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001054076 1001_ $$0P:(DE-HGF)0$$aLe-Dinh, Tan$$b0
001054076 245__ $$aData-driven machine learning modelling for the manufacturing of the fuel electrode support in solid oxide cells
001054076 260__ $$aAmsterdam$$bElsevier ScienceDirect$$c2026
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001054076 520__ $$atape casting typically involves a multi-stage process, demanding precise control over tape thicknessand density. However, conventional SOC manufacturing processes are resource-intensive and oftenrely on industry/R&D unpublished knowledge and trial-and-error practices to achieve the targetproperties of the resulting tape. Hence, machine learning (ML) was employed for predicting thethickness and density across three distinct stages of the fabrication process: tape casting, sintering,and NiO-reduction process. Our developed ML models (e.g., Extra Trees and Ridge Regressions)demonstrate exceptional accuracy (R2 > 0.9) for each specific prediction task. Concurrently,experimental data analysis was conducted to elucidate the impact of the manufacturing parameterson the tape properties. Our data-driven ML approach offers a pathway towards achieving precise tapeproperty control and advancing more efficient SOC support manufacturing.
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001054076 7001_ $$0P:(DE-Juel1)133034$$aSchlenz, Hartmut$$b1$$eCorresponding author
001054076 7001_ $$0P:(DE-Juel1)129636$$aMenzler, Norbert H.$$b2$$ufzj
001054076 7001_ $$0P:(DE-HGF)0$$aFranco, Alejandro A.$$b3
001054076 7001_ $$0P:(DE-Juel1)161591$$aGuillon, Olivier$$b4$$ufzj
001054076 773__ $$0PERI:(DE-600)3017958-0$$a10.1016/j.egyai.2026.100687$$gp. 100687 -$$p100687 - 100699$$tEnergy and AI$$v24$$x2666-5468$$y2026
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