BBG research project develops self-learning automation

A system partner for the plastics processing industry, BBG is collaborating within the framework of a research project to develop production automation based on artificial intelligence (AI).

The project, named ‘EKI - Engineering für die KI-basierte Automation in Produktionsumgebungen (Engineering for AI-based Automation in Production Environments)’, is funded by the dtec.bw – Zentrum für Digitalisierungs- und Technologieforschung der Bundeswehr (Center for Digitization and Technology Research of the German Armed Forces).

Other partners are the Helmut Schmidt University/University of the Federal Armed Forces Hamburg and software specialist Weidmüller Interface GmbH & Co. KG. 

The project is designed to enable the self-learning adaptation of production systems to changing requirements and environmental conditions in order to manufacture new variants of a product.  

At BBG, an end-to-end line for finishing glass with polyurethane (PUR), one of BBG's core competencies, is being set up to demonstrate and test the research results. This includes various automation modules for preparing the encapsulation process, such as component priming and flash-off. This is complemented by the PUR encapsulation station, consisting of a metering machine, mould carrier system and encapsulation mould with automated release agent application. The feeding of inserts and the removal of finished components are also handled automatically. At the end of the process chain, there are further automation modules equipped with AI-based algorithms for finishing and quality control. 

As early as 2020, BBG presented intelligent tools for use in industry 4.0 applications and in the smart factory environment. 

In total, the project partners have defined five specific use cases for which the research results will be implemented in everyday industrial practice and validated with the help of BBG's fully automated production line. Self-learning processes are to be developed for primer application during production preparation and trimming of the parting plane and sprue surface during finishing. Cloud-based formulation management, autonomous detection of the need for preventive maintenance and optimisation of resource consumption via an energy management system are further research priorities.

The various tasks are scientifically supported in individual doctoral theses by the Helmut Schmidt University. The project is initially scheduled to run until 31 August 2024. For BBG, the use of AI is the next logical step in the further development of their own product range. 

The background to the research project is the increasing pressure on manufacturers. So far, individual aspects such as line modularization, intuitive software, the improvement of mechatronic components and parameter optimization have been investigated.

The "EKI" research project is seeking a comprehensive solution. This is because AI and machine learning (ML) are currently giving rise to new automation approaches that enable production processes to be created automatically and adapted to new requirements.

The project partners are researching how such software components can be combined into end-to-end solutions that can be used in as many industrial fields of application as possible.