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| 001 | 1054076 | ||
| 005 | 20260210202250.0 | ||
| 024 | 7 | _ | |a 10.1016/j.egyai.2026.100687 |2 doi |
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| 037 | _ | _ | |a FZJ-2026-01710 |
| 082 | _ | _ | |a 624 |
| 100 | 1 | _ | |a Le-Dinh, Tan |0 P:(DE-HGF)0 |b 0 |
| 245 | _ | _ | |a Data-driven machine learning modelling for the manufacturing of the fuel electrode support in solid oxide cells |
| 260 | _ | _ | |a Amsterdam |c 2026 |b Elsevier ScienceDirect |
| 336 | 7 | _ | |a article |2 DRIVER |
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| 336 | 7 | _ | |a ARTICLE |2 BibTeX |
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| 336 | 7 | _ | |a Journal Article |0 0 |2 EndNote |
| 520 | _ | _ | |a 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 However, conventional SOC manufacturing processes are resource-intensive and oftenrely on industry/R&D unpublished knowledge and trial-and-error practices to achieve the targetproperties of the resulting tape.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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| 700 | 1 | _ | |a Schlenz, Hartmut |0 P:(DE-Juel1)133034 |b 1 |e Corresponding author |
| 700 | 1 | _ | |a Menzler, Norbert H. |0 P:(DE-Juel1)129636 |b 2 |u fzj |
| 700 | 1 | _ | |a Franco, Alejandro A. |0 P:(DE-HGF)0 |b 3 |
| 700 | 1 | _ | |a Guillon, Olivier |0 P:(DE-Juel1)161591 |b 4 |u fzj |
| 773 | _ | _ | |a 10.1016/j.egyai.2026.100687 |g p. 100687 - |0 PERI:(DE-600)3017958-0 |p 100687 - 100699 |t Energy and AI |v 24 |y 2026 |x 2666-5468 |
| 856 | 4 | _ | |u https://juser.fz-juelich.de/record/1054076/files/1-s2.0-S2666546826000133-main.pdf |y OpenAccess |
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