AI system cuts manual wear inspection from weeks to hours
Researchers combined neural networks with expert knowledge to automate the analysis of tool wear in microscopy images, slashing the need for manual inspection. The approach works with small datasets and explains its conclusions, offering manufacturers a faster, cheaper way to extend equipment life and accelerate material development.
Originaltitel: Towards neuro-symbolic classification of abrasive wear in scanning electron microscopy
<p>The analysis of abrasive wear is central to sustainable material design and tool development, yet current practice relies on manual inspection of scanning electron microscopy (SEM) images, limiting scalability and reproducibility. We propose early work towards a neuro-symbolic approach that integrates convolutional neural networks for SEM image segmentation with an expert-elicited taxonomy of wear features encoded in Answer Set Programming. A curated dataset of 400 laboratory and field SEM images with expert-labeled annotations supports interpretable detection of wear mechanisms. This approach aims to reduce the dependency on large datasets, increase interpretability, in automated abrasive wear analysis. The contribution opens the way for scalable and transparent decision processes in tribology, with implications for efficient materials development and extended service life of industrial tools.</p>