TY  - JOUR
AU  - Le-Dinh, Tan
AU  - Schlenz, Hartmut
AU  - Menzler, Norbert H.
AU  - Franco, Alejandro A.
AU  - Guillon, Olivier
TI  - Data-driven machine learning modelling for the manufacturing of the fuel electrode support in solid oxide cells
JO  - Energy and AI
VL  - 24
SN  - 2666-5468
CY  - Amsterdam
PB  - Elsevier ScienceDirect
M1  - FZJ-2026-01710
SP  - 100687 - 100699
PY  - 2026
AB  - 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 However, conventional SOC manufacturing processes are resource-intensive and oftenrely on industry/R&D unpublished knowledge and trial-and-error practices to achieve the targetproperties of the resulting tape.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.
LB  - PUB:(DE-HGF)16
DO  - DOI:10.1016/j.egyai.2026.100687
UR  - https://juser.fz-juelich.de/record/1054076
ER  -