ExpertiseUpdated on 28 May 2025
Increasing Material Efficiency in Sinter-based Additive Manufacturing
About
My focus is on sinter-based additive manufacturing (AM) processes, which combine powder injection molding (PIM) with AM to offer innovative approaches for circular value creation in production. In my team, we have developed a 3D printer capable of processing PIM series material, particularly scrap, without requiring an upstream material preparation step. This allows us to automatically conduct rheological measurements and identify process parameters efficiently. Another area of focus is the application of AI, especially computer vision techniques, for in-situ process control, such as defect detection, and the analysis of key part properties, like density. Our aim with this is to increase material efficiency in both product development and production.
Field
- (Advanced) Materials and additive manufacturing
- (AI based) process and system control technologies
- AI-driven diagnostic systems
- Industry 4.0 technologies
- Manufacturing and machine learning
Organisation
Similar opportunities
Expertise
Expert in Additive Manufacturing & Shape Memory Alloys for Circular Value Creation
Nazim Babacan
Associated Professor at Sivas University of Science and Technology
Sivas, Türkiye
Project cooperation
Al and Circular Economy in Coating and Insulation
- Consortium seeks Partners
- Data technologies | Assistance and Expert systems
- Enabling technologies | Network design of reverse supply chains
- Data technologies | (AI based) process and system control technologies
- Data technologies | (AI based) Material and Product Design, Decomposition and Separation
- Enabling technologies | Life cycle assessment / Product life cycle management – e.g., Digital Twin / Digital Product Passport
- 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)
- 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.
Kaan AKSOY
Advanced Technologies and Sustainability R&D Department at BETEK BOYA VE KİMYA SANAYİ A.Ş
İSTANBUL, Türkiye
Project cooperation
- Early idea
- Data technologies | (AI based) Material and Product Design, Decomposition and Separation
- Enabling technologies | Life cycle assessment / Product life cycle management – e.g., Digital Twin / Digital Product Passport
- Enabling technologies | Reverse Manufacturing (e.g. adaptive automation for high variance, sorting, sophisticated logistic systems)
- Data technologies | (AI based) recognition systems (e.g. image recognition) to evaluate materials, components and products and determine the best use paths
Rebecca Pahmeyer
Research Scientist at Fraunhofer IPA
Stuttgart, Germany