The project goal is to provide an approach for a supply-chain- and lifecycle-wide concept that enables producing companies implementing additive manufacturing in an economical way.
The aim of the CLLAIM project (Creating KnowLedge and SkilLs in AddItive Manufacturing) is to develop a standardized education and training system within the EU for skilled workers in the field of additive manufacturing.
Within the ULTRAREIN project, a multi-sensor system is being developed with which the measured variables of the ultrasonic bath can be recorded under comparable boundary conditions in order to optimize the cleaning result.
Conventional manufacturing techniques are to be supplemented by additive manufacturing in the form of high-throughput line production systems in order to create a continuous digital process chain from design to product manufacture.
Intelligent search and gripper system: combining additive manufacturing, sensor and control technology, and artificial intelligence with a bionic approach.
Agile methods for the model-based development of components for electromobility. They enable a comprehensive and consistent view of the entire life cycle and take production and operation into account.
The research project REIF - Resource-Efficient, Economic and Intelligent Foodchain investigates the potential of Artificial Intelligence (AI) for optimizing the planning and control of value creation in the food industry.
Projects in the field of factory planning and evaluation
Kokos
Helicopter rotor blades in prepreg construction are very expensive and have a high manual effort. Goal: Illustration and optimization of the material flow / production layout.
"SynErgie" is one of four projects nationwide within the funding initiative "Kopernicus projects for the energy turnaround" and the only project in the funding line "Alignment of industrial processes to fluctuating energy supply".
The HErzSchLag project (highly automated preform production by layer-by-layer layering) combines different processes for the cost-effective production of CFRP components.
In industry, natural aging effects cause energy efficiency deficits that often go unnoticed. We detect such efficiency deficits automatically with machine learning methods.
Intelligent search and gripper system: combining additive manufacturing, sensor and control technology, and artificial intelligence with a bionic approach.
Fraunhofer Institute for Casting, Composite and Processing Technology IGCV