Bibliographic Details
| Title: |
Effect of processing conditions on the sliding-wear resistance of ZrC triboceramics fabricated by spark-plasma sintering. |
| Authors: |
Bertagnoli, Davide1, Borrero-López, Oscar1, Rodríguez-Rojas, Fernando1, Guiberteau, Fernando1, Ortiz, Angel L.1 alortiz@unex.es |
| Source: |
Ceramics International. Dec2015 Part B, Vol. 41 Issue 10, p15278-15282. 5p. |
| Subjects: |
Sliding wear, Wear resistance, Zirconium carbide, Ceramics, Plasma gases, Sintering |
| Abstract: |
The conditions under which ZrC triboceramics are fabricated (high-energy ball-milling and spark-plasma sintering (SPS)) lead to major microstructural differences. The effects of these conditions on the resulting ceramic׳s lubricated sliding-wear resistance were investigated. One condition studied entailed SPS of the as-purchased micrometre powder, giving super-coarse-grained ceramics with little residual porosity. Another was SPS at the same temperature for a shorter time, with the powder having been subjected to short high-energy ball-milling, yielding near-dense, coarse-grained ceramics. The last was with the powder subjected to prolonged high-energy ball-milling, followed by SPS in smother conditions in terms of temperature and time, resulting in fully-dense, fine-grained ceramics. It was found that, while all three were highly wear resistant, the ceramic obtained by combining prolonged ball-milling with smooth SPS was the most so, with less damage and a lower steady-state wear rate coefficient, ascribable to the greater hardness and toughness deriving from the microstructural refinement and densification. The results therefore demonstrate the importance of the processing conditions in obtaining electrically-conductive ZrC triboceramics with superior wear resistance. [ABSTRACT FROM AUTHOR] |
|
Copyright of Ceramics International is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) |
| Database: |
Engineering Source |