PINNForm - Physics-Informed and AI-Driven Forming for Resilient and Sustainable Manufacturing

Purpose and goal

PINNForm addresses the industrial need for rapid, flexible manufacturing of complex metal components for resilience and sustainability. The project combines digital technology with physics-based models via AI/ML. The goal is a design and optimization support system providing precise models with limited data, leading to faster manufacturing, reduced scrap, and extended tool life. The technology is demonstrated with drone components. Long-term, predicting optimal repair methods is also an aim.

Expected effects and result

At the project´s conclusion, the technology is expected to reach TRL 6, demonstrated in a relevant industrial environment. Results will include a verified digital twin based on AI/ML methods, integrated with an adaptive production method for metal components using industrial origami technology. The project will demonstrate functional, robust drone components for forestry and emergency preparedness. New expertise in AI-driven forming optimization will be developed

Planned approach and implementation

The project is divided into five WPs. WP1 focuses on project management and coordination. WP2 encompasses the development of a digital twin and AI models for data generation. WP3 handles the training and evaluation of the AI models. WP4 concerns the implementation and validation of industrial origami technology with a physical demonstrator for drone components. WP5 aims at communication & dissemination of the project´s results. The work is a collaboration between RISE, STILFOLD, and AirForestry.