Projects in Machine Learning and Artificial Intelligence

DIGIPD – Validating DIGItal biomarkers for better personalized treatment of Parkinson’s Disease

The European project DIGIPD, funded with around 1.6 million euros and coordinated by the Fraunhofer Institute for Algorithms and Scientific Computing SCAI, is investigating the extent to which digital techniques (sensors, speech recognition, recognition of facial expressions) can be used to make a more precise and individualized diagnosis and prognosis of Parkinson's disease. The project is funded by the European network for personalized medicine, ERA PerMed, in the "Joint Transnational Call 2020". The German share of DIGIPD is funded by the Federal Ministry of Education and Research (BMBF).
Project duration: 04/2021 until 03/2024

More Info


Computer Aided Manufacturing (CAM) systems enable computer-aided planning of manufacturing processes. CAM software is used to create the control code for Computerized Numerical Control (CNC) machine tools.The CAM2030 project aims to develop a new generation of CAM systems characterized by reduced planning effort, optimized process planning and long-term knowledge acquisition and retention. To this end, the project uses user-centric enrichment of CAM systems with novel digital optimization tools - such as evolutionary algorithms, cloud computing and artificial intelligence.
Project duration: 10/2020 - 09/2023

More Info (in German)

© Fraunhofer SCAI

MaGriDo – Mathematics for Machine Learning Methods for Gaph-Based Data with Integrated Domain Knowledge

MaGriDo's goal is to (further) develop and analyze deep neural networks (NNs) for industrial problems, which allow existing domain knowledge to be incorporated into the architecture of the networks. Such a hybrid approach can make use of the complementary strengths of "end-to-end" learning approaches and "a-priori models/rules". This approach promises more efficient solutions for many fields of application. For example, the amount of data required is reduced, or the predictions of the ML model are consistent with existing knowledge.

Project duration: 04/2020 – 03/2023

ManuBrain – universal, scalable AI-platform for industrial applications

In the »ManuBrain« project, a universal, scalable, and open platform for artificial intelligence applications in medium-sized industrial companies is being developed. The state of North Rhine-Westphalia and the European Fund for Regional Development are supporting the project over three years with a total of 1.8 million euros. Fraunhofer SCAI develops and evaluates machine learning methods for engineering applications.
Project duration: 01/2020 - 12/2022

© Fraunhofer IPT


The big vision of Industry 4.0 is the automatic adaptation of production processes to rapidly changing requirements. The new Fraunhofer Leitprojekt EVOLOPRO wants to come a step closer to this vision. The project is part of the Fraunhofer initiative "Biological Transformation". Starting in 2019, seven institutes will jointly investigate how developmental and evolutionary biological principles can be transferred to man-made production processes. Transfer and multi-task learning play an important role here - as well as the concept of so-called "digital twins". Biology is the great role model for the work in EVOLPRO and provides important impulses for the further development of procedures.
Project duration: 01/2019 - 12/2022

More Info

© Fraunhofer SCAI

ViPrIA – Virtual Product Development Using Intelligent Assistance Systems

The goal of the project ViPrIA is the development of intelligent assistance systems based on artificial intelligence and machine learning approaches to support engineers in simulation-based, virtual product development. With the help of intelligent assistance functions, calculation engineers are to be supported in the development process with complex decisions and relieved of routine tasks.
Project duration: 10/2019 – 09/2022

COMMITMENT – Comorbidity Modeling via Integrative Transfer Machine Learning in Mental Illness

The project "COMMITMENT – COMorbidity Modeling via Integrative Transfer machine-learning in MENTal illness" will establish an interdisciplinary research framework for the identification of systems-molecular hallmarks of psychotic and comorbid somatic diseases. The identification of shared and distinct biological profiles and their underlying pathophysiological processes will allow disentangling patient heterogeneity and provide the basis for objective tools for a personalized clinical management of psychotic disorders.
Project duration: 09/2019 – 08/2022



New analysis methods improve evaluation of complex engineering data

A further increase in the performance of supercomputers is expected over the next few years. So-called exascale computers will then be able to deliver more accurate simulation results. Fraunhofer SCAI is developing efficient data analysis methods for the much larger amounts of data generated in this way, which will also provide the engineer with detailed insights into the complex technical interrelationships.
Project duration: 12/2018 – 02/2022

digitalTPC – digital twins for thermoplast lightweight construction

The potential of digital twins is still largely untapped for cross-value-added chain and material-triggered process control. The digitalTPC project is intended to demonstrate this potential by means of the hybrid injection moulding technology, which is capable of large series production. digitalTPC aims at the comprehensive and holistic consideration of all sub-process steps from semi-finished product to component production. Relevant material, process and component characteristics are to be measured, recorded and virtually modelled and analyzed in a digital twin in local resolution across the entire real value chain. The challenge of the project is the material- and process-related intelligent acquisition of sensor data and their linkage with the integrated simulation chain within the framework of the digital twin by SCAI. The project is funded by Fraunhofer's internal PREPARE program.
Project duration: 02/2019 - 01/2022

More Info

COPERIMOplus – COronavirus PErsonalized RIsk MOdels

The Fraunhofer institutes cooperating in COPERIMOplus want to use rational, data-driven modeling to enable individual risk assessments in order to improve the prognosis of disease progression and to optimize personalized therapies and their evaluation based on objective, standardized criteria. Thus, the project contributes to making it possible to live with the pandemic and return to economic and social normality. The project is funded in the Fraunhofer Anti-Corona program.
Project duration: 10/2020 - 12/2021

More Info

© Fraunhofer SCAI


Using machine learning for the early detection of anomalies helps to avoid damages

The analysis of sensor data of machines, plants or buildings makes it possible to detect anomalous states early and thus to avoid further damage. For this purpose, the monitoring data is searched for anomalies. By means of machine learning, anomaly detection can already be partially automated.
Project duration: 10/2018 - 09/2021

More Info


VMAP – Virtual Material Modelling in Manufacturing

The VMAP project aims to gain a common understanding of interoperable definitions for virtual material models in CAE. Using industrial use cases from major material domains and with representative manufacturing processes, new concepts will be created for a universal material exchange interface for virtual engineering workflows.
Project duration: 09/2017 - 10/2020

More Info


VAVID allows technology firms to get a better grip on the massive amount of data they need to handle. The partners in this project are developing methods to tackle the enormous volumes of data that accumulate at engineering departments. Examples of such data include simulation results and the sensor data received from machines and installations. VAVID works by using comparative analysis and data compression to reduce data to its relevant core. This saves on the costs of data storage and creates the transparency needed by engineers to optimize both production and products. Project duration: 09/2014 - 12/2017
More Info