Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Monitoring of Joint Gap Formation in Laser Beam Butt Welding by Neural Network-Based Acoustic Emission Analysis

Saichand Gourishetti
* ORCID logo ,
Leander Schmidt
ORCID logo ,
Florian Römer
ORCID logo ,
Klaus Schricker
ORCID logo ,
Sayako Kodera
,
David Böttger
,
Tanja Krüger
,
András Kátai
,
Joachim Bös
,
Benjamin Strass
,
Bernd Wolter
ORCID logo ,
Jean Pierre Bergmann
ORCID logo
Version 1 : Received: 28 June 2023 / Approved: 29 June 2023 / Online: 29 June 2023 (10:14:05 CEST)

A peer-reviewed article of this Preprint also exists.

Gourishetti, S.; Schmidt, L.; Römer, F.; Schricker, K.; Kodera, S.; Böttger, D.; Krüger, T.; Kátai, A.; Bös, J.; Straß, B.; Wolter, B.; Bergmann, J.P. Monitoring of Joint Gap Formation in Laser Beam Butt Welding using Neural Network-Based Acoustic Emission Analysis. Crystals 2023, 13, 1451. Gourishetti, S.; Schmidt, L.; Römer, F.; Schricker, K.; Kodera, S.; Böttger, D.; Krüger, T.; Kátai, A.; Bös, J.; Straß, B.; Wolter, B.; Bergmann, J.P. Monitoring of Joint Gap Formation in Laser Beam Butt Welding using Neural Network-Based Acoustic Emission Analysis. Crystals 2023, 13, 1451.

Abstract

This study aimed to explore the feasibility of using airborne acoustic emission in laser beam butt welding for the development of an automated classification system based on neural networks. The focus was on monitoring the formation of joint gaps during the welding process. To simulate various sizes of butt joint gaps, controlled welding experiments were conducted, and the emitted acoustic signals were captured using audible to ultrasonic microphones. To implement an automated monitoring system, a method based on short-time Fourier transformation was developed to extract audio features, and a convolutional neural network architecture with data augmentation was utilized. The results demonstrated that this non-destructive and non-invasive approach was highly effective in detecting joint gap formations, achieving an accuracy of 98%. Furthermore, the system exhibited promising potential for low latency monitoring of the welding process. The classification accuracy for various gap sizes reached up to 90%, providing valuable insights for characterizing and categorizing joint gaps accurately. Additionally, increasing the quantity of training data with quality annotations could potentially improve the classifier model's performance further. This suggests that there is room for future enhancements in the study.

Keywords

laser beam butt welding (LBW); Joint-gap formation; AE-analysis; Non-destructive test-30 ing (NDT); Deep learning; Audible to Ultrasonic sensors

Subject

Engineering, Industrial and Manufacturing Engineering

Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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