Periodic Reporting for period 1 - PROMATAI (Development and testing of innovative solutions for the processing of hybrid materials and nanomaterials using artificial intelligence algorithms)
Período documentado: 2024-01-01 hasta 2025-12-31
The PROMATAI project addresses the critical need for advanced manufacturing solutions in processing complex hybrid materials and nanomaterials. Traditional extrusion technologies often struggle with materials that have unique rheological properties, such as high viscosity or variable particle sizes, leading to energy inefficiency and material waste. Coordinated by the Łukasiewicz Research Network – Institute of Polymer Materials (Poland), the consortium includes academic and industrial partners from Poland, Slovakia, Greece, Lithuania, and Bulgaria.
The project aims to exchange scientists, strengthen cross-border and cross-sector collaboration, and develop the world’s first “smart extruder” prototype. This innovative machine uses Artificial Intelligence (AI) algorithms to adjust its internal geometry in real time based on the material’s characteristics. It will improve efficiency, reduce waste and energy consumption, and enable processing of complex materials—ranging from polymers and composites to bio(nano)composites—used in food, cosmetics, packaging, construction, and chemical industries.
R&D focuses on three novel extruder components: the Feed Opening Section (FOS), Adjusted Grooved Feed Section (AGFS), and Rotating Barrel Section (RBS). Prototypes will be tested with bio(nano)composite pellets to generate data for machine learning models, enabling flexible, optimized control of extrusion processes. The results will benefit researchers, entrepreneurs, and industry by improving productivity, homogenization, and sustainability. A review of the market and literature confirmed that no similar “smart extruder” exists, ensuring the project’s novelty.
The project aims to exchange scientists, strengthen cross-border and cross-sector collaboration, and develop the world’s first “smart extruder” prototype. This innovative machine uses Artificial Intelligence (AI) algorithms to adjust its internal geometry in real time based on the material’s characteristics. It will improve efficiency, reduce waste and energy consumption, and enable processing of complex materials—ranging from polymers and composites to bio(nano)composites—used in food, cosmetics, packaging, construction, and chemical industries.
R&D focuses on three novel extruder components: the Feed Opening Section (FOS), Adjusted Grooved Feed Section (AGFS), and Rotating Barrel Section (RBS). Prototypes will be tested with bio(nano)composite pellets to generate data for machine learning models, enabling flexible, optimized control of extrusion processes. The results will benefit researchers, entrepreneurs, and industry by improving productivity, homogenization, and sustainability. A review of the market and literature confirmed that no similar “smart extruder” exists, ensuring the project’s novelty.
The project focuses on the development of three key “smart” extruder components: the Feed Opening Section (FOS), the Adjusted Grooved Feed Section (AGFS), and the Rotating Barrel Section (RBS), aiming to enable AI-driven, adaptive extrusion of complex materials and the assembly of a prototype of the complete “smart” extruder.
Work Package 1 (WP1): Feed Opening Section (FOS). Research activities for WP1 were completed between January 2024 and February 2025. The main achievements include:
1. Material research (Task 1.2): Selection and production of specialized bio(nano)composites with varying granule sizes (see Figure 1) and physical properties to enable comprehensive testing of material flow behaviour.
2. Virtual modelling and simulation (Task 1.1 1.3 1.4): Development of 2D and 3D CAD models and numerical simulations to determine throughput limits, analyse material flow, and optimise design parameters for different material systems.
3. Prototyping (Task 1.5): Fabrication of four full-scale 3D-printed FOS models (see Figure 2) to validate kinematics, adjustable feed mechanisms, and throughput control under diverse operating conditions.
4. Hardware manufacturing (Task 1.6): Design and production of a universal metal FOS prototype incorporating adjustable wedges (see Figures 3 and 4) and an Intelligent Feeding System. Machine learning algorithms support real-time adaptation to material properties, ensuring stable flow, accurate throughput, and optimised process performance.
Work Package 2 (WP2): Adjusted Grooved Feed Section (AGFS). WP2 activities started in April 2025 and will be completed in February 2026. This work focuses on improving material transport and mixing quality through a feed section with longitudinal grooves. Achievements to date include:
1. Development of detailed virtual models and complete technical documentation for the AGFS design (Task 2.1 2.2).
2. Successful numerical simulations optimising groove geometry to enhance throughput control and mixing quality during composite processing (Task 2.3).
3. Production of a 1:1 scale functional 3D-printed AGFS model (see Figure 5) to verify adjustable groove parameters and operational feasibility (Task 2.4).
