On 17 and 18 June, CIDAUT travelled to Braga, Portugal, to represent both the BERTHA (GA 101076360) and STWIN (GA 101112504) projects at the EPoSS Annual Forum. As a cornerstone event for European stakeholders in smart systems integration, this year’s forum highlighted the challenges and opportunities currently shaping the future of the continent’s industrial landscape.
Key discussions centred on strategic priorities that the European Union is actively addressing. These include accelerating domestic chip development to ensure technological sovereignty, creating hardware-agnostic electronics to enable more flexible digital paradigms, and embedding sustainability at the heart of electronic component lifecycles. Crucially, a major focal point was the transformative role of Artificial Intelligence (AI) in revolutionising core sectors such as manufacturing and automotive.
Aligning with these macro-trends, CIDAUT presented the latest breakthroughs from initiatives, sparking engaging discussions around AI applications. Within the automotive domain, CIDAUT shared insights from the BERTHA project through the work titled “Using data from driving trials to tune and validate the performance of driver behavioural models in the CCAM framework”, which demonstrated how real-world data can refine AI models to pave the way for safer Connected, Cooperative and Automated Mobility (CCAM). Turning the focus to smart manufacturing, CIDAUT then showcased STWIN’s research, titled “An integrated framework for defect detection in FSW using Machine Learning and multimodal NDT data fusion”. This work highlighted how combining Machine Learning with advanced Non-Destructive Testing (NDT) data can detect defects in Friction Stir Welding (FSW) in real time, drastically reducing waste and boosting production efficiency.
By showcasing these innovative solutions, CIDAUT not only demonstrated its technical expertise but also underscored how both BERTHA and STWIN are actively turning the EU’s industrial strategies into practical, market-ready realities.