WeChat Mini Program
Old Version Features

Microstructure Controlling, Properties, and Thermodynamic Analysis of SiC Joints Brazed with Ni-Ti Fillers

Materials (Basel, Switzerland)(2025)

Cited 0|Views0
Abstract
Silicon carbide (SiC) ceramics were brazed with Ni-Ti fillers at 1350 °C for 10 min. The experimental results show that with the increase in Ti content in the fillers, the interface layer composed of Ni2Si, Ni3Si2, graphite, and TiC becomes thinner due to the inhibition of the Ti/SiC reaction on the Ni/SiC reaction. When Ni-45Ti filler is used, TiC becomes the only phase of the interface layer in the brazing seam. The elimination of graphite improves the mechanical property of the joints. The shear strength of the SiC joints brazed by Ni-15Ti, Ni-30Ti, and Ni-45Ti fillers is 33 MPa, 92 MPa, and 125 MPa, respectively. From the point of thermodynamics, the calculated component point of the Ni/SiC reaction transition to the Ti/SiC reaction is xTi = 31 at.%. When the Ti content is higher than 31 at.%, the ΔGNi/SiC > ΔGTi/SiC, and TiC will be preferentially generated at the interface. Therefore, the Ni/SiC reaction is inhibited and the harmful graphite is eliminated.
More
Translated text
Key words
SiC ceramics,Ni-Ti brazing filler,microstructure controlling,joint properties,thermodynamic analysis
求助PDF
上传PDF
Bibtex
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
  • Pretraining has recently greatly promoted the development of natural language processing (NLP)
  • We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
  • We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
  • The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
  • Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined