2. Data description

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    The aim of this section is to detail the nature, sources and characteristics of data in use for machine learning methods. Some of the variables are experimental data previously treated. Others have been added to the dataset for this internship. We will also expose a statistical exploration of data to show what problems will appear next.

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• 2.1. Experimental Data
  • 2.1.1. AITA data : orientations and microstructure
  • 2.1.2. Data Computing : Geometric variables
    • 2.1.2.1. Distance to grain boundary and triple junction
    • 2.1.2.2. Schmid factor
    • 2.1.2.3. Schmid difference and misorientation angle
  • 2.1.3. CraFT data : Strain/Stress modelization
  • 2.1.4. RX map : variable to explain
• 2.2. Additional data
  • 2.2.1. Data computing : Anisotropy factors of triple jonctions
  • 2.2.2. Triple jonctions dataset for tabular data learning
  • 2.2.3. Triple jonctions dataset for convolutional and mixture Artificial Neural Network learning
• 2.3. Data exploration
  • 2.3.1. Data correlations
    • 2.3.1.1. Tabular dataset of pixels
    • 2.3.1.2. Tabular dataset of Triple Junctions
  • 2.3.2. Principal Components Analysis (PCA)
    • 2.3.2.1. PCA on pixels dataset
    • 2.3.2.2. PCA on TJ dataset
  • 2.3.3. T-Stochastic Neighbor Embleding (t-SNE)
  • 2.3.4. Pixel threshold