%0 Journal Article
%A Le-Dinh, Tan
%A Schlenz, Hartmut
%A Menzler, Norbert H.
%A Franco, Alejandro A.
%A Guillon, Olivier
%T Data-driven machine learning modelling for the manufacturing of the fuel electrode support in solid oxide cells
%J Energy and AI
%V 24
%@ 2666-5468
%C Amsterdam
%I Elsevier ScienceDirect
%M FZJ-2026-01710
%P 100687 - 100699
%D 2026
%X tape 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 However, conventional SOC manufacturing processes are resource-intensive and oftenrely on industry/R&D unpublished knowledge and trial-and-error practices to achieve the targetproperties of the resulting tape.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.
%F PUB:(DE-HGF)16
%9 Journal Article
%R 10.1016/j.egyai.2026.100687
%U https://juser.fz-juelich.de/record/1054076