The transition to a circular economy, the re-use of products, requires an efficient collection and identification of used products. What do they consist of? What is usable? Every product is unique due to its history. Nevertheless, it is often similar to its successors. To facilitate identification, an artificial intelligence (AI) supports the identification of the product in the "EIBA" project.

Teamwork with artificial intelligence

At the end of a utilization phase, there are various disposal or processing strategies for a product. Depending on their type and condition, products can be recycled or processed and reused. For this purpose, they must be clearly identified and evaluated. The challenge here is that many product models differ only slightly from one another and are difficult to identify due to contamination and wear. In addition, the professional personnel only has a few seconds to identify and evaluate them.

In order to support people in their work or to train new employees, EIBA’s project consortium wants to provide them with a machine. It is supposed to support looking at and evaluating the product. Sensor-based data is evaluated with the help of artificial intelligence in combination with other information and formulated into a decision recommendation. Thanks to the dual control principle of man and machine, both the error rare during identification and the strain on people is to be reduced.

Self-learning technology

The aim of the EIBA is to develop a machine for the identification and condition assessment of old parts. This will make an important contribution to closing the cycle through digital technologies. By using methods of Artificial Intelligence - such as machine learning as well as deep learning - the machine should be able to identify products and compare them with other available information. By continuously expanding the data, it should also be able to adapt to new products and requirements. In all of this, the human being should not be replaced by the machine but supported.

The innovation of the project is, among other things, to enable cooperation between man and machine in order to combine the competences of both as well as overcome the obstacles and difficulties in sorting and evaluation. The resulting system will be analysed according to sustainability aspects: What has changed for the worker? What additional environmental burdens are initially caused by the use of machines, and how great are the environmental benefits gained through increased efficiency?

Economic use of data

In EIBA, engineers from different disciplines work together to look at the challenges from different perspectives and make the best possible use of the potential. The Fraunhofer IPK focuses on the image-assisted recognition of products. The emphasis is on the balance between accuracy and costs to be met. Further existing market information about the products and their added value for identification is analysed by the TU Berlin and transferred into a common database. C-ECO bundles the knowledge gained and implements it in a process suitable for industry. The impact of the system on sustainability is quantified by the TU Berlin. The German Academy of Science and Engineering makes the project results available to other industrial sectors by asking for their requirements at the beginning of the project and discussing the results together at the end.



Project flyer of the funding measure (German / English) (March 2021)
The project flyers offer an insight into the contents and goals of the ReziProK projects and present first results in each case.

Project sheets of the funding measure (German) (September 2019)
The project sheets provide a brief overview of the individual projects and their goals.


Contributions to the ReziProK Kick-off event in December 2019

Poster - in German (December 2019)

Presentation - in German (December 2019)