This study aims to systematically investigate the effects of alumina powder properties (including solid loading and particle size distribution), photosensitive resin formulation and additives on the rheological behavior of ceramic slurries for digital light processing (DLP) 3D printing, as well as their subsequent influences on the performance of sintered alumina ceramics.
A comprehensive review was performed to clarify how key parameters govern slurry rheological behavior, along with their inherent links to printing quality (interlayer bonding, forming accuracy) and the final ceramics’ mechanical properties and microstructure. Moreover, recent core technical challenges in advancing alumina slurries for DLP 3D printing were outlined, and corresponding optimization strategies were put forward.
These parameters directly determine slurry rheology, which governs printing precision and interlayer bonding and thus ultimately controls the mechanical properties and structural reliability of sintered ceramics. Notably, machine learning is identified as a key tool for intelligent process optimization in this field.
This work systematically clarifies the critical correlations between slurry formulation, rheology, printing quality and final product performance. It provides a theoretical and technical foundation for manufacturing high-performance ceramic components via DLP, underscores the technology’s significant potential in advanced fields like aerospace and electronics and highlights machine learning’s role in advancing DLP technology toward intelligent, low-cost production.
