Artificial Intelligence in Manufacturing
applications from the world of manufacturing
Artificial Ingelligence (AI) already contributes to diverse challenges in manufacturing at the shop-floor level. We have collected a few use-cases which nicely show details about challenges and related solutions.
RobnAI: Empowering Robotic Design
through AI-Driven Optimization
This application demonstrates the integration of AI-driven design tools to optimize robotic components, achieving structural efficiency, reduced material usage, and lower production costs through the AIRISE–I Know How collaboration.
D-TASE: AI-Driven Efficiency and Quality in Textile Manufacturing
AI-based Early-Stage Defect Detection in Fabric Rolls
The D-TASE experiment helped a major Turkish textile manufacturer optimize energy use and reduce waste in garment production. By applying AI to detect flaws early and streamline operations, the company achieved sustainability gains and improved product quality.
MCCI: Enhancing CNC Machining Performance with AI Monitoring
Monitor & Control system for Chatter identification
Through the MCCi experiment, CNC Solutions leveraged AI to optimize machine utilization, reduce scrap, and improve working conditions. The result was a more efficient, sustainable, and operator-friendly manufacturing process.
AIMODO: Accelerating Eco-Friendly Product Design with AI
Artificial Intelligence assisted Mold Design Optimization
The AIMODO experiment enabled Plus Science to accelerate product development and reduce material use in its eco-friendly STEM kits. AI-supported design tools helped cut mold requirements and significantly shorten design time.
AI4CHEESE: AI-Guided Recipe Optimization in Traditional Cheese Production
AI for enhanced cream cheese recipe generation supporting improved efficiency and waste reduction
The AI4CHEESE experiment supported KEFIS, a Greek cheese producer, in optimizing traditional recipes with AI. By fine-tuning process parameters, the company reduced energy and water consumption while maintaining product quality.
AI4MAT: AI Driven Material Selection for Machine Tool Design
Accelerating engineering workflows through data driven material optimisation
The AI4MAT experiment helped Ce.S.I. transform material selection for machine tool design using AI-driven modelling. By combining data-driven techniques with explainable AI, engineers could quickly and reliably identify optimal materials, greatly reducing simulation time while ensuring accuracy and transparency.