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  • DL AI-2.0 – Laboratory for the Development of Artificial Intelligence
  • DL AI-2.0 – Laboratory for the Development of Artificial Intelligence

    The DL AI-2.0 is an artificial intelligence laboratory from De Lorenzo for teaching students to develop AI solutions for industrial applications. It combines hardware modules with a server that holds the AI software. Students build models from live sensor data, then check the result on a physical actuator panel. Experiments are set as challenge-based tasks that simulate industrial problems such as quality control, predictive maintenance, condition monitoring, process automation and optimisation.

    Three students work on the hardware, each with their own computer. Up to 17 more connect to the server from their own computers using DL AI-2.0SW licences, so a class of 20 can work at the same time. The computers for those 17 students are not included. Teachers can run more than 20 experiments. The hardware covers an IoT module with Wi-Fi, MQTT and Node-RED, an AI development module for computer vision and neural networks, an embedded module programmed in C++, and sensor, display, actuator and power supply modules. The software environment includes Python, TensorFlow, MySQL and OpenCV.

    Training objectives include PID control programming, control using neural networks, real-time operating system routines, colour detection, object tracking and pathfinding, and supervised learning on temperature, humidity, pressure and lighting data. The laboratory suits AI, automation, software, mechatronics and process engineering courses, as well as information technology and technical and vocational training. Bestech Australia supplies the DL AI-2.0 with on-site training and commissioning support available.

    • Industrial challenges: quality control, predictive maintenance, condition monitoring, process automation
    • Engineering courses: AI, automation, software, mechatronics and process engineering
    • Information technology and technical and vocational training
    • Route planning and optimisation with the A* algorithm
    • Capacity: 20 students: 3 on the hardware, up to 17 on their own computers (not included)
    • Experiments: More than 20
    • Modules: IoT, AI development, embedded, sensor, UDP sensor, display, actuator and power supply
    • Software: Python, TensorFlow, MySQL and OpenCV, plus path programming and database applications
    • Teaching approach: Challenge-based learning with simulated industrial tasks
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