Two experts from Karlsruhe Institute of Technology (KIT) give insights how the production of the future can be designed more economical and sustainable.

AI-based geometric similarity search for systematic component reuse

Increasing variant diversity, customized products, shortened product life cycles and enormous competitive pressure - these are just a few of the challenges facing companies today. For the area of product development in particular, this means bringing new products to market as quickly and cost-effectively as possible in accordance with individual customer requirements. Products are therefore often developed in generations in order to build on proven solutions and thus knowledge in new generations and to reduce risks, for example with regard to functionality or also producibility. In the course of digitization, many companies therefore already have a large amount of data, for example in the form of product models, from previous generations. This data represents a large knowledge base, but it has not yet been systematically used. One reason for this is that a large part of the knowledge is implicit and therefore difficult to formalize. Machine learning methods offer great potential here for extracting this knowledge from existing product models and thus making it usable for further generations.

X-ray Computed Tomography as key enabler for flexible production system

The transition from rigid production lines to flexible production systems of customized products is essential for the future competitiveness of companies. Thereby, a comparably high product quality must be guaranteed. X-ray computed tomography (CT) plays a key role in this transition, as it is the only known technique capable of certifying internal and complex structures in a single step, non-destructively and yet with high accuracy. Already significant for reference measurements, CT's use as a fully integrated on-line quality assurance will transform manufacturing. This presentation highlights the potential of this exciting technology and shows current and future areas of application.