Abstract

Although laser-welding processes are frequently used in industrial production the quality control of these processes is not satisfactory yet. Until recently, the “full penetration hole” was presumed as an image feature which appears when the keyhole opens at the bottom of the work piece. Therefore it was used as an indicator for full penetration only. We used a novel camera based on “cellular neural networks” which enables measurements at frame rates up to 14 kHz. The results show that the occurrence of the full penetration hole can be described as a stochastic process. The probability to observe it increases near the full penetration state. In overlap joints, a very similar image feature appears when the penetration depth reaches the gap between the sheets. This stochastic process is exploited by a closed-loop system which controls penetration depth near the bottom of the work piece (“full penetration”) or near the gap in overlap joints (“partial penetration”). It guides the welding process at the minimum laser power necessary for the required penetration depth. As a result, defects like spatters are reduced considerably and the penetration depth becomes independent of process drifts such as feeding rate or pollution on protection glasses.

Highlights

  • Laser beam welding is a joining technique frequently used in high volume applications, such as in the automotive industry

  • We introduced a novel camera technology based on so called “cellular neural networks” (CNN) in order to increase frame rate and robustness of the full penetration hole (FPH) detection

  • In this paper the FPH image feature in zinc-coated steel sheets is regarded as a stochastic process

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Summary

Introduction

Laser beam welding is a joining technique frequently used in high volume applications, such as in the automotive industry. In so called keyhole welding processes, the laser beam is focused to intensities between 106 and 107 W/cm2 In this regime, vaporization takes place on the surface of metals like steel or aluminum. The approach of measuring the contrast of image intensity between the full penetration hole and the surrounding laser interaction zone at frame rates below 1 kHz seemed inappropriate. For this reason, we introduced a novel camera technology based on so called “cellular neural networks” (CNN) in order to increase frame rate and robustness of the FPH detection. Due to its similarity with the FPH in the full penetration state, we consider both image features as “full penetration holes” (FPH) in the following

System setup
Definition of measurement quantities
Open-loop characterization
Closed-loop control
Welding results
Findings
Conclusions

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