收藏切换
Dispersion uniformity of titanium alloy/graphene oxide mixed powder based on deep learning image segmentation
收藏切换
Hang CHEN1, Yike LI1, Guangbao MI2, Peijie LI3, Haoyu WANG1
Journal of Materials Engineering | 2026, 54(4) : 35 - 46
Less
收藏切换
Journal of Materials Engineering | 2026, 54(4): 35-46
• FRONTIERS OF TITANIUM-BASED MATERIALS COLUMN •
Dispersion uniformity of titanium alloy/graphene oxide mixed powder based on deep learning image segmentation
Full
Hang CHEN1, Yike LI1, Guangbao MI2, Peijie LI3, Haoyu WANG1
Affiliations
  • 1.School of Mechanical Engineering,Liaoning Technical University,Fuxin 123000,Liaoning,China
  • 2.Aviation Key Laboratory of Science and Technology on Advanced Titanium Alloys,AECC Beijing Institute of Aeronautical Materials,Beijing 100095,China
  • 3.National Center of Novel Materials for International Research,Tsinghua University,Beijing 100084,China
Published: 2026-04-20 doi: 10.11868/j.issn.1001-4381.2025.000491
Outline
收藏切换

The dispersion uniformity of titanium alloy/graphene oxide (GO) mixed powder is crucial for the preparation of high-quality graphene-reinforced titanium matrix composites. The mechanism of GO adsorption onto the surface of titanium alloy particle is revealed by theoretical analysis and a quantitative evaluation method is established based on image segmentation using deep learning and statistics analysis. The results show that the SEM secondary electron images of the mixed powder dried for 12 h after solution stirring mixing exhibit high imaging contrast and strong GO adsorption state. The additional pressure generated by the pressure difference between the inside and outside of the liquid bridge is the dominant part for GO adsorption force onto the surface of titanium alloy particle,which is ten orders of magnitude higher than the force of GO. The U-Net network model demonstrates superior segmentation performance for the mixed powder and GO compared to DeepLabV3+ and PSPNet. Under the optimal training parameters,the segmentation accuracies of U-Net for the mixed powder and GO reach relatively high values of 0.9433 and 0.8774, respectively. The stirring blades shape,stirring speed, and stirring time are optimized by numerical simulation of stirring process and the established quantitative evaluation method. The paddle with three inclined blades is the preferred stirring paddle shape for preparing titanium alloy/GO mixed powder. For the mixed powder containing 0.15% (mass fraction, the same below) GO, the preferred stirring process is 400 r/min for 40 min, where the standard deviation and range of GO content are 0.82% and 2.15%, respectively. For the mixed powder containing 0.30%GO, the preferred stirring process is 300 r/min for 80 min, and the standard deviation and range of the GO content are 1.03% and 3.40%,respectively.

titanium alloy/graphene oxide  /  mixed powder  /  dispersion uniformity  /  image segmentation  /  adsorption mechanism
陈航, 李一可, 弭光宝, 李培杰, 王浩雨. 基于深度学习图像分割的钛合金/氧化石墨烯混合粉体分散均匀性研究. 材料工程, 2026 , 54 (4) : 35 -46 . DOI: 10.11868/j.issn.1001-4381.2025.000491
Hang CHEN, Yike LI, Guangbao MI, Peijie LI, Haoyu WANG. Dispersion uniformity of titanium alloy/graphene oxide mixed powder based on deep learning image segmentation[J]. Journal of Materials Engineering, 2026 , 54 (4) : 35 -46 . DOI: 10.11868/j.issn.1001-4381.2025.000491
Year 2026 volume 54 Issue 4
59
29
Cite this Article
BibTeX
Article Info
doi: 10.11868/j.issn.1001-4381.2025.000491
  • Receive Date:2025-09-01
  • Online Date:2026-10-27
  • Published:2026-04-20
Article Data
Affiliations
History
  • Received:2025-09-01
  • Accepted:2025-10-14
Funding
Affiliations
    1.School of Mechanical Engineering,Liaoning Technical University,Fuxin 123000,Liaoning,China
    2.Aviation Key Laboratory of Science and Technology on Advanced Titanium Alloys,AECC Beijing Institute of Aeronautical Materials,Beijing 100095,China
    3.National Center of Novel Materials for International Research,Tsinghua University,Beijing 100084,China
References
Share
https://castjournals.cast.org.cn/joweb/clgc/EN/10.11868/j.issn.1001-4381.2025.000491
Share to
QR

Scan QR to access full text

Cite this article
BibTeX
Citations
表12种不同金属材料的力学参数
科
Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)
属
Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
关闭全屏
  • BibTeX
  • EndNote
  • RefWorks
  • TxT