Green AI for Sustainable Automotive Industry

The automotive  value  chain  involves  extreme  data  flows  of heterogeneous, distributed, fast-growing, disconnected, or hardly compatible information. ML methods face new challenges and opportunities to holistically analyse the massive and unprecedented data integrated across these chains, to support decisions that fundamentally change automotive manufacturing processes towards a sustainable, circular, and climate-neutral automotive industry. Graph-Massivizer enables new graph-based encoding that captures several value-chain stages to predict their outcome better and detect anomalies. Better and quicker analysis prevents defect propagation and unnecessary waste,  contributing  to  a  sustainable,  circular,  and  climate-neutral  automotive  industry. By combining graph-based ML methods with digital twins, Graph-Massivizer provides new insights and boosts the efficiency and scalability of the diagnosis beyond that of more expensive alternatives (e.g., excessive sensor deployment for continuous monitoring).


Predict “best” production configurations for a given BiW type and welding machines over simulated data with predictable manufacturing KPIs (BiW quality).


Manufacturing Graph Generator with extremely controlled scaling of Man-MG in multiple dimensions (number of sensors, production lines, processes).