Data-Driven Modeling

Functionality of a Machine Learning model
Use of hybrid models for process improvements

Data-driven modeling in production

Digitization and networking are increasingly finding their way into production. The consistent use of data opens up far-reaching potential for production optimization. Traditional approaches, for example to identify fluctuating quality parameters, are reaching their limits due to the increasing individualization of products.

A systematic and cross-process analysis of data using new technologies such as machine learning and artificial intelligence makes it possible to further improve both processes and products. In this way, process and product data can be used specifically for predictive maintenance, process optimization or predictive quality. Thus data contributes to avoiding machine downtimes, shortening throughput times and improving the quality of processes and products.

Hybrid modelling

In order to further improve data-driven modeling, the Fraunhofer IPT is researching various modeling strategies and their possible combinations (grey-box modeling). The aim is to combine the advantages of different modeling strategies by skillful combination. For example, the Fraunhofer IPT is investigating approaches for combining physical simulation (white-box modeling) and data-driven approaches (black-box modeling) with a view to their potential for application in production.

Automation of the modelling process

New approaches to machine learning such as AutoML help to automate the creation of machine learning models to a large extent. The aim is to improve the accessibility of machine learning by increasing its ease of use without special training. This is for example achieved through automated data preparation or the targeted selection of algorithms and hyper-parameters.

However, modelling is not enough to fully exploit the potential of data-driven approaches. Practice-oriented optimization strategies support the acquisition of new knowledge by combining modelling and the use of existing knowledge about processes and products in the company.

Our range of services

  • Industrie 4.0 audit
  • Data-driven modelling of production processes
  • Project work for data-driven optimization
  • Identification of promising applications in industry

Online - Seminar / 7. - 9. December 2021

Data Scientist Training

In cooperation with Fraunhofer IAIS, we offer an online seminar on "Data Quality and Data Preprocessing". Unlock the full potential of your data.

Innovations in turbo-machinery engineering

The "International Center for Turbomachinery Manufacturing" offers an integrated and interdisciplinary platform for the development of production and repair technologies in turbomachinery engineering.

Lean data management

The research project "charMant" develops concepts to enable small and medium-sized companies to carry out efficient and cost-effective process and product analyses.

Highly flexible manufacturing processes

In the research project "Openmind", systems for highly flexible manufacturing processes for individualized minimally invasive disposable medical products are being developed.

Networked optical production chains

The research project "EverPro" is dedicated to the creation of cross-technology, transferable infrastructure concepts for the networking of process chains of complex products in optics manufacturing.