Didactic Factories Experiments

The experiment will make the lab software architecture more decentralized and resilient thanks to the installation of new hardware directly on the machine. This will substitute the existing one, limited to the gathering, filtering and formatting of raw data, to make the system more resilient towards unexpected events and more sustainable in environmental terms, as the data processing will be moved on the more efficient edge layer. 

The main goal of the experiment is to develop an edge solution, from hardware and software point of view, with algorithm for predictive maintenance. The edge solution is a key point to divide the overall computational algorithms in two main level: in edge computer solution, and a remote solution on or on premises. The key part of experiment is to design a build on a new edge computing, new features from algorithms. 

The experiment “Self-evolving monitoring systems for assembly production lines” consists of an industrial set-up representing a part of the DC drive assembly process. The experiment will set up a testbed for self-evolving solutions for monitoring production asset performances, targeting repetitive processes, such as assembly production lines. Demonstrated diagnostic systems will self-learn by analyzing the patterns from process time-series measurement, while using machine-learning methods.

The experiment focuses on generating synthetic datasets that are statistically meaningful and reflect real data in ways that are relevant to yield improvement. Most recently, AI and machine learning algorithms have been used to create synthetic data that is more useful and representative and are used to train and calibrate models before real-world data becomes available. 

Environmental sustainability through AI is the key topic of the experiment. A full-scale instrumentation will be available as well as appropriate digital infrastructure(s) bringing together data from different sources to eventually built-up digital twins. Dedicated innovative solutions to address environmental targets will be developed and demonstrated. 

The experiment will deal with specific topics of work based on Digitalisation/AI used to reduce costs/increasing revenue according to a circular economy approach, such as quality monitoring in the whole production process, predictive maintenance, product traceability, management of industrial by-products. 

The experiment consists of upgrading the existing DF concerning Predictive Maintenance from Industry 4.0 to Industry 5.0, with AI on the edge and easy-to-use and focused human-machine interfacing for decision support and providing dissemination events so that engineers of SMEs can experience I5.0. 

The purpose of this experiment is to take a typical production scenario, such as an assembly process, and use the already available data to create a stream of information which the IWOK system can use to suggest a solution to a problem to the Operator, who can in turn use their ‘human’ I/O to verify the solution and ‘teach’ the machine how to fix the problem itself. This then creates a living Knowledge base between the Operator and the Machine that can be used to avoid problems before they occur. 

BEhAI is a DF driven experiment in Lombardy focused on implementing AI inside manufacturing. What the experiment wants to demonstrate is how the application of the AI linked to sensors’ data can enhance human-machine interactions by identifying the state of the operator and adjust its patterns and functions to it, accordingly.

This will be done to address two main challenges:

- Understand and adapt to human states;

- Understand and adapt to context.

QUAD-AI@E experiment will develop a product defect detection system able to acquire product images, to manipulate images by an embedded AI tool, to extract info about the nature and severity of the product defect and to send alert to the personnel. The experiment builds on integrating AI at the edge technology into a product defect detection system prototype suitable for quality control automation in textile industry. 

The experiment aims to demonstrate an AI-driven real-time monitoring of a robotic assembly line equipped with a delta robot assembling a small 4-wheel vehicle. The core problem of the monitoring is the detection of non-OK state from force and torque time series acquired by sensors positioned on the robot end effector. The main goal is to detect a problem quickly after it appears so that a corrective action will come early, which can significantly improve the assembly process. The problem is formulated as a real-time anomaly detection from multidimensional time-series, which can be efficiently solved by machine learning techniques.  

The experiment will update the AAU IoT suitcase to include more advanced data analytics and machine learning capabilities. One machine type from the host SME will be selected and used as basis for experimenting and validating data-driven technologies.  

PBN am-Lab in Hungary is conducting the SUNSYNC experiment, aiming to optimize recycling processes in industrial environments. The experiment focuses on automating and streamlining recycling methods, utilizing solar power as the primary energy source, extending battery life, and making data-driven decisions throughout the project. The experiment involves implementing an AI-driven system that integrates data from light detectors, weather forecasts, and accumulator monitoring systems. The process includes data integration, AI-based data processing, and real-time decision-making to optimize energy use and align recycling operations with available clean energy.

The experiment will deploy at least an edge node that allows to create a Digital Twin of the lab and its systems. The main objective is to provide a system with AI/ML models to study approaches to optimize a virtualized intralogistics or production process both from the operational and energy efficiency point of view. The Didactic Factory aims to be a safe environment where novel algorithms and approaches can be tested with the available process data in order to research improvements that could be further optimized later in a real environment.