First of all, download the source code of the Matlab toolbox.
To have more details about the use of the toolbox, please have a look to :
How to use the GUI for “pop-in” analysis from indentation tests ?¶
First of all a GUI is a Graphical User Interface.
- Run the following Matlab script :
- Answer ‘y’ or ‘yes’ (or press ‘Enter’) to add path to the Matlab search paths, using this script:
- The following window opens:
- Import your (nano)indentation results (.xls file obtained from MTS software with at least more than 20 indentation tests for statistics), by pressing the button ‘Select file’.
- Select the end segment (if segments exist), in order to set the maximum indentation depth.
- Set units and criterion to detect pop-in.
- Once the dataset is loaded and parameters set, run calculations by pressing the green button ‘RUN CALCULATIONS and PLOT’.
- Load-displacement curves and selected cumulative distribution function (cdf) are plotted respectively on the left graphic and the right graphic.
- A picture of the main window as .png file is created and cdf fit results are stored in a .txt file when you press the button ‘SAVE’.
- Results are accessible by typing in the Matlab command window (here for 50 indentation tests) :
gui = guidata(gcf) gui = config: [1x1 struct] % config. of the GUI data_xls: [1x1 struct] % details about .xls file handles: [1x1 struct] % handles of the GUI = buttons, boxes... settings: [1x1 struct] % settings of the GUI flag: [1x1 struct] % flags for errors, calculations data: [1x50 struct] % data cropped results: [50x1 struct] % results obtained after calculations Hertz: [1x1 struct] % details about hertzian fit cumulativeFunction: [1x1 struct] % cdf fit results
The YAML configuration files¶
Default YAML configuration files, stored in the folder yaml_config_files, are loaded automatically to set the GUI:
You have to update these YAML config. files, if you want to change indenter properties, constant parameters of models and constant parameters of the least-square method used to solve nonlinear curve-fitting and the path to your datasets.