journal article·2023-08

Enhanced mechanical properties of 3D printed alumina ceramics by using sintering aids

He Li, Hamada Elsayed, Paolo Colombo

Ceramics International· Volume 49· Issue 15
Publisher page

Abstract

Stereolithography based 3D printing provides an efficient pathway to fabricate alumina ceramics, and the exploration on the mechanical properties of 3D printed alumina ceramics is crucial to the development of 3D printing ceramic technology. However, alumina ceramics are difficult to sinter due to their high melting point. In this work, alumina ceramics were prepared via stereolithography based 3D printing technology, and the improvement in the mechanical properties was investigated based on the content, the type and the particle size of sintering aids (TiO2, CaCO3, and MgO). The flexural strength of the sintered ceramics increased greatly (from 139.2 MPa to 216.7 MPa) with the increase in TiO2 content (from 0.5 wt% to 1.5 wt%), while significant anisotropy in mechanical properties (216.7 MPa in X-Z plane and 121.0 MPa in X–Y plane) was observed for the ceramics with the addition of 1.5 wt TiO2. The shrinkage and flexural strength of the ceramics decreased with the increase in CaCO3 content due to the formation of elongated grains, which led to the formation of large-sized residual pores in the ceramics. The addition of MgO help decrease the anisotropic differences in shrinkage and flexural strength of the sintered ceramics due to the formation of regularly shaped grains. This work provides guidance on the adjustment in flexural strength, shrinkage, and anisotropic behavior of 3D printed alumina ceramics, and provides new methods for the fabrication of 3D printed alumina ceramics with superior mechanical properties.
Source: openaire · Quality: candidate unverified · Source record ↗

Research context

OpenAlex topics: Additive Manufacturing and 3D Printing Technologies · Innovations in Concrete and Construction Materials · Injection Molding Process and Properties

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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