[{"citation":{"ieee":"K. Yang, C. Li, R. Beck, D. Hein, and G. Meschut, “A physics-guided hybrid framework for online pre-expulsion prediction in resistance spot welding,” <i>Journal of Manufacturing Processes</i>, vol. 174, pp. 135–153, 2026, doi: <a href=\"https://doi.org/10.1016/j.jmapro.2026.07.042\">10.1016/j.jmapro.2026.07.042</a>.","apa":"Yang, K., Li, C., Beck, R., Hein, D., &#38; Meschut, G. (2026). A physics-guided hybrid framework for online pre-expulsion prediction in resistance spot welding. <i>Journal of Manufacturing Processes</i>, <i>174</i>, 135–153. <a href=\"https://doi.org/10.1016/j.jmapro.2026.07.042\">https://doi.org/10.1016/j.jmapro.2026.07.042</a>","short":"K. Yang, C. Li, R. Beck, D. Hein, G. Meschut, Journal of Manufacturing Processes 174 (2026) 135–153.","chicago":"Yang, Keke, Chong Li, Robert Beck, David Hein, and Gerson Meschut. “A Physics-Guided Hybrid Framework for Online Pre-Expulsion Prediction in Resistance Spot Welding.” <i>Journal of Manufacturing Processes</i> 174 (2026): 135–53. <a href=\"https://doi.org/10.1016/j.jmapro.2026.07.042\">https://doi.org/10.1016/j.jmapro.2026.07.042</a>.","mla":"Yang, Keke, et al. “A Physics-Guided Hybrid Framework for Online Pre-Expulsion Prediction in Resistance Spot Welding.” <i>Journal of Manufacturing Processes</i>, vol. 174, Elsevier BV, 2026, pp. 135–53, doi:<a href=\"https://doi.org/10.1016/j.jmapro.2026.07.042\">10.1016/j.jmapro.2026.07.042</a>.","bibtex":"@article{Yang_Li_Beck_Hein_Meschut_2026, title={A physics-guided hybrid framework for online pre-expulsion prediction in resistance spot welding}, volume={174}, DOI={<a href=\"https://doi.org/10.1016/j.jmapro.2026.07.042\">10.1016/j.jmapro.2026.07.042</a>}, journal={Journal of Manufacturing Processes}, publisher={Elsevier BV}, author={Yang, Keke and Li, Chong and Beck, Robert and Hein, David and Meschut, Gerson}, year={2026}, pages={135–153} }","ama":"Yang K, Li C, Beck R, Hein D, Meschut G. A physics-guided hybrid framework for online pre-expulsion prediction in resistance spot welding. <i>Journal of Manufacturing Processes</i>. 2026;174:135-153. doi:<a href=\"https://doi.org/10.1016/j.jmapro.2026.07.042\">10.1016/j.jmapro.2026.07.042</a>"},"file_date_updated":"2026-07-18T12:04:02Z","quality_controlled":"1","oa":"1","status":"public","has_accepted_license":"1","publisher":"Elsevier BV","_id":"66541","page":"135-153","volume":174,"ddc":["600"],"user_id":"65085","publication":"Journal of Manufacturing Processes","abstract":[{"text":"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.","lang":"eng"}],"date_created":"2026-07-18T12:03:15Z","file":[{"creator":"kekeyang","date_created":"2026-07-18T12:04:02Z","file_name":"1-s2.0-S1526612526007012-main.pdf","file_size":10864983,"access_level":"closed","relation":"main_file","date_updated":"2026-07-18T12:04:02Z","file_id":"66542","content_type":"application/pdf","success":1}],"department":[{"_id":"157"}],"keyword":["Resistance spot welding","Expulsion prediction","Physics-guided machine learning","Hybrid ensemble modelling","Process monitoring"],"type":"journal_article","publication_identifier":{"issn":["1526-6125"]},"author":[{"full_name":"Yang, Keke","last_name":"Yang","orcid":"0000-0001-9201-9304","first_name":"Keke","id":"65085"},{"last_name":"Li","first_name":"Chong","full_name":"Li, Chong"},{"first_name":"Robert","last_name":"Beck","orcid":"0000-0001-9056-4528","full_name":"Beck, Robert","id":"38279"},{"last_name":"Hein","first_name":"David","full_name":"Hein, David","id":"7728"},{"id":"32056","full_name":"Meschut, Gerson","orcid":"0000-0002-2763-1246","last_name":"Meschut","first_name":"Gerson"}],"title":"A physics-guided hybrid framework for online pre-expulsion prediction in resistance spot welding","year":"2026","intvolume":"       174","article_type":"original","date_updated":"2026-07-18T12:07:50Z","publication_status":"published","language":[{"iso":"eng"}],"main_file_link":[{"open_access":"1"}],"doi":"10.1016/j.jmapro.2026.07.042"}]