Journal Article

FZJ-2026-01710

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Data-driven machine learning modelling for the manufacturing of the fuel electrode support in solid oxide cells

Le-Dinh, T. ; Schlenz, H. (Corresponding author)FZJ* ; Menzler, N. H.FZJ* ; Franco, A. A. ; Guillon, O.FZJ*

2026
Elsevier ScienceDirect Amsterdam

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Please use a persistent id in citations: doi:10.1016/j.egyai.2026.100687  doi:10.34734/FZJ-2026-01710

Abstract: 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 However, conventional SOC manufacturing processes are resource-intensive and oftenrely on industry/R&D unpublished knowledge and trial-and-error practices to achieve the targetproperties of the resulting tape.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.

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Contributing Institute(s):

1. Werkstoffsynthese und Herstellungsverfahren (IMD-2)

Research Program(s):

1. 1222 - Components and Cells (POF4-122) (POF4-122)

Appears in the scientific report 2026

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Database coverage:
; ; ; Article Processing Charges ; Clarivate Analytics Master Journal List ; DOAJ Seal ; Emerging Sources Citation Index ; Fees ; SCOPUS ; Web of Science Core Collection

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