Accelerated Production Ramp-Up Utilising Clustering and Visualisation of Process Chain Interrelationships

Abstract

With the increasing demand for electric cars, automobile manufacturers and suppliers are ramping up more and more plants for the production of electric traction motors. In the development of new plants, Industry 4.0 aspects are usually already considered, resulting in a large number of integrated sensors and thus a relatively high data availability. These process data in turn contain implicit knowledge that can be used to accelerate the ramp-up phase and to optimise the later series production. Especially in times characterised by frequent process adaptations and continuous learning, increasing knowledge can bring competitive advantages. This particularly applies to the automotive production of electric traction motors, which is currently confronted with ever changing product concepts, low production experience and uncertain quantities. Therefore, this paper presents a method for a simple, holistic visualisation of process chain interrelationships, which can already be used during series ramp-up and the associated limited data amount. By using suitable visualisation and clustering methods, process knowledge can be increased, ramp-up times be shortened and production be improved in the long run.