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КартаВръзкиТемиБългарияНощта (K-TRIO)Мария Склодовска-КюриЗа ученициЗа учителиМетодологияИзненадай ме
HEОбмен на изследователи2024–2027

SynAM · Integration of Advanced Experiments, Imaging and Computation for Synergistic Structure-Performance Design of Powders and Materials in Additive Manufacturing

„Хоризонт Европа“ — Действия „Мария Склодовска-Кюри“

Период
2024-01-01 → 2027-12-31
Финансиране от ЕС
565 800 €
Участници
9
Схема
HORIZON-TMA-MSCA-SE

Линиите свързват координатора с партньорите.

Накратко на български

3D принтирането на материали като магнезиеви сплави и стомани се анализира чрез съчетание от физическо моделиране и машинно обучение. Това помага за подобряване на качеството, здравината и устойчивостта на корозия на произведените части.

Този кратък обзор е генериран от изкуствен интелект

Кратко обяснение, генерирано от езиков модел по текста на CORDIS. Оригиналът е по-долу.

Резултати накратко

Integration of Advanced Experiments, Imaging and Computation for Synergistic Structure-Performance Design of Powders and Materials in Additive Manufacturing

Additive manufacturing (AM), often referred to as 3D printing, is transforming traditional manufacturing by enabling unprecedented design flexibility, rapid prototyping, and customization. However, significant challenges remain, such as controlling material properties, predicting structural integrity, and managing surface quality—particularly regarding corrosion resistance and functional reliability. These challenges limit AM’s broader industrial application, especially with advanced materials requiring precise phase control, like duplex stainless steels, amorphous glass metals, and magnesium alloys. The overarching objective of this project is to integrate state-of-the-art data systems, experimental approaches, imaging techniques, machine learning, and predictive physical modeling to address these critical challenges in AM. By establishing a comprehensive data repository encompassing structural characteristics, defects, distortions, and performance metrics across various material scales, the project aims to significantly enhance materials development and optimize AM processes. Through interdisciplinary collaboration between academia and industry, this initiative seeks to systematically quantify how AM processing parameters and surface treatments affect material integrity and functional properties. The project further intends to develop advanced imaging methods and processing algorithms, ensuring consistent quality control, from initial powder production through final manufacturing. A unique aspect of this project is the fusion of physical modeling with machine learning, enabling predictive design of optimal material compositions and structures. The anticipated outcomes include enhanced printability, improved mechanical and corrosion-resistant properties, and streamlined development cycles. These advancements will substantially impact industrial capabilities, aligning with strategic EU priorities related to Industry 4.0 and Industry 5.0. Ultimately, by fostering international and cross-sectoral knowledge transfer, this project aims to accelerate the application of innovative AM solutions, enhancing competitiveness, sustainability, and technological leadership within the EU and globally.

Текст от CORDIS, на английски · Данни: CORDIS, © Европейски съюз

Цел на проекта

The proposed project aims to collaboratively integrate modern data system, experiments, imaging, machine learning and predictive engineering-physical modelling for additive manufacturing (AM) and materials developments. Through focused knowledge transfer, close interdisciplinary teamwork and fusion of the academic-industrial research/resource, the team will jointly establish a systematic data system of the structure, properties, defects and distortions in AM of a range of materials at different scales and use the data for materials development and AM process optimisation. The effect of AM processing and surface treatments on the surface integrity and functional properties (e.g. corrosion resistance) of AM materials is to be systematically established. The project will develop practical imaging and processing algorithms for the analysis, design, and joint quality control for the input materials in AM, including powder production. Engineering and key physical modelling is to be integrated with machine learning for predictive composition and structure design for optimum synergy between printability, properties and performances. Materials development balancing printability and structure properties will be focused on advanced materials requiring critical phase control in AM, including duplex stainless steels, amorphous glass metals and Mg. The advanced data and materials will serve as a pivoting platform for future research and innovation in AM, speeding up material development within the full product development life cycle. Through focused intersectoral and international knowledge exchange and joint R&I within a multidisciplinary team, the project will contribute to the continuous practical applications of Industry 4.0 technologies and development for industry5.0 in AM, further enhancing the design freedom in composition and structure for application-specific products, and accelerating the researcher development with lasting impact in the EU and beyond.

Оригинален текст от CORDIS (на английски).

Участници

Връзки

Данни: CORDIS, © Европейски съюз