The ISAPI course aims to introduce students to the world of smart factories and Industry 4.0, providing theoretical tools on aspects of production systems, sensors used in industrial production and the basics of the most innovative algorithms of artificial intelligence and vision, with a careful look at the future applications and impacts that these have had and that these will have on future production systems. In addition, key topics such as cyber physical systems and digital twin, useful for virtual commissioning (but not only), will also be covered. Finally, a series of exercises will be proposed in order to introduce students to Python programming and modeling-simulation of complex systems.
- Recap on production systems
- Key concept of Industry 4.0
- Sensor used in industrial production
- Communication protocol
- Machine learning and machine vision
- Edge computing devices
- Intro to python, linux based OS and git
- Exercise :python programming
- Digital twin
- A really quick intro to robotics
- Developing physical models with Simulink and Simscape
- Modeling contacts with Simscape multibody
- Modeling robot kinematics with Simscape Multibody
- Modelling of GMAW process in Simulink
- Modelling of Vipers850 in Simscape
- Slide : Design industrial cell with Fanuc SR6iA robot
- Introduction to signal processing with Simulink
- Signal processing with Simulink. Low pass and Kalman filters design
- Recap on key concepts in computer vision and OpenCV library
- Off line seamtracker for path generation
- Key concepts in machine learning and computer vision. Introduction to Tensorflow framework. An overview to Recurrent NN and Reinforcement learning
- DNN : Universal approximation theorem proof with sine wave function with tensorflow
- DNN : Run or walk detector (using MATLAB Deep learning toolbox)
- DNN : Quality predictor for GMA Welding with tensorflow
- CNN : Flower classifier with tensorflow
- MATLAB R2021a : Simscape, Robotics, DSP, Mulitbody, Control design toolboxes
- Tensorflow 2.2.0 with Keras API
- Python 3.7.6
- OpenCV 4.5.5
- Numpy 1.18.1
- Scikitlearn 0.22.1
- Matplotlib 3.1.3
- Pandas 1.0.1
- Machine learning course from Berkeley university
- Getting started with Simulink
- Introduction to model based design with Simulink and Simscape
- Code generation for Rasberry Pi
- OpenCV free course
- Git and GitHub for beginners
- Keras with TensorFlow Course - Python Deep Learning and Neural Networks for Beginners Tutorial
- Panda free course
- Numpy tutorial
- Matplotlib Tutorial
- ML algorithms in python
- Deep Neural Networks for Defects Detection in Gas Metal Arc Welding
- Control with deep learning : Using RL for cartpole system
- Queensland University (AU) Robot Academy
- Computer vision exercises