Inline Weld Depth Evaluation and Control Based on OCT Keyhole Depth Measurement and Fuzzy Control

Maximilian Schmoeller, Tony Weiss, Korbinian Goetz, Christian Stadter, Christian Bernauer, Michael F. Zaeh

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

Abstract

In an industrial joining process, exemplified by deep penetration laser beam welding, ensuring a high quality of welds requires a great effort. The quality cannot be fully established by testing, but can only be produced. The fundamental requirements for a high weld seam quality in laser beam welding are therefore already laid in the process, which makes the use of control systems essential in fully automated production. With the aid of process monitoring systems that can supply data inline to a production process, the foundation is laid for the efficient and cycle-time-neutral control of welding processes. In particular, if novel, direct measurement methods, such as Optical Coherence Tomography, are used for the acquisition of direct geometric quantities, e.g., the weld penetration depth, a significant control potential can be exploited. In this work, an inline weld depth control system based on an OCT keyhole depth measurement is presented. The system is capable of automatically executing an inline control of the deep penetration welding process based only on a specified target weld depth. The performance of the control system was demonstrated on various aluminum alloys and for different penetration depths. In addition, the ability of the control to respond to unforeseen external disturbances was tested. Within the scope of this work, it was thus possible to provide an outlook on future developments in the field of laser welding technology, which could develop in the direction of an intuitive manufacturing process. This objective should be accomplished through the use of intelligent algorithms and innovative measurement technology—following the example of laser beam cutting, where the processing systems themselves have been provided with the ability to select suitable process parameters for several years now.

Original languageEnglish
Article number1422
JournalProcesses
Volume10
Issue number7
DOIs
StatePublished - Jul 2022

Keywords

  • fuzzy control
  • inline weld depth control
  • inline weld depth evaluation
  • laser beam welding
  • machine learning
  • optical coherence tomography
  • wavelet transformation

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