The worst-case scenario for every series producer is a production standstill. A downtime of five minutes in automotive production, for example, causes average costs of 100,000 euros. In this context, maintenance and servicing play an important role. To prevent machines, systems and production logistics from breaking down, they must be serviced in good time.

With the help of sensor, transmission and data storage technology, predictive maintenance of production processes is already a reality in some sectors and shows great potential, especially in the context of Industry 4.0.

Generic action guide

For successful predictive maintenance applications, close cooperation and partnership between series producers and toolmaking companies is essential. Together, the benefits of tool and process knowledge can be synergistically developed and utilized in series production. To enable companies to independently develop and offer predictive maintenance systems and services in the field of predictive maintenance, the Corporate Development department of the Chair of Production Systems at the Laboratory for Machine Tools and Production Engineering WZL at RWTH Aachen University has therefore developed a generic guideline for action in cooperation with series producers and toolmaking companies.

The guideline's target group includes, in particular, companies that are experiencing increased tool-related failures in their series production due to repeated, unforeseen disruptive influences. Predictive maintenance can help to predict malfunctions such as tool failure and derive specific measures based on this. This gives toolmaking companies the opportunity to expand their existing service portfolio with predictive maintenance solutions and tap into additional business areas.

Procedure for company-specific implementation

The functionality of predictive maintenance is based on the collection, transmission, storage and near-real-time utilization of extensive amounts of data. Based on complex analysis processes and algorithms, deviations in the recorded operating parameters of a machine-tool system can be identified and necessary maintenance can be anticipated. As both the technical implementation and the embedding of the technical solutions in the existing product and service portfolio often pose major challenges for series producers and toolmaking companies, the guide is based on a comprehensive study which, in addition to concrete research results, is also based on the expert knowledge of the participating partners from industry.

The generic guideline presents a systematic approach to the development of predictive maintenance solutions in three phases with a total of six steps. As part of the analysis phase, all relevant prerequisites and requirements for a predictive maintenance solution are first recorded. In the design phase, these are translated into tool, infrastructure and service solutions. Finally, the implementation phase involves commissioning, teaching the algorithm and defining interaction points and workflows.

Great potential for machine availability

Procedure for developing a predictive maintenance solution

© WZL

During the development of the guideline, it became clear that the use of a predictive maintenance solution offers great potential for increasing machine availability. A significant reduction in unplanned downtime also reduces maintenance costs. This enables more predictable, condition-based maintenance in series production. At the same time, toolmaking companies have the opportunity to expand their range of services, effectively differentiate themselves from the competition and increase their profitability. By cooperatively developing predictive maintenance solutions, both sides can benefit equally from corresponding service concepts.