Production efficiency in small batches

Key Info

Basic Information

01.03.2017 to 29.02.2020
Organizational Unit:
Chair of Production Engineering, Innovation Management
European Regional Development Fund ERDF

Research partner

    • StreetScooter GmbH
    • e.GO Mobile AG
    • LBBZ GmbH
    • TRUMPF GmbH + Co. KG
    • FH Aachen

The aim of the research project "Production efficiency in small series" - ProeK - is to increase efficiency in small series production using the example of electric vehicle production. The research project KMUProduction.NET was funded within the framework of the previous Objective 2 programme 2007 - 2013 in order to enable small and medium-sized enterprises from NRW to produce components, electric vehicles and small vehicles. Building on these results, the ProeK research project is investigating novel, practice-oriented technical solutions in order to produce small series in the future field of electromobility as cost-effectively, efficiently and flexibly as possible. The research project is subdivided into two subprojects, the outer skin and the bodywork, in order to map increases in efficiency in the production of key components. The partners involved cover the competencies required to successfully complete the project: StreetScooter and e.GO Mobile - electric vehicle manufacturer, the LBBZ - component manufacturer body and joining technology expert, the FH Aachen and the WZL of the RWTH Aachen (research partner) as well as Trumpf as an associated partner (manufacturer of laser-based production systems). ProeK is financially supported by the European Fund for Regional Development - EFRE.

Within the framework of the bodywork subproject, requirements for welded plug connections as well as those for a tolerance concept are determined in order to develop a qualified tolerance concept for low-device joined plug connections based on this, which takes into account the welding process and design guidelines.
Within the framework of the subproject outer skin, a framework for integrated adjustment process planning will be created and an adaptive tolerance management and adjustment concept based on methods of machine learning will be developed.