Cognitive optimization of an automotive rear-axle drive production process

While optimizing tolerances in tolerance chains only single characteristics or objectives of single process steps are considered, there is no information exchange across all processes. Interdependencies between processes, materials, means of production and individuals acting in this environment as well as their effect on product variations are usually not fully understood. In order to face a dynamisation of process specification, interdependencies have to be identified and integrated in future production. The holistic consideration of the process chain focused on the allocation of tolerances allows detection of correlations and interdependencies in the production process itself. By this, process chain information is traced back to conduct the right optimizations at the right place in the process chain. But therefore intelligent controlling mechanisms are needed to analyze and optimize even complex production systems with multi-level interdependencies. Such a cognitive system is able to form the core of self-optimizing production system. Using this cognitive system, the production process of an automotive rear-axle drive is optimized in order to minimize disturbances created by structure-borne sound emissions. Therefore several cognitive technologies have been evaluated to fulfil specific tasks in process optimization