Effect of particle size distribution and printing parameters on alumina ceramics prepared by Additive Manufacturing
Source abstract
Abstract
Vat photopolymerization-based additive manufacturing is a promising technology for the preparation of ceramic parts, owing to its short fabrication cycle and low manufacturing cost. However, its application is limited due to the low mechanical properties and deformation of ceramic parts. To improve the properties of ceramic parts, changing particle size and printing parameters have been found to be useful. Herein, alumina ceramic parts were prepared using three different powders with different particle size (Powder A: D50 = 1.3 mu m, D90 = 6.1 mu m; Powder B: D50 = 3.4 mu m, D90 = 10.0 mu m; Powder C: D50 = 1.3 mu m, D90 = 2.8 mu m), different layer thickness (50 mu m, 75 mu m, 100 mu m), and different curing times (1 s, 3 s, 5 s, 8 s). The ceramics prepared with Powder A and Powder C, which possessed the same D50, had almost the same flexural strength, indicating that the flexural strength is closely related to the particle size and its distribution. With the increase in layer thickness, the flexural strength was increased. When the layer thickness was 100 mu m, the flexural strength reached 18.5 MPa when samples were prepared with Powder A and Powder C. At the same time, the flexural strength firstly increased and subsequently decreased with increasing curing time. Based on the flexural strength and shrinkage of the sintered ceramics, using Powder A, layer thickness of 50 mu m, and curing time of 5 s were regarded as the best fabrication conditions. The results indicate that adjusting powder particle size distribution, layer thickness, and curing time are promising methods for the fabrication of 3D printed ceramics with optimized properties.
Source: openaire · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Additive Manufacturing and 3D Printing Technologies · Innovations in Concrete and Construction Materials · Dental materials and restorations
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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