Mohammad Safiuddin, Ch Likith Reddy, Ganesh Vasantada, CHJNS Harsha, Dr. Srinu Gangolu
Paper: http://dx.doi.org/10.1007/978-981-97-6367-2_39
Abstract: The microstructure of a material strongly influences its mechanical properties and the microstructure itself is influenced by the processing conditions. Thus, establishing a Process-Structure-Property relationship is a crucial task in material design and is of interest in many engineering applications. In this work, the processing-structure relationship is modelled as deep learning based conditional image synthesis problem. This approach is devoid of feature engineering, needs little domain awareness, and can be applied to a wide variety of material systems. We develop a GAN (Generative Adversarial Network) to synthesize microstructures based on given processing conditions. Results show that our GAN model can produce high-fidelity multiphase microstructures which have a good correlation with the given processing conditions.
The following files are included in this package:
omni-loss-biggan.ipynb: an Ipython notebook that contains the code used to train the model.new_metadata.xlsx: an Excel workbook that holds the training image metadata..\app: a directory that contains the source code for the app. Further instructions on the app can be found below.
If you want to run the app locally, follow the instructions below
To install the app, unzip the .\Microstructure_GAN folder. Next, navigate to the .\Microstructure_GAN\app directory in a terminal and run the following command to install the necessary packages:
pip install requirements.txt
Once the packages have been installed, run the following command to start the web app:
streamlit run gan_app.py
Recreating Results:
Generated micrographs can be downloaded by clicking the "Download Micrograph" button. The file name of the saved image contains the processing conditions and seed value, for example: 800-85H-Quench-864.png. To recreate the image, the latent vector can be generated using the seed as follows.
seed = 864
rng = np.random.RandomState(seed)
latent_vector = rng.normal(0, 1, (1, 384))