Leibniz-Institut für Werkstofforientierte Technologien - IWT

Supporting AI Readiness Through Digital Workflows in Materials Science

Materials science produces heterogeneous data across experiments, simulations, and industrial processes that often remain bound to local formats, manual procedures, and project‐specific software. Digital workflows address this fragmentation through explicit, repeatable, and machi...

Selection of semiconducting metal oxides for light-activated gas sensors at low temperature

Abstract Light activation is a promising approach for lowering the power consumption and increasing the selectivity of chemoresistive gas sensors based on semiconducting metal oxides. In this study, different aspects of commonly used materials are investigated, compared, and eval...

A robot-assisted optical approach for the in-situ measurement of cutting tool wear development

Abstract In-situ measurement of cutting tool wear is essential for understanding wear mechanisms and for improving machining reliability and process efficiency. However, most established measurement methods require periodic interruption of machining operations and tool removal, w...

Contribution of the Strain Rate to the Generation of Residual Stresses During Deep Rolling and Machine Hammer Peening

Deep rolling and machine hammer peening are widely applied mechanical surface treatments for improving the fatigue performance of high-strength materials by introducing compressive residual stresses and modifying the near-surface material state. Despite their industrial relevance...

Gaussian process regression for optimizing L-DED processing parameters in AlSi10Mg additive manufacturing

Abstract This work presents an experimental workflow coupled with statistical tools to optimize L-DED processing parameters to build defect-free AlSi10Mg multilayer parts. Experimental steps included the quantitative evaluation of single beads, single layers, and multilayers to d...