Industry 4.0 aims to transform industrial processes through the integration of ‘smart’ technologies such as AI, automation, and robotics, into manufacturing processes. Integral to many high-value industries are machine vision systems (MVSs) where cameras and sensors are used to monitor production. These systems enable correct part placement during assembly, coordinate robots and detect defects whilst improving efficiency, increasing productivity, and reducing costs. The integration of ‘digital twins’ could further improve MVSs by providing virtual models of physical objects that use real-time data from sensors to simulate MVSs behaviour and monitor operations. However metrological standards, calibration methods, traceability and uncertainty assessment for existing and newly developed MVSs are lacking.

This project will address this by developing advanced, traceable MVSs and tools for in-line measurement on manufactured parts along with validated DTs based on physical and AI-driven methods. In collaboration with industrial partners three case studies on the developed methods and tools will be performed, the results of which will form a good practice guide detailing surface, dimensional, structural and operational qualities. The new standards, calibration procedures and improved capabilities for MVSs available to industry will speed-up the digital transformation in a wide range of sectors including the aeronautic, pharmaceutical, medical, electronics, and semiconductor industries.

In the long term this will boost European industrial competitiveness by providing improvements in reliability, efficiency, and speed of production. This will not only reduce waste and energy consumption but also help to reduce Europe’s CO₂ footprint.