: . Chapter 3. Analytical Methods and
Page 124 and 125: Chapter 4. Experimental Procedures
Page 148 and 149: Chapter 5 - 112 -
Page 150 and 151: Chapter 5. Characterization <strong
Page 158 and 159:
Chapter 5. Characterization <strong
Page 160 and 161:
Page 162 and 163:
Page 164 and 165:
Page 166 and 167:
Page 168 and 169:
Page 170 and 171:
Page 172 and 173:
Page 174 and 175:
Page 176 and 177:
Page 178 and 179:
Page 180 and 181:
Page 182 and 183:
show all

Influence of the Processes Parameters on the Properties of The ...

You also want an ePaper? Increase the reach of your titles

Delete template?

Save as template?