Training and Validation Dataset of MgAl2O4 SEM Fracture Images for "Toward Quantitative Fractography Using Convolutional Neural Networks"

Tsopanidis, Stylianos; Osovski, Shmuel

experiment machine learning microscopy ceramic training set scanning electron microscopy SEM fracture fractography intergranular transgranular validation set

Organizations

MDF Open

Year

2019

Source Name

tsopanidis_training_validation_networks

License

CC-BY 4.0

DOI

10.18126/vu60-4htj View on Datacite

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This is a dataset of SEM fracture images for training and validation of deep learning algorithms. The fracture surfaces of the MgAl2O4 ceramic material can be used to train a convolutional neural network for identifying the intergranular and transgranular fracture modes. European Union's Horizon2020 Programme (Excellent Science, Marie-Sklodowska-Curie Actions) under REA grant agreement 675602 (Project OUTCOME) Contact: Stylianos Tsopanidis tsopanidisstelios@gmail.com or Shmuel Osovski shmuliko@technion.ac.il