Vorstellung des Verbundprojekts “INESS” — Prozess‐Sicherung beim Laserstrahlschweißen

Müller‐Borhanian, J.; Jakschitsch, A.; Deininger, C.; Hohenadel, J.; Pfitzner, D.; Kogel‐Hollacher, Markus; Kattwinkel, Axel; Körner, Klaus; Wiesendanger, Tobias F.; Ruprecht, Aiko K.

doi:10.1002/9783527624225.ch33

“…Up to 14000 frames per second were acquired and evaluated for an instant feedback to the laser power of the welding process. This is about one order of magnitude faster than the frame rates achieved with conventional image processing systems [5] for similar applications. However, this special application benefits from an extremely strong laser with an optical power of 6 kW at a wavelength of 1030 nm as a light source.…”

Section: Introductionmentioning

confidence: 87%

High power LED lighting for CNN based image processing at frame rates of 10 kHz

Blug

¹

,

Jetter

²

,

Strohm

³

et al. 2010

2010 12th International Workshop on Cellular Nanoscale Networks and Their Applications (CNNA 2010)

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CNN based cameras are able to process images in the kHz range in machine vision systems, as demonstrated for the closed loop control of laser welding processes. Other applications do not benefit from a comparable light source. This paper reports the current state of a LED based lighting for the inspection of cylindrical metallic surfaces on aluminum wires. Frame rates of 10 kHz and exposure times of 20 µs have been achieved so far

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“…CNN have been introduced by Chua and Yang in 1988 [1] to be employed in such areas like image processing and pattern recognition. The basic circuit unit of CNNs is called a cell.…”

Section: Introductionmentioning

confidence: 99%

“…

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.

…”

mentioning

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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“…The FPH at full penetration is a well-known image feature used for monitoring or closed-loop control of laser keyhole welding processes [5][6][7]. However, the FPH exhibits rapid fluctuations with frequencies of several kHz [8,9].…”

Section: Introductionmentioning

confidence: 99%

The full penetration hole as a stochastic process: controlling penetration depth in keyhole laser-welding processes

Blug

¹

,

Abt²,

Nicolosi³

et al. 2012

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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 closedloop 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.

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Vorstellung des Verbundprojekts “INESS” — Prozess‐Sicherung beim Laserstrahlschweißen

Cited by 4 publications

References 1 publication

High power LED lighting for CNN based image processing at frame rates of 10 kHz

High power LED lighting for CNN based image processing at frame rates of 10 kHz

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

The full penetration hole as a stochastic process: controlling penetration depth in keyhole laser-welding processes

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