With demand for more sustainable and efficient energy solutions growing across Europe, the ORGANIC project, coordinated by AIMEN Technology Centre, is tackling a key challenge in wind energy manufacturing: producing large, high-performance turbine blades that are both recyclable and industrially reliable. By combining bio-based materials, generative design, and artificial intelligence within large-format additive manufacturing (AM), the initiative—comprising 13 partners from eight European countries—aims to develop bio-intelligent systems capable of delivering high-quality components while overcoming technical hurdles such as material durability, process stability, and scaling complex designs to industrial dimensions.
Funded under the Horizon Europe framework, the project brings together a diverse consortium including Asociación de Investigación Metalúrgica del Noroeste (Spain), Aeroblade (Spain), Instituto Tecnológico del Embalaje, Transporte y Logística (Spain), Fundación Cartif (Spain), EIT Manufacturing Central GGMBH (Germany), ICONIQ Innovation (United Kingdom), 10XL BV (Netherlands), Addcomposites Oy (Finland), Tampereen Korkeakoulusatio SR (Finland), Ethnicon Metsovion Polytechnion (Greece), Core Innovation and Technology OE (Greece), Netcompany (Luxembourg) and Scuola Universitaria Professionale della Svizzera Italiana (Switzerland).
“Our goal is to build an integrated bio-intelligent ecosystem that makes bio-inspiration industrially relevant,” explains Andrea Fernández Martínez, ORGANIC Project Coordinator and researcher in Artificial Intelligence and Data Analytics.
I recently spoke with Fernández, Dr. Ander Reizábal López-Para, Researcher in Additive Manufacturing of Composites and Ramón Angosto Artigues, Researcher in Artificial Intelligence and Data Analytics, about the project’s objectives, technical innovations, and industrial ambitions.
Rethinking Additive Manufacturing for Wind Energy
Traditional AM is often energy-intensive, difficult to scale for large components like wind turbine blades, and limited in recyclability. While bio-inspired 3D printing has been explored extensively in research, most applications remain at a small scale. ORGANIC aims to address these challenges.
On the materials front, the project validates bio-based, fully recyclable composites as net-zero alternatives to conventional polymers. AI-driven generative methods create bio-inspired architectures that go beyond conventional lattice designs, while large-format AM platforms with in-situ smart control optimize fabrication in real time. The Gentelligence framework captures and leverages process–structure–property–performance knowledge, enabling continuous learning and improvement across production cycles.
These approaches target turbine blades with high structural efficiency, fatigue resistance, and dimensional accuracy. Expected benefits include higher first-time-right print success rates, reduced material waste, improved mechanical reliability in an industrially relevant environment (TRL6) demonstrator, and circular material strategies that enhance recyclability and lower CO₂ emissions.
“By connecting design, materials, and process intelligence, ORGANIC moves beyond biomimicry to true bio-intelligent systems —adaptive, evolving, and capable of delivering high-quality components for sustainable energy applications,” Fernández explained.
Bio-Based Materials and Generative Design
A central innovation in ORGANIC is replacing conventional core materials—such as PET, PVC, and balsa wood—with bio-based thermoplastic composites shaped into lattice architectures via large-format AM. Fernández explained that previous studies have shown fiber-reinforced thermoplastic lattices can match or exceed balsa wood in shear strength, while providing better stiffness-to-weight ratios and improved damage tolerance.
While specific numerical thresholds are still being defined, material selection balances mechanical, thermal, and environmental performance with recyclability and life-cycle impact. However, industrial readiness remains a challenge, as many bio-based composites are still early-stage in terms of mechanical performance, thermal stability, and fiber compatibility.
ORGANIC addresses this by combining detailed material characterization with generative, bio-inspired design workflows and AI-enabled process monitoring, including LSTM-based control systems, to ensure a reliable, high-performing material–design–fabrication chain. Standardized mechanical tests, benchmarking against conventional materials, and validation at AIMEN’s Open Pilot Line and 10XL facility demonstrators will confirm performance under industrial conditions.
Hardware Innovation: The ORGANIC FGF Printhead
Dr. Ander Reizábal López-Para, researcher in Additive Manufacturing of Composites, emphasized that scaling up to large components—produced at several kilos per hour with high-performance or bio-based composites—dramatically changes material behavior and strains process reliability. For ORGANIC, achieving “first-time-right” production isn’t just desirable—it’s essential: every large part must print correctly on the first attempt to avoid wasted time, energy, and material.
