Participation in HORIZON-RAISE-2027-01-01: Automated Scientific Discovery (RAISE pilot) (RIA) as consortium partner

The specialist group for smart materials at the Hochschule Mittweida University of Applied Sciences offers its expertise as consortium partner.

The team’s expertise lies in machine learning for materials science, with focus on microstructure characterisation, quantitative image analysis, computer vision, semantic segmentation, defect detection and structure-property relationships. The team has experience in extracting quantitative information from complex experimental data and in developing explainable and physics-informed AI methods for materials research. This expertise can support AI-driven closed-loop experimentation by enabling automated interpretation of imaging data, real-time analysis of experimental results, and uncertainty-aware modelling for adaptive decision-making in autonomous laboratory workflows.

Contact          

Prof. Dr.-Ing. Kristin Hockauf

Chair of Smart Materials

Mittweida University of Applied Sciences

Faculty Engineering Scienecs

Technikumplatz 17

09648 Mittweida

mail: hockauf@hs-mittweida.de

phone: +49 3727 58 1008

Research areas

• machine learning for materials science

• structure-property relationships

• microstructure characterisation

• microstructure segmentation

• semantic segmentation

• computer vision

• image analysis in materials science

• scientific machine learning

• computational materials science

• defect detection

• exture / grain analysis

• physics-informed machine learning

• explainable AI (XAI) in materials science

Laboratory for materials analysis

• mechanical materials testing (tensile, compression and bending tests)

• hardness testing

• notched bar impact test

• metallography

• fatigue testing

• scanning electron microscopy

• emission spectrometry

• computed tomography

• X-ray diffraction

• non-destructive materials testing

Laboratory for computation

covers multiple GPU clusters equipped with NVIDIA A100 80GB GPUs and NVIDIA V100 32GB GPUs

Chair's website

https://www.inw.hs-mittweida.de/webs/wfq/smart-materials/

https://www.inw.hs-mittweida.de/webs/wfq/werkstofftechnik/