Feature extraction in laser welding processes

Geese, Marc; Tetzlaff, Ronald; Carl, Daniel; Blug, A.; Höfler, H.; Abt, Felix

doi:10.1109/cnna.2008.4588677

Cited by 11 publications

(10 citation statements)

References 2 publications

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“…9, it can be seen that the welding seam has noticeable marks corresponding to the areas of low white-pixel count. In the case with red test welding, we were able to detect a process error that occurred even though all parameters were in order [7].…”

Section: Fig 9: Results Of the Preexamination With The Ace-16k The Co...mentioning

confidence: 93%

“…Different algorithms, based on the idea described in [7], were implemented to be applied on the EyeRis system v1.2 for the detection of full-penetration. The application of two of them always led to an accurate detection of full-penetration holes in all treated cases with a high image processing rate, performing the elaboration within 40-100 µs per image.…”

Section: Algorithms For Feature Extractionmentioning

confidence: 99%

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Closed loop control of laser welding processes with cellular neural network (CNN) cameras

Abt¹,

Nicolosi²,

Carl³

et al. 2008

International Congress on Applications of Lasers &Amp; Electro-Optics

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The growing number of laser welding applications from automobile production to micro mechanics require fast and reliable process control systems. The high process dynamics in time, space and intensity, especially in scanner based remote welding or high speed micro welding, demand extremely fast and spatially resolved in-process control systems to create closed loop control for error prevention and correction.Today's conventional micro processor based image processing architectures (as used for example in [1]) are not able to provide the high frame rates needed for the real-time closed loop control of high speed laser welding.With "Cellular Neural Networks" (CNN) it is possible to implement Single-Instruction-Multiple-Data (SIMD)-architectures in the electronic circuitry of each pixel of the camera chip itself in order to produce a so called Focal Plane Processor (FPP). Such pixel parallel systems provide extremely fast real-time image processing. With these new CNNcameras it is now possible to implement a camera based high speed in-process control system for laser welding that enables closed loop control of various quality features.With a multi modal diagnostic approach we were able to identify direct and explicit image attributes for a variety of quality features as a base for the process control. It could be shown that closed loop control of the "full-penetration" quality feature is possible with frame rates of 10 kHz and beyond with a CNN-camera system.

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Section: Fig 9: Results Of the Preexamination With The Ace-16k The Co...mentioning

confidence: 93%

Section: Algorithms For Feature Extractionmentioning

confidence: 99%

Closed loop control of laser welding processes with cellular neural network (CNN) cameras

Abt¹,

Nicolosi²,

Carl³

et al. 2008

International Congress on Applications of Lasers &Amp; Electro-Optics

View full text Add to dashboard Cite

show abstract

“…Afterwards, the resulting image is binarized. As shown in earlier works [6] the intensity image of the laser interaction zone is rather constant for a large range of laser power and feeding rates. Therefore, a global threshold is applicable to binarize the images acquired during the welding process.…”

Section: A Omnidirectional Algorithmmentioning

confidence: 81%

Omnidirectional algorithm for the full penetration hole extraction in laser welding processes

Nicolosi

Tetzlaff

Abt

et al. 2009

2009 European Conference on Circuit Theory and Design

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In this paper a new cellular neural network (CNN) based visual algorithm for welding processes is proposed. The idea described in can be used in processes, whose welding direction has a constant orientation well known a priori. The algorithm proposed in the following is omnidirectional in the sense that it does not depend on the welding direction. This fact enables closed loop control systems for welding processes with curved seeds. On Eye-RIS systems processing times of about 110 mus are achievable for both acquisition and evaluation of full frame images

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“…Laser beam welding is a well-researched method with related work in process observation systems and sensors [3], [4], [5]. Approaches for laser material processing and using classifiers such as Artificial Neural Networks [6], [7], Support Vector Machines [8], [9] and Fuzzy Logic [10] have been discussed.…”

Section: Introductionmentioning

confidence: 99%

A cognitive approach for a robotic welding system that can learn how to weld from acoustic data

Wersborg

Bautze

Born

et al. 2009

2009 IEEE International Symposium on Computational Intelligence in Robotics and Automation - (CIRA)

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Laser beam welding is the method of choice for the high-quality joining of materials. However, for industrial production these systems have to be set up and calibrated manually with much effort. Our objective is to apply intelligent data processing that results in a cognitive technical system that can learn how to weld, speed up the configuring process, and reduce costs. While monitoring laser welding with cameras and optical sensors has already been demonstrated elsewhere, this paper emphasizes the benefits of monitoring with acoustic sensors and feature extraction. Using acoustic sensors, the cognitive system is more sensitive to strong optical radiation. Several combined methods such as wavelet analysis, fast Fourier transformation, and linear dimensionality reduction are evaluated with sensor data from real experiments. Finally, as machine learning, the results are classified with learned reference data to obtain reliable information for monitoring and possibly using closedloop control.

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Feature extraction in laser welding processes

Cited by 11 publications

References 2 publications

Closed loop control of laser welding processes with cellular neural network (CNN) cameras

Closed loop control of laser welding processes with cellular neural network (CNN) cameras

Omnidirectional algorithm for the full penetration hole extraction in laser welding processes

A cognitive approach for a robotic welding system that can learn how to weld from acoustic data

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