Cantilever

2D stress distribution prediction in cantilever beams using convolutional neural network.

Variable parameters:

• Beam length
• Beam height
• Load angle
• Load location

Input images:

• Channel 1: Beam shape and load location
• Channel 2: Load angle

Output images:

• 2D von Mises stress distributions generated by FEA simulations in ANSYS (Mechanical APDL)

The 2023-02 dataset and 2023-05 dataset were both generated using the same simulation parameters, but 2023-05 was generated using an updated and simplified Ansys script, which had minor changes to syntax and calculations and small discrepancies in values. Future training should be done using the 2023-05 dataset.

Creating the Dataset

Ansys (Mechanical APDL) is used to run simulations and generate response data, which are then processed in Python.

Define the simulation parameters in the script ansys_script.lgw. The script loops over each combination of simulation parameters, and it simulates responses and saves the response data to text files. Refer to this reference on Ansys APDL commands.

Open Ansys and run the script ansys_script.lgw.

• File > Change Directory... > [Select the folder where the script exists]
• File > Read Input from... > [Select the script]
• To maximize performance, close the File Explorer window of the folder in which the program is running while simulations are running.

Each line in a text file contains the response value at a different node (not an element) along with the X, Y, and Z coordinates of that node. The coordinates of the node are used later to insert each response value into the proper location in a matrix.

Run preprocessing.py to process and cache the text files for easier loading when training models. Specifically, this code reads the response data contained in the text files and converts them into tensors and saves them as .pickle files. Caching the data in this way avoids having to read and convert the text files directly every time a model is trained, which takes longer.

Define the location and name of the .pickle file in the CantileverDataset class in datasets.py.

Training and Testing

Call the main() function in main.py to train and test the model. The model architecture is defined in networks.py. Results and model predictions are plotted after testing.

Set train to True to train the model, and set test to True to test the model. When training a model from scratch, train_existing must be set to False. Manually specify training hyperparameters, such as number of epochs, learning rate, batch size, etc.

During training, the best model weights (determined by lowest validation loss) are tracked and saved separately.

Google Colab

Uploading Files

1. Upload all required .py files other than main.py to the current directory: /content/. These files need to be uploaded each time the session resets.
2. Uploaded all other required files (.csv, .txt, etc.) to a folder of choice in Google Drive. This folder is specified in the code as: drive/My Drive/<FOLDER>. Click "Mount Drive" in the sidebar to make files from Google Drive available. This creates a folder called drive in the directory /content/.

Using GPU

To use GPU, click Runtime > Change runtime type.