InnoLight

Motivation

Digital twins are being researched and applied in various forms. They mirror their real-world counterparts and their condition in a virtual world and can communicate bidirectionally with their physical counterparts. It is crucial that the properties, boundary conditions, and influences are accurately represented. The mapping of components, such as machine components, made of fiber-reinforced plastic composites (FRP) poses a particular challenge. Due to their heterogeneity and anisotropic and complex material behavior, the modeling of thermal and mechanical properties and behavior is still the subject of research today.

Due to their advantageous properties, FRPs, especially carbon fiber reinforced plastics (CFRPs), are increasingly being used in machine tools and manufacturing equipment for dynamic and precise manufacturing processes. In addition to their low density and high specific stiffness and strength, these non-metallic materials have high material damping, which helps to reduce vibrations during operation. Furthermore, compared to metallic materials, FRPs offer expanded possibilities for integrating additional functionalities and elements into the structure, as the material is created during the manufacturing process of the components.

Objective

The aim of the project is to enable data-based process optimization through innovative lightweight machine components with integrated sensor technology. This will enable the creation of a basis for data-based applications while optimizing the thermal and dynamic behavior of the innovative lightweight machine components, which is important for manufacturing processes. As part of the project, the innovative concepts to be developed for optimization and sensor integration in components for a CFRP motor spindle and for the construction of a lightweight cantilever beam for a laser cutting system are to be implemented. The measured values from the sensor-integrated lightweight components serve as input values for modeling as a basis for digital twins. Together with the sensor-integrated lightweight components, these digital twins in turn form the basis for data-driven and efficient process optimization in machine tools and the application of digital services to increase throughput, manufacturing quality, and energy efficiency.

A smart performance test bench to be developed as part of the project will also be used to test the standardized provision of component data using Asset Administration Shell (AAS). This will enable component-specific data to be made available automatically during initial commissioning. The properties determined by measurement during initial commissioning on the smart test bench will then be automatically exchanged with the AAS of the CFRP motor spindle via standardized interfaces to be developed. The data determined in this way and provided via the AAS will then be available for instantiating digital twins of the CFRP motor spindle. The routines for instantiating a digital twin to construct a smart laser cutting system with the sensor-integrated lightweight cantilever beam are to be developed using similar approaches for a complete production system.

Approaches

The solution for developing innovative lightweight machine components for data-driven processes comprises four approaches:

• sensor integration in machine components close to the process zone,
• the expansion of component models to data-driven digital twins,
• the implementation of standardized interfaces for data provision to laser cutting systems and the motor spindle performance test bench,
• further development of machine components for dynamically and thermally optimized behavior.

The focus here is on the development of innovative, functionally integrated lightweight solutions for a cantilever beam and a CFRP motor spindle.

Funding source

Consortium partners