Programming Exercises for course DPMS_AS/1110 - Intelligent Manufacturing Systems, School of Mechanical Engineering, National Technical University of Athens.
In this exercise we created a model of a manufacturing system, using petri nets. In the petri net P-Invariant and T-Invariant analysis was used, in order to check the the bound of the system as well and the deadlocks.
In order to run, first you must downnload software PIPE v.4.3.0. In addition open a terminal or cmd inside Pipe directory and execute the command
java Pipe
In this exercise we have created two neural network models for predicting maximum temperature at a welding plate process and predicting the time interval for temperatires above 723C.
pip3 install virtualenv
virtualenv neural_network
source neural_network/bin/activate
pip3 install -r requirements.txt
For maximum temperature NN training, run this command
python3 MaxTemperatureNN_training.py
For time over 723 Celcius degrees NN training, run this command
python3 AboveStandardTemperatureNN_training.py
For maximum temperature prediction, run this command
python3 MaxTemperatureNN_inference.py --plate_thickness 0.004 --initial_temperature 180 --heat_input 900 --electrode_velocity 0.004 --X 0.0 --Y 0.02 --Z 0.002
For time over 723 Celcius degrees, run this command
python3 AboveStandardTemperatureNN_inference.py --plate_thickness 0.005 --initial_temperature 200 --heat_input 1200 --electrode_velocity 0.0035 --X 0.025 --Y 0.025 --Z 0.0025
In this exercise we have created a genetic algorithm for optimizing the neural networks for welding plate process. The cost funtion of the algorithm implements an euclidean distance between two temperatures. It also uses a penalty criterion on temperature on two occasions.
-The first occasion is when
-The second occasion is when