ExpertiseUpdated on 2 July 2025
ROBOTIC ADDITIVE MANUFACTURING OF NATURE-INSPIRED, TOPOLOGY OPTIMIZED, AND MULTIFUNCTIONAL SMART MATERIALS-BASED DESIGNS
About
This multidisciplinary research project pioneers a novel design and manufacturing framework that merges nature-inspired principles, multifunctional smart materials, and topology optimization with multiaxis robotic additive manufacturing technologies.
The project establishes a comprehensive methodology that draws from nature-inspired structures to create lightweight, structurally efficient, smart, environmentally-friendly functional materials with sensors and integrated electronics. It integrates topology and field optimization techniques to reduce material use while maintaining mechanical performance in applications ranging from aerospace parts to smart consumer and artistic products produced by a multi-axis robotic additive manufacturing system.
The expertise aligns with Technology Readiness Levels (TRL) 2–4. The project goal is for the 3-year project to produce a prototype that reaches TRL 6–7 validation to enable commercialization.
Field
- (Advanced) Materials and additive manufacturing
Attached files
Organisation
Similar opportunities
Project cooperation
- Already defined
- Expertise offered
- Consortium seeks Partners
- Enabling technologies | (Advanced) Materials and additive manufacturing
- Enabling technologies | (Advanced/smart) Sensors, e.g., enabling materials, components and product flows measurement
Dante Jorge Dorantes Gonzalez
Professor at MEF University
Istanbul, İstanbul, Türkiye
Project cooperation
Expertise in Additive Manufacturing & Advanced Materials for Lightweighting Technologies
Nazim Babacan
Associated Professor at Sivas University of Science and Technology
Sivas, Türkiye
Project cooperation
- Early idea
- Data technologies | Assistance and Expert systems
- Enabling technologies | Network design of reverse supply chains
- Data technologies | Design of an adaptable Digital Product Pass:
- Enabling technologies | Robotic / handling - and assistance systems
- Data technologies | (AI based) process and system control technologies
- Enabling technologies | (Advanced) Materials and additive manufacturing
- Data technologies | (AI based) Material and Product Design, Decomposition and Separation
- Data technologies | Approaches to support SME fully exploit the value of existing CVC-related data
- Enabling technologies | (Advanced/smart) Sensors, e.g., enabling materials, components and product flows measurement
- Data technologies | Interoperability of CVC-relevant data ecosystems, quality assurance and traceability across systems
- Data technologies | Algorithm that shows the (positive) impact of a Circular Economy process or Circular Economy product
- Enabling technologies | Industry 4.0 technologies (IoT, big data analytics) for monitoring and managing circular value chains
- Enabling technologies | Life cycle assessment / Product life cycle management – e.g., Digital Twin / Digital Product Passport
- Enabling technologies | Tools and solutions addressing challenges emerging from product focused regulations (such as the ESPR)
- Data technologies | Simulation models and predictive analytics to assess the scalability of circular processes across industries
- Enabling technologies | Reverse Manufacturing (e.g. adaptive automation for high variance, sorting, sophisticated logistic systems)
- Enabling technologies | AI-driven diagnostic systems, e.g., for assessing the viability of reused, remanufactured, and recycled components
- Data technologies | (AI based) recognition systems (e.g. image recognition) to evaluate materials, components and products and determine the best use paths
- Data technologies | Data ecosystems for the realisation of circular value creation exploiting the full potential of digitalisation – e.g., harnessing existing, purpose-built platform solutions.
- Enabling technologies | Manufacturing and machine learning, e.g., to increase the flexibility of industrial processes, modular approaches, reduce use of materials, quality assurance and certification of products)
Dr. Huseyin KOYMATCIK
R&D Manager at Barkom Group AŞ.
ankara, Türkiye