StarMon – Stabilisation of starting assembly systems

 

Today's production landscape is characterised by an increasing variety of products, resulting from a strong customer orientation and the demand for individualised products. The steadily increasing share of mechatronic components, which combine mechanical, electrical, but also software functions, further increases the complexity of products. Finally, product life cycles are becoming shorter, as product updates are expected more frequently. This poses a major challenge for management and has a direct impact on the requirements for the design and operation of production systems. Due to the more frequent adjustments to products and processes, ramp-up management and the monitoring and control of product manufacturing processes are becoming increasingly complex. As a result, around 60% of production ramp-ups still fail to meet their targets. This is partly due to the dynamics and complexity of a production system, which make it difficult to monitor and predict the impact of decisions. Ultimately, this leads to instabilities in the production system and the resulting costs.

The aim of this research project is to ensure quality and quantity performance by evaluating and adapting the ramp-up strategy in a stability-oriented manner.

For this purpose, the production ramp-up is mapped in a system-dynamic model together with the relevant stability and disturbance factors. These factors are to be incorporated into a design model that provides decision support for start-up management with the help of an artificial neural network. As a result, the state of the production system can be analysed at any time during start-up to select a suitable strategy for stabilising the production system.