Material extrusion of low-thermal-expansion cordierite ceramics: Process optimization and performance evaluation by data-driven technique

Liu, Dianzi (2026) Material extrusion of low-thermal-expansion cordierite ceramics: Process optimization and performance evaluation by data-driven technique. Materials and Design, 265. pp. 1-13. ISSN 0261-3069

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Abstract

Material extrusion of cordierite ceramics faces challenges in fabricating dense, high-strength components due to non-linear parameter coupling. To address this, a two-stage framework based on Latin hypercube sampling is proposed. The first stage employs planar dimensional deviation of green bodies for rapid, non-destructive screening to filter impractical parameters prior to sintering. The second stage optimizes flexural strength of sintered bodies under dimensional constraints, enabling a decoupled enhancement of mechanical performance. Sensitivity analysis reveals that layer height (41.8%) and nozzle temperature (39.0%) govern dimensional deviation, whereas layer height is the dominant factor (60.7%) governing flexural strength, which is further validated by the evolution of microstructural defects. Under optimized conditions, the green bodies achieve a planar dimensional deviation of <0.25%, while the sintered samples reach a flexural strength of 196.8 MPa after hot isostatic pressing. To validate the proposed framework, a 148-mm lightweight mirror is successfully fabricated, exhibiting near-zero thermal expansion (−0.45 to 0.05 × 10−6/K, 0–50 °C). This study provides a useful insight into the development of a data-driven fused deposition modeling strategy for the fabrication of high-performance ceramic components.

Item Type:	Article
Additional Information:	Data availability The data that support the findings of this study are available from the corresponding author upon reasonable request. Acknowledgements The author would like to thank eceshi (www.eceshi.com) for rheological analysis.
Uncontrolled Keywords:	cordierite ceramic,material extrusion,low thermal expansion,artificial neural network,latin hypercube sampling
Faculty \ School:	Faculty of Science > School of Engineering, Mathematics and Physics
UEA Research Groups:	Faculty of Science > Research Groups > Sustainable Energy
Depositing User:	LivePure Connector
Date Deposited:	19 May 2026 08:45
Last Modified:	19 May 2026 08:45
URI:	https://ueaeprints.uea.ac.uk/id/eprint/103083
DOI:	10.1016/j.matdes.2026.115993

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