Explainable AI for applications in production engineering

被引：0

作者：

Kick M.K. ^{[1
]}

Stadter C. ^{[1
]}

Weiß T. ^{[1
]}

Backenstos M. ^{[2
]}

Zäh M.F. ^{[1
]}

机构：

[1] Technische Universität München, Institut für Werkzeugmaschinen und Betriebswissenschaften (iwb) TUM School of Engineering and Design, Boltzmannstr. 15, Garching bei München

[2] DatenBerg GmbH, Haid-und-Neu-Str. 7, Karlsruhe

来源：

WT Werkstattstechnik | 2022年 / 112卷 / 03期

关键词：

Optical tomography;

D O I：

10.37544/1436-4980-2021-3-71

中图分类号：

学科分类号：

摘要：

Optical coherence tomography allows for in-process monitoring of weld penetration depth during laser beam welding. Computed tomography scans are essential to validate the measurements. Depending on the material, and in some circumstances, a visual segmentation of the weld seam is hardly possible. Artificial neural networks, on the other hand, are able to identify the weld seam more reliably than humans. Explainability approaches make prediction transparent and allow for tracing back the causing features. © 2022, VDI Fachmedien GmBH & Co. KG. All rights reserved.

引用

页码：173 / 177

页数：4

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