Production engineering

Projects on the research topic “Production Engineering

Research focus

Handling technology

In the manufacture of products made of fiber-reinforced plastic composites(FRP), positioning accuracy or reproducibility when depositing the reinforcing textile semi-finished products or fiber bundles is a decisive quality criterion for the properties of the entire subsequent component. The alignment of the fiber bundles relative to each other, as well as of textile semi-finished products as a whole, must be carried out according to the component load. The flexible characteristics of textile semi-finished products pose a particular challenge. In order to reduce the production time as well as to increase the quality of the fiber composite components at the same time, the use of handling and automation solutions in the process chain for the production of FRP is required.

To achieve this goal, handling and automation technologies for flexible materials are being researched and developed at ForWind member BIK. Especially for the production of rotor blades of wind turbines, with the increasing demands on quality and processing speed, positive effects can be achieved by using these technologies.

Condition monitoring of the FKV

The ability to adjust specific strength and stiffness almost at will – and, above all, in a problem-oriented manner – by means of layer structure gives fiber-reinforced plastic composites(FRP) a high potential for design. Component weight can be minimized by problem-oriented variation of fiber, matrix and layer structure of the FRP. With the growing use of fiber-reinforced plastic(FRP) composites, sudden and unforeseen material failure is a major problem. In ductile materials, cracks generally grow continuously; in fiber-reinforced composites, externally invisible damage, such as delamination, inter-fiber fractures or fiber breaks, can occur above a damage-critical energy input. These result in reduced normalized residual strength of a fiber composite. If a critical damage level is reached, structural integrity failure can suddenly occur.

The investigations at BIK deal with the detection and localization of impact events. From recorded signals at local sensors, the load due to the impact, such as impact force curve and maximum amplitude, is fully reconstructed. By means of frequency analysis of the time signals, an evaluation of the damage relevance of the impact is finally carried out.
This opens up a new and exciting field of application, especially for rotor blades of wind turbines.

Intelligent maintenance

The physical measurement of vibrations on rotating machine elements of wind turbines via accelerometers has become widely accepted. Furthermore, mathematical methods for vibration and oscillation analysis are relevant, which allow the automatic assessment of the bearing condition. In this context, information and service life estimates must be condensed in such a way that they can be used operationally in the operative maintenance processes, for example by prioritizing different plant conditions in order to improve planning and control aspects of maintenance and spare parts logistics.

Pre-emptive maintenance of offshore wind turbines

The maintenance of offshore wind turbines (WTGs) is very complex and particularly cost-intensive, as it depends on numerous imponderables. Weather and availability of transportation resources, among other factors, can result in delays in maintenance and repair. This can quickly result in unplanned shutdowns at offshore wind farms. Per plant and day and with a good breeze, this can quickly lead to high yield losses. Solutions are offered here – as a basis for forward-looking planning and action – among other things through better insights into the current technical status of the plants and their components, through the development and use of further data sources, and through the increased integration of experience-based knowledge into planning. In order to make maintenance more effective and efficient, a comprehensive system is required for planning and controlling as well as for supporting maintenance processes and the accompanying logistical processes.

With the help of artificial intelligence and automated data analysis, tools and methods have been developed to support stakeholders in planning and control decisions and to enable a predictive (“preagent”) maintenance strategy. Offshore maintenance processes were recorded and analyzed, and data sources for automated decision support were identified. For example, sensor values, statistical data, maintenance data from the plant’s life cycle file, employee knowledge, weather data, as well as stock levels and availability of personnel and means of transport flow into a software module of the system, where they are recognized, prioritized, processed and automatically linked with each other. The system can also learn from mistakes. It also always considers the logistics processes and relies on decentralized control systems here. The overall result: automated decision support based on a currently best possible forecast for dynamic maintenance scope planning and scheduling into workflows.