The ORGANIC FGF printhead tackles these challenges head-on. Built on a pellet extrusion system, it incorporates advanced hardware, sensing, and control technologies. It enables variable-length fiber reinforcement for weak areas and adaptive temperature control for fast, energy-efficient layer bonding. Embedded pressure and temperature sensors, an infrared camera for thermal monitoring, and a profilometer for layer quality detection provide continuous, real-time data. The self-X control framework allows the system to self-monitor, optimize, and correct issues automatically—always under human supervision.
In the first phase, extensive sensing collects comprehensive process data. “Reliability is a central goal for the ORGANIC project. With limited prior data on large-scale sensing and self-X control, the team is adopting a multi-stage approach,” Reizábal noted. Insights from AIMEN’s Laser Applications Centre pilot stage will inform the design of the second-generation printhead, ensuring robustness, reliability, and readiness for industrial-scale deployment.
AI, Digital Twins, and Continuous Optimization
ORGANIC employs a layered AI system combining reinforcement learning (RL) and cognitive predictive control to continuously optimize the AM process. Pre-print RL agents select configurations based on historical data and sustainability KPIs such as energy use and build time. During printing, a calibration RL agent analyzes sensor data to fine-tune simulations, improving predictive accuracy. Post-print, a long-term RL loop evaluates part quality—porosity, dimensional accuracy, and mechanical performance—and proposes optimized settings for engineers to validate and reintegrate into the system. Simultaneously, a specialized AI-driven cognitive controller will track live sensor data and anticipate process deviations in real time, allowing corrective actions.
“Over successive cycles, this multi-source integration will enable more accurate predictions, better process control, and smarter design choices, continuously enhancing both the reliability of the manufacturing process and the structural performance of wind turbine blade components across generations,” said Fernández and Angosto.
Key challenges include limited real process data, interoperability with diverse simulation tools, and real-time deployment complexities. ORGANIC addresses these by collecting multi-source data at AIMEN’s Open Pilot Line, using a modular approach within the Gentelligence digital framework supported by Asset Administration Shells (AAS) and ontology-based process knowledge, and performing offline testing with safeguards before scaling to the 10XL large-scale demonstrator.
From Wind Turbines to Cross-Sector Potential
Fernández explained that adapting ORGANIC’s bio-intelligent AM approach has cross-sector potential, although mechanical, thermal, and durability requirements vary across industries. ORGANIC addresses this through standardized material characterization protocols and structured property datasets integrated into its CAx design tools. Generative, bio-inspired lattice strategies are scalable but must meet sector-specific loads, geometries, and performance targets, with the CAx tool’s process–structure–property–performance relationships ensuring traceability.
Large-format AM technologies—including hybrid extrusion, multi-material deposition, and in-situ monitoring—are modular, with stepwise validation at AIMEN’s Open Pilot Line supporting scalable deployment to new industrial contexts.
In its first year, the project emphasizes foundational outputs rather than full-scale prototypes. Milestones include a whitepaper on bio-intelligent additive manufacturing, expected in November 2025, consolidating methodologies across materials, design, fabrication, and AI-driven control. By year’s end, manufacturing requirements and digital architecture will be finalized. “From there, the ORGANIC system will advance through successive alpha, beta, and final releases, until its completion in a full demonstrator by the end of the project,” said Fernandez.
3D Printing in Wind Energy Research
The ORGANIC project demonstrates how bio-intelligent AM can produce large, high-performance turbine blades while integrating AI, generative design, and recyclable materials. Complementing this work, recent studies show how 3D printing is being used both to improve blade monitoring and extend the lifecycle of components.
At the University of Groningen, researchers developed a low-cost, scalable approach to detect faults in wind turbine blades using 3D printed models, vibration analysis, and machine learning. They fabricated 300 mm scaled replicas of the NREL 5MW blade using PLA, introducing five types of crack-like damages in critical regions such as the root, mid-span, and transition zones. By simulating real-world damage scenarios and collecting vibration data, they trained support vector machines and k-nearest neighbor algorithms to classify structural faults with over 94% accuracy. This approach illustrates how 3D printed replicas can provide a cost-effective, rapid, and highly accurate method for structural health monitoring and predictive maintenance in wind energy.
Elsewhere, 3D printing is helping extend the lifecycle of decommissioned wind turbine blades. In one project, a retired blade was repurposed into a modular footbridge. Designers from Poly Products, a Dutch company specializing in composite processing, combined the existing composite material with 3D printed connectors to create a durable, adaptable, and sustainable infrastructure solution.
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Featured photo shows The ORGANIC project, which combines bio-based materials, generative design, and artificial intelligence. Photo via AIMEN Technology Centre.
