An artificial intelligence classifier for electron beam powder bed fusion as-built surface topographies

被引：4

作者：

Maculotti, Giacomo ^{[1
]}

Ghibaudo, Cristian ^{[2
]}

Genta, Gianfranco ^{[1
]}

Ugues, Daniele ^{[2
]}

Galetto, Maurizio ^{[1
]}

机构：

[1] Politecn Torino, Dept Management & Prod Engn, Cso Duca Abruzzi 24, I-10129 Turin, Italy

[2] Politecn Torino, Dept Appl Sci & Technol, Cso Duca Abruzzi 24, I-10129 Turin, Italy

来源：

CIRP JOURNAL OF MANUFACTURING SCIENCE AND TECHNOLOGY | 2023年 / 43卷

关键词：

Surface topography; Machine learning; Feature characterisation; Classification; Electron beam melting; Quality control; MECHANICAL-PROPERTIES; PARAMETERS; METAL; EBM; MICROSTRUCTURE; COMPONENTS; ROUGHNESS; INSERTS; SYSTEM;

D O I：

10.1016/j.cirpj.2023.03.006

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

Additive Manufacturing, a pillar of Industry 4.0, to enable automatic real-time process control, relies on in -situ measurements, some of which -currently under development -exploit surface topography. Topographic characterisation requires a large set of parameters, loosely linked to visual appearance upon which related in-situ measurements are mostly based. A supervised machine learning classifier of as-built surfaces based on topographical characterisation is proposed and applied to tool steel test pieces fabricated by electron beam powder bed fusion. The methodology is developed to provide process engineers with the visual appearance of the topographical parameters set, and enable multi-scale, information-rich quality control.(c) 2023 CIRP.

引用

页码：129 / 142

页数：14

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