Photodiode-based cutting interruption sensor for near-infrared lasers

Adelmann, Benedikt; Schleier, Max; Neumeier, B.; Hellmann, Ralf

doi:10.1364/ao.55.001772

Cited by 14 publications

(4 citation statements)

References 11 publications

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“…The online estimation of dross attachment represents a challenging and innovative issue in the laser cutting context. Related works employ photodiodes [7], [8], to transduce the cut image from an optical signal to an electrical one. More recently, camera based monitoring systems are used, which guarantee a much more high quantity of information of the cut, [9], [10], [11], Fig.…”

Section: Introductionmentioning

confidence: 99%

Dross attachment estimation in the laser-cutting process via Convolutional Neural Networks (CNN)

Franceschetti

Pacher²,

Tanelli

et al. 2020

2020 28th Mediterranean Conference on Control and Automation (MED)

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Laser cutting of metals offers the advantage of high precision and accuracy. Dross attachment, measured as the length of the re-solidified material perpendicular to the surface, has definitely the highest impact on the overall process quality. Dross attachment is commonly judged by skilled technicians that evaluate the cut quality. Process parameters are optimized to maximize the cutting speed while keeping an acceptable level of dross attachment. However, in practice, increased levels of dross may occur due to different processing conditions. In this framework, a real-time dross attachment monