Laser processing quality monitoring by combining acoustic emission and machine learning: a high-speed X-ray imaging approach

被引:50
|
作者
Wasmer, K. [1 ]
Le-Quang, T. [1 ]
Meylan, B. [1 ]
Vakili-Farahani, F. [1 ]
Olbinado, M. P. [2 ]
Rack, A. [2 ]
Shevchik, S. A. [1 ]
机构
[1] Swiss Fed Labs Mat Sci & Technol Empa, Lab Adv Mat Proc LAMP, Dubendorf, Switzerland
[2] ESRF European Synchrotron, CS40220, F-38043 Grenoble, France
关键词
Laser welding; in situ and real-time; monitoring; High speed X-ray; acoustic; machine learning;
D O I
10.1016/j.procir.2018.08.054
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
In situ and real-time monitoring of laser processes are very challenging due to complex dynamics of the laser-matter interactions. Acoustic emission (AE) technique is often used as non-destructive monitoring of many kinds of processes. However, acoustic emission is not industrialized for laser processing for two reasons. First, the signals are too sensitive to the environmental noises. Second, a correlation of the acoustic emission signal with the real events is very difficult to realize despite being of utmost importance. To overcome these difficulties, we combined fast hard X-ray imaging with acoustic sensors and state-of-the-art machine learning. (C) 2018 The Authors. Published by Elsevier Ltd.
引用
收藏
页码:654 / 658
页数:5
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