AI improved advanced manufacturing and production processes in factories (RIA) (Made in Europe and AI, Data and Robotics partnerships)
Project Proposal
Seeking project coordinator and partners: AI improved advanced manufacturing and production processes in factories (RIA)
Can act as:
Seeking expertise:
KNEIA is seeking a project coordinator and partners to participate in the CIRCUL-AI project to design, develop, and validate a new generation of explainable, adaptive, and trustworthy AI-driven manufacturing intelligence that optimises production processes in real time, reduces environmental and climate impact, prevents defects, and maximises circular material use across advanced European factories, while being seamlessly integrated into industrial value-creation structures and decision-making processes.
TOPIC: “HORIZON-CL4-2026-02-DIGITAL-EMERGING-51-two-stage: AI improved advanced manufacturing and production processes in factories (RIA) (Made in Europe and AI, Data and Robotics partnerships)” indicates: “AI approaches in manufacturing processes hold the potential to significantly enhance circularity, process and operational efficiency as well as sustainability of modern factories. (…) New solutions based on innovative enabling technologies such as deep learning, large language models, digital twins, synthetic data, and data-driven models allow manufacturers to improve production system efficiency, elevate product quality, and proactively address critical challenges in energy consumption and carbon footprint. (…) Proposals should produce dedicated innovative explainable AI based solutions in advanced manufacturing for at least two of the following: 1) improve processes and operational efficiency, and reduce climate and environmental impact of processes and factories through dynamic selection of optimal processes and production parameters, exploiting AI for process modelling and/or optimisation; 2) avoid the production of defective parts using AI to detect process drift and anomalies and correct proactively defects in real time; and 3) maximise the fraction of regenerated components or materials used in the production using AI to optimise the material flow.
The outcomes of the project will contribute to the development and deployment of next-generation, explainable AI solutions for advanced manufacturing, enabling factories to achieve higher operational efficiency, improved product quality, and enhanced sustainability. By integrating deep learning, hybrid physics–data-driven models, digital twins, and real-time adaptive optimisation, the project will allow manufacturers to proactively detect and correct process anomalies, optimise energy and material flows, and maximise circularity. These outcomes will support the transition toward smart, resilient, and climate-conscious production systems in Europe, strengthening industrial competitiveness, reducing environmental impact, and fostering scalable AI-driven innovations across the manufacturing sector.
If you are interested, please contact the manager in charge of proposals Ciro Avolio ([email protected])