当前位置: X-MOL 学术Metall. Mater. Trans. A › 论文详情
A Data-Driven Scheme for Quantitative Analysis of Texture
Metallurgical and Materials Transactions A ( IF 1.985 ) Pub Date : 2019-12-01 , DOI: 10.1007/s11661-019-05529-x
Yafei Wang, Chenfan Yu, Leilei Xing, Kailun Li, Jinhan Chen, Wei Liu, Jing Ma, Zhijian Shen

Texture is the orientation distribution of crystallites in polycrystalline materials. Given the discrete orientations, Schaeben suggested to adopt statistics for quantitative analysis of texture from discrete orientations, and he also conceived a clustering algorithm to facilitate the applications of statistical methods (H. Schaeben, J Appl Crystal 26:112–121, 1993). This data-driven scheme becomes more urgent and more necessary for the oncoming fourth paradigm: data-intensive scientific discovery, which follows after experimental science, theoretical science, and computational science paradigm. This research adopts a density-based clustering algorithm, DBSCAN, to process the orientation data from an austenitic stainless steel 316 L sample fabricated by selective laser melting. It is validated that the algorithm can robustly identify the orientation cluster (or texture component or preferred orientation). The statistical methods can successfully quantify the features of the identified orientation cluster with quantified uncertainty (statistical significance), which is often lacked in the general method of orientation distribution function. It is believed that this data-driven scheme can be applied to the many aspects of texture analysis.
更新日期:2020-01-06

 

全部期刊列表>>
全球疫情及响应:BMC Medicine专题征稿
欢迎探索2019年最具下载量的化学论文
新版X-MOL期刊搜索和高级搜索功能介绍
化学材料学全球高引用
ACS材料视界
南方科技大学
x-mol收录
南方科技大学
自然科研论文编辑服务
上海交通大学彭文杰
中国科学院长春应化所于聪-4-8
武汉工程大学
课题组网站
X-MOL
深圳大学二维材料实验室张晗
中山大学化学工程与技术学院
试剂库存
天合科研
down
wechat
bug