Operational and online planning of maintenance and production

• Applicant: Professor Dr.-Ing. Berend Denkena
• Subject Area: Metal-Cutting and Abrasive Manufacturing Engineering
• Term: from 2014 to 2017
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 257820368

The central conflict of production and maintenance is the fact that maintenance measures have to ensure a high availability of machines. However, for their implementation a machine downtime is often required at short notice. Especially in complex, multi-stage and high utilized production systems, the conflicting behavior of production and maintenance planning is particularly pronounced, since the machine downtime can quickly cause production losses and failure consequences for the entire production system. Existing scientific approaches do not solve these issues, since the complexity of real production systems is mapped inadequate and under very restrictive assumptions (e. g. single machine approaches, deterministic and static decision environment).In the framework of the DFG-funded scientific project MK-ProInst, a dynamic planning approach to coordinate the production and maintenance planning was developed and implemented by means of discrete event simulation (DES) at the Institute of Production Engineering and Machine Tools (IFW). This approach allows a robust prediction and quantification of the resulting failure consequences that arise when implementing a maintenance measure during production. Moreover, planning alternatives can be derived and evaluated (e. g. alternative maintenance starting time). A validation of the approach showed that the production and maintenance costs can be reduced by up to 9 %. Furthermore, the planning quality is significantly increased since the complexity, dynamics and stochastics of real production systems are considered using the simulation technology in decision making. The basis for industrial exploitation of the methodology is to reduce the time needed for model creation and adaption and to show a low model validity at an early stage.The goal of the knowledge transfer project is the exploitation of the dynamic planning approach to achieve the essential foundation for an efficient and online application for a variety of companies. For this, the existing planning approach will be expanded with regard to methods for learning and self-parameterization of process-oriented simulation models based on data from BDE-/MDE-Systems (e. g. for stochastic data). In addition, appropriate feedback loops will be developed to the timely recognition of a low model validity. These aspects are the basis for time-efficient model creation and adaption and the operational (online) application in practice.

• DFG Programme: Research Grants (Transfer Project)
• Participating Institution: Volkswagen AG; WEBfactory GmbH