Overview

The characterization section was set to help with characterizing microstructural features from digital images. The examples use a variety of codes to process images, ranging from GUI-based codes such as ImageJ to more hands-on scripts in MATLAB using the Image Processing Toolbox. This particular site contains production and research codes that have been developed both at CAVS and outside for segmenting microstructural features and analyzing the corresponding binary images.

Software for Plotting Data

This section includes links to common data plotting packages. A more comprehensive list of free software is available. The examples include:

• Kaleidagraph
• Veusz (free)

Example Programs for Microstructural Characterization

This section includes links to some of the codes used for microstructural characterization.

• MATLAB with Image Processing Toolbox
• ImageJ
• Clemex

Tutorials

Here are a few tutorials to get started using some materials characterization software.

• MATLAB Beginner Tutorial 1
  • This tutorial introduces basic image processing in MATLAB along with an automated threshold technique^[1].
• ImageJ Beginner Tutorial 1
  • This tutorial introduces basic image processing and particle analysis in ImageJ.
• ImageJ Beginner Tutorial 2
  • This tutorial teaches how to calculate nearest neighbor distances of particles using ImageJ.

Microstructural Characterization Research

Equipment used for materials Characterization

Image Segmentation

• Automated Region Growing Technique for EFTEM image^[2][3]

Multiscale Characterization Techniques

• Multiscale Analysis of Area Fractions^[4][5][6]

Synthetic Microstructure Generation

• 2D Composite Material Microstructure Builder^[4][5][6]

Animiating images in ImageJ

• How to make a movie with ImageJ

Data Analysis Tools

This section includes codes to automate common data analysis tasks.

• (WIP) Stress-Strain Analysis (Matlab GUI)
• Split-Hopkinson Pressure Bar^[7] strain gauge voltage analysis tool (Matlab GUI)
• (WIP) Fatigue Data Sorting for MTS specimen.dat files (Python)
• XY Data Averaging and Uncertainty (Python) Media:XY_Average.zip
• (WIP: Adding GUI) Savistsky-Golay Filter for smoothing noisy datasets (Python) Media:Savitsky_Golay_Smoothing.zip
• Averaging stress-strain curves, with error bars, import from MS Excel, export to CSV. Installer for Windows is File:Str str AppInstaller web.zip. Supports cyclic datasets.

References

1. ↑ Otsu, N., "A Threshold Selection Method from Gray-Level Histograms," IEEE Transactions on Systems, Man, and Cybernetics, Vol. 9, No. 1, 1979, pp. 62-66.
2. ↑ Tschopp, M.A., Tiley, J.S., Viswanathan, G.B., "Automated Identification and Characterization of Secondary & Tertiary Gamma Prime Precipitates in Nickel-based Superalloys," Materials Science & Technology 26(12) (2010) 1414-1422, http://dx.doi.org/10.1179/026708309X12560332736638.
3. ↑ Tiley, J.S., Rajagopolan, S., Viswanathan, G.B., Shiveley, A., Tschopp, M.A., Banerjee, R., Fraser, H.L., "Measurement of Gamma Prime Precipitates in a Nickel-based Superalloy using Energy-Filtered Transmission Electron Microscopy coupled with Automated Segmenting Techniques," Micron, 41(6) (2010) 641-647, http://dx.doi.org/10.1016/j.micron.2010.03.003.
4. ↑ Tschopp, M.A., Wilks, G.B., Spowart, J.E., "Multi-Scale Characterization of Orthotropic Microstructures," MSMSE 16 (2008) 065009, http://dx.doi.org/10.1088/0965-0393/16/6/065009.
5. ↑ Spowart, J.E., Maruyama, B., Miracle, D., "Multi-Scale characterization of spatially heterogeneous systems: Implications for discontinuously reinforced metal matrix composite microstructures," Materials Science & Engineering A 307 (2001) 51-66, http://dx.doi.org/10.1016/S0921-5093(00)01962-6.
6. ↑ Wilks, G.B., Tschopp, M.A., Spowart, J.E., "Multi-Scale Characterization of Inhomogeneous Morphologically Textured Microstructures," Materials Science and Engineering: A, 527 (2010) 883-889, http://dx.doi.org/10.1016/j.msea.2009.09.003.
7. ↑ Francis, D., Whittington, W., Lawrimore, W. et al. Exp Mech (2017) 57: 179. https://doi.org/10.1007/s11340-016-0191-9.