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).