Reliable characterization of deformation behaviour and mechanical properties requires objective and reproducible evaluation of experimental data. Time-dependent creep tests, as well as tensile and fatigue experiments, frequently involve heterogeneous machine outputs and manual parameter selection, limiting comparability across laboratories.

The underlying methodology for automated segmentation, statistically robust point selection, and regression-based parameter extraction was originally developed and scientifically established within SFB/TRR 103 (Projects C6/T2), where it was validated on creep and shear experiments [1-3]. In recent years, the method has been re-implemented and substantially extended in Python to broaden its applicability across several different mechanical test types and material systems [4]. In close scientific exchange with BAM, the approach has been applied to new datasets and further generalized to enable consistent evaluation of creep, tensile, and fatigue data from different laboratory environments.

The work contributes to NFDI-MatWerk (Infrastructure Use Case IUC02) by advancing reproducible and FAIR-oriented concepts for mechanical data evaluation. Ongoing work includes systematic application to Al-alloy datasets and further validation across different deformation experiments.

References
[1] E. Turchenko; Automated Data Processing and its Application to Creep Experiments. Master’s Thesis, Ruhr-Universität Bochum, 2016.
[2] M. Almodallaleh; Applications of Automated Data Processing to Double-Shear Tests of Nickel-Based Superalloys. Master’s Thesis, Ruhr-Universität Bochum, 2018.
[3] S. Zomorodpoosh et al.; Modelling and Simulation in Materials Science and Engineering, 2020, 28, 045006.
[4] I. Roslyakova; From Superalloys to Semiconductors: Class-Independent Automation of Heterogeneous Materials Data for Sustainable Design. Invited talk, SuCiMat2025, Saarbrücken, Germany, 2025.
[5] I. Roslyakova; Making Aluminium Data AI-Ready: Automated and FAIR Foundations for Data-Driven Design of Sustainable Alloys. Invited talk, FA Aluminium (online), 2026.

Dr. Irina Roslyakova