Digital twins in product life cycles

The main objective of all processes to manufacture high-tech products is compliance with the specified ranges of permissible variation. For this purpose, all data must be recorded that might provide some evidence of status changes anywhere along the process chain. Sensors in machinery and equipment can provide valuable clues as to whether or not the actual values will fall into the tolerance range.

All data from the sensors and the production system will be stored individually for each product, creating a digital twin that retains a full production history including project data and order specifications. Identification systems allow this twin to be assigned to the individual component, making it available for every downstream process step. The extended product data models provide relevant, context-specific data from the manufacturing history for further analyses, accelerating process development and process optimization in the production of prototypes as well as large series.

A specific time and place must be recorded for each set of sensor data about default process parameters, the temperature in the hall or machine vibrations. This still provides technological challenges, but the causes of certain defects can be revealed only if the process can be fully traced back.

• Detailed recording and storage of all relevant process data from the manufacturing chain
• More immediate use of information about manufacturing errors and component defects to identify the critical manufacturing steps
• Customized and adapted repair processes based on the knowledge of entire product histories throughout the product life cycle
• Higher levels of machine availability, lower downtimes and quicker response times following breakdowns through predictive maintenance of machine tools

Mass production of turbomachinery components

More efficient process chains and the provision of evidence for the compliance with certification requirements: these are the objectives of real time production data generation in the mass production of turbine components. The data are gathered through the use of standardized interfaces and are available for simulations and documentation purposes along the entire process chain.

Production and repair of gas turbine blades

Experiences with the application of virtual planning tools such as process simulation and process chain reconfiguration for additive and subtractive manufacturing or repair processes including milling and laser metal deposition (LMD) are analyzed to establish their potential use in the production and repair of gas turbine blades. By providing detailed logs of real data from the processes under review, it is possible to recognize patterns which reveal where adjustments along the process chain may be beneficial. Optimized planning tools benefit from data consistency and ensure high levels of transparency in the planning process.