Preparation of large-size alumina ceramic parts by DLP 3D printing using high-solid-loading paste and optimizing the debinding process
CERAMICS INTERNATIONAL(2023)
Abstract
Digital light processing (DLP) is a promising additive manufacturing method for creating complex structural alumina ceramic with high precision, high efficiency, and low cost. However, printing large-size parts with high feature resolution by DLP, obtaining high-performance pastes, and optimizing the debinding process are challenges that still need to be overcome. In this study, an advanced top-down Matrix-DLP technology that seamlessly connects two high-definition-resolution DLP projectors, with high resolution and a large projection area, was used to achieve fine-structured large-size alumina green bodies. The homogeneous and stable non-self-levelling alumina pastes suitable for the DLP technology were developed, with a high solid loading up to 55 vol%, viscosity of 35.0 Pa s at 30 s−1, and good rheological properties by innovatively using 2.5 wt% P5318 as surface modifiers of alumina powders and with only a 0.244% volume of bubble residue by using 0.5 wt% BYK-066N defoamer in combination with a vacuum planetary stirring defoaming process. The debinding processes were designed based on TG-DSC analysis and then optimized, and it was proved that the segmented-atmosphere debinding process was the best approach and can obtain defect- and carbon residue-free bodies with uniform grain distribution, and can also achieve non-destructive debinding for the thick-walled parts with thickness greater than 10 mm. The effects of the debinding residual carbon and the solid loading on the microstructure and properties after sintering were also investigated. Consequently, the high-precision large-size alumina ceramic with good interlayer bonding, dense microstructure, and high microhardness of 17.92 GPa was obtained.
MoreTranslated text
Key words
Top-down DLP,Large-size alumina ceramic,Optimization of debinding process,High solid loading,Defoaming of paste,Microstructure
AI Read Science
Must-Reading Tree
Example
![](https://originalfileserver.aminer.cn/sys/aminer/pubs/mrt_preview.jpeg)
Generate MRT to find the research sequence of this paper
Chat Paper
Summary is being generated by the instructions you defined