Ding Zhao, Jiangkun Fan, Zesen Chen, Wenyuan Zhang, Zhixin Zhang, Bin Tang, Jian Wang, Hongchao Kou, Jinshan Li
{"title":"Cooling rate effects on microstructure and diffusion behaviour in Ti65 alloy: Insights from a modified diffusion model","authors":"Ding Zhao, Jiangkun Fan, Zesen Chen, Wenyuan Zhang, Zhixin Zhang, Bin Tang, Jian Wang, Hongchao Kou, Jinshan Li","doi":"10.1016/j.jmrt.2024.09.012","DOIUrl":null,"url":null,"abstract":"In the continuous cooling process, the growth of the equiaxed α-phase grains in near-α high-temperature titanium alloys is controlled by the diffusion of alloying elements. Establishing a specific connection between the cooling rate and the diffusion behaviour of alloying elements aids in the precise prediction of the evolution of equiaxed α-phase grain size. This study meticulously controlled the cooling rate during the two-phase region annealing treatment of the Ti65 alloy. Using EPMA technology, the diffusion behaviour of solute elements during cooling was accurately characterized. The study found that slowing the cooling rate promotes the coarsening of the lamellar secondary α-phase grains and the growth of the primary equiaxed α-phase grains. At higher annealing temperatures, the growth of equiaxed α-phase grains can occur at faster cooling rates, while coarse lamellar secondary α-phase grains can be retained at slower cooling rates. The diffusion behaviour of solute elements Al, Ta, Mo, and W between the α-phase and transformed β-phase matrix is significantly influenced by the cooling rate, thus they are considered as the controlling elements for the growth of the equiaxed α-phase grains. Based on the diffusion behaviours of these controlling elements, their single-element diffusion models were categorized and integrated for predicting the grain size of the equiaxed α-phase. The predictions from the revised diffusion model show an excellent agreement with the actual results, with an error margin of about 5%.","PeriodicalId":501120,"journal":{"name":"Journal of Materials Research and Technology","volume":"110 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Materials Research and Technology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1016/j.jmrt.2024.09.012","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
In the continuous cooling process, the growth of the equiaxed α-phase grains in near-α high-temperature titanium alloys is controlled by the diffusion of alloying elements. Establishing a specific connection between the cooling rate and the diffusion behaviour of alloying elements aids in the precise prediction of the evolution of equiaxed α-phase grain size. This study meticulously controlled the cooling rate during the two-phase region annealing treatment of the Ti65 alloy. Using EPMA technology, the diffusion behaviour of solute elements during cooling was accurately characterized. The study found that slowing the cooling rate promotes the coarsening of the lamellar secondary α-phase grains and the growth of the primary equiaxed α-phase grains. At higher annealing temperatures, the growth of equiaxed α-phase grains can occur at faster cooling rates, while coarse lamellar secondary α-phase grains can be retained at slower cooling rates. The diffusion behaviour of solute elements Al, Ta, Mo, and W between the α-phase and transformed β-phase matrix is significantly influenced by the cooling rate, thus they are considered as the controlling elements for the growth of the equiaxed α-phase grains. Based on the diffusion behaviours of these controlling elements, their single-element diffusion models were categorized and integrated for predicting the grain size of the equiaxed α-phase. The predictions from the revised diffusion model show an excellent agreement with the actual results, with an error margin of about 5%.