During the reporting period from 1 January 2024 to 31 December 2025, the consortium carried out ten international secondments and six workshop-training sessions, supporting scientific collaboration and knowledge exchange in artificial intelligence, polymer processing, intellectual property, environmental protection, and equality-related aspects relevant to research environments.
Overall, at this stage, the project has established a strong technical foundation for the development of a “smart” extruder, validating innovative solutions through simulation and prototyping and paving the way for further testing and patenting.
Work Package 1 (WP1): Feed Opening Section (FOS). Research activities for WP1 were completed between January 2024 and February 2025. The main achievements include:
1. Material research (Task 1.2): Selection and production of specialized bio(nano)composites with varying granule sizes (see Figure 1) and physical properties to enable comprehensive testing of material flow behaviour.
2. Virtual modelling and simulation (Task 1.1 1.3 1.4): Development of 2D and 3D CAD models and numerical simulations to determine throughput limits, analyse material flow, and optimise design parameters for different material systems.
3. Prototyping (Task 1.5): Fabrication of four full-scale 3D-printed FOS models (see Figure 2) to validate kinematics, adjustable feed mechanisms, and throughput control under diverse operating conditions.
4. Hardware manufacturing (Task 1.6): Design and production of a universal metal FOS prototype incorporating adjustable wedges (see Figures 3 and 4) and an Intelligent Feeding System. Machine learning algorithms support real-time adaptation to material properties, ensuring stable flow, accurate throughput, and optimised process performance.
Work Package 2 (WP2): Adjusted Grooved Feed Section (AGFS). WP2 activities started in April 2025 and will be completed in February 2026. This work focuses on improving material transport and mixing quality through a feed section with longitudinal grooves. Achievements to date include:
1. Development of detailed virtual models and complete technical documentation for the AGFS design (Task 2.1 2.2).
2. Successful numerical simulations optimising groove geometry to enhance throughput control and mixing quality during composite processing (Task 2.3).
3. Production of a 1:1 scale functional 3D-printed AGFS model (see Figure 5) to verify adjustable groove parameters and operational feasibility (Task 2.4).
During the reporting period from 1 January 2024 to 31 December 2025, the consortium carried out ten international secondments and six workshop-training sessions, supporting scientific collaboration and knowledge exchange in artificial intelligence, polymer processing, intellectual property, environmental protection, and equality-related aspects relevant to research environments.
Overall, at this stage, the project has established a strong technical foundation for the development of a “smart” extruder, validating innovative solutions through simulation and prototyping and paving the way for further testing and patenting.
As of 31 December 2025, the first smart extruder component, the Feed Opening Section (FOS), has been designed and manufactured in metal. A 3D-printed model of the second component, the Adjusted Grooved Feed Section (AGFS), has been produced, with its metal prototype expected by February 2026, according to plan.
Throughput tests showed that multiple metal prototypes would be inefficient, so a single universal FOS prototype was developed, featuring an adjustable feed opening (50–90 mm) and an Intelligent Feeding System. Machine learning algorithms enable real-time adjustments for different materials, particle sizes, and properties, optimizing flow and process stability.
The metal FOS will be used in the plasticizing system prototype and the complete smart extruder pilot. A patent is being prepared for the FOS delivery method, complementing five patent applications on extruder screws. The design works for granules and micro-/nanoscale powders, improving flow, preventing clumping, and adaptable to twin-screw extruders.
The AGFS concept, with longitudinal grooves and variable parameters, allows wide-range throughput adjustment and improved mixing. From March 2026, work will start on the third component, the Rotating Barrel Section (RBS), and assembling the extruder prototype.
Throughput tests showed that multiple metal prototypes would be inefficient, so a single universal FOS prototype was developed, featuring an adjustable feed opening (50–90 mm) and an Intelligent Feeding System. Machine learning algorithms enable real-time adjustments for different materials, particle sizes, and properties, optimizing flow and process stability.
The metal FOS will be used in the plasticizing system prototype and the complete smart extruder pilot. A patent is being prepared for the FOS delivery method, complementing five patent applications on extruder screws. The design works for granules and micro-/nanoscale powders, improving flow, preventing clumping, and adaptable to twin-screw extruders.
The AGFS concept, with longitudinal grooves and variable parameters, allows wide-range throughput adjustment and improved mixing. From March 2026, work will start on the third component, the Rotating Barrel Section (RBS), and assembling the extruder prototype.