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   	<dc:title>A physics-guided hybrid framework for online pre-expulsion prediction in resistance spot welding</dc:title>
   	<dc:creator>Yang, Keke</dc:creator>
   	<dc:creator>Li, Chong</dc:creator>
   	<dc:creator>Beck, Robert</dc:creator>
   	<dc:creator>Hein, David</dc:creator>
   	<dc:creator>Meschut, Gerson</dc:creator>
   	<dc:subject>Resistance spot welding</dc:subject>
   	<dc:subject>Expulsion prediction</dc:subject>
   	<dc:subject>Physics-guided machine learning</dc:subject>
   	<dc:subject>Hybrid ensemble modelling</dc:subject>
   	<dc:subject>Process monitoring</dc:subject>
   	<dc:subject>ddc:600</dc:subject>
   	<dc:description>Expulsion in resistance spot welding (RSW) causes weld quality fluctuations and increases quality-control effort in high-volume manufacturing. Existing data-driven studies have mainly addressed post-occurrence expulsion detection, process-end classification, or the identification of influencing factors, whereas online monitoring requires short-term risk estimation before the event occurs. In this study, expulsion prediction is formulated as a sliding-window-based pre-expulsion risk estimation task for the currently welded spot. A physics-guided hybrid GRU-XGBoost ensemble is developed to combine temporal learning from dynamic resistance and electrode-force signals with process-physics-related scalar features describing heat input, resistance state, and force response. The framework was evaluated on 2730 valid welds, including 588 expulsion and 2142 non-expulsion welds, using weld-grouped five-fold cross-validation with fold-level working-point selection. The ensemble achieved an area under the ROC curve of 0.945 ± 0.004 and a weld-level recall of 90.6 ± 3.7% at an average false alarm rate of 9.8 ± 0.2%, outperforming both individual branches. For the 533 correctly warned expulsion welds, the median early-warning lead time was 56 ms. These results indicate that online, physically interpretable pre-expulsion risk prediction is feasible under low-false-alarm constraints within the investigated RSW configuration and provide a basis for future adaptive monitoring and control studies.</dc:description>
   	<dc:publisher>Elsevier BV</dc:publisher>
   	<dc:date>2026</dc:date>
   	<dc:type>info:eu-repo/semantics/article</dc:type>
   	<dc:type>doc-type:article</dc:type>
   	<dc:type>text</dc:type>
   	<dc:type>http://purl.org/coar/resource_type/c_6501</dc:type>
   	<dc:identifier>https://ris.uni-paderborn.de/record/66541</dc:identifier>
   	<dc:source>Yang K, Li C, Beck R, Hein D, Meschut G. A physics-guided hybrid framework for online pre-expulsion prediction in resistance spot welding. &lt;i&gt;Journal of Manufacturing Processes&lt;/i&gt;. 2026;174:135-153. doi:&lt;a href=&quot;https://doi.org/10.1016/j.jmapro.2026.07.042&quot;&gt;10.1016/j.jmapro.2026.07.042&lt;/a&gt;</dc:source>
   	<dc:language>eng</dc:language>
   	<dc:relation>info:eu-repo/semantics/altIdentifier/doi/10.1016/j.jmapro.2026.07.042</dc:relation>
   	<dc:relation>info:eu-repo/semantics/altIdentifier/issn/1526-6125</dc:relation>
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