Feature Engineering and Machine Learning Predictive Quality Models for Friction Stir Welding Defect Prediction in Aerospace Applications

被引:0
作者
Camps, Marta [1 ]
Etxegarai, Maddi [1 ]
Bonada, Francesc [1 ]
Lacheny, William [2 ]
Pauleau, Sylvain [2 ]
Domingo, Xavier [1 ]
机构
[1] Eurecat, Ctr Tecnol Catalunya, Unit Appl Artificial Intelligence, Ave Carrer Bilbao 72, Barcelona 08005, Spain
[2] Ariane Grp, 51-61 Route Verneuil,Batiment 71 Bur 142, F-78131 Les Mureaux, France
来源
ARTIFICIAL INTELLIGENCE RESEARCH AND DEVELOPMENT | 2022年 / 356卷
基金
欧盟地平线“2020”;
关键词
Machine Learning; Predictive Quality; Industry; 4.0; Aerospace Industry;
D O I
10.3233/FAIA220330
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Data-Driven Predictive Quality solutions are of utmost importance for Industry 4.0 in general and for high added value and complex manufacturing systems in particular. A unique Friction Stir Welding process is performed for the manufacturing of the new Ariane 6 aerospace launchers. This work presents a novel feature engineering approach that correlates Friction Stir Welding process data and quality inspection data to build a Machine Learning-based predictive quality solution. This solution predicts the presence of welding defects, empowering end-user's quality assurance and reducing quality inspection time and associated costs.
引用
收藏
页码:151 / 154
页数:4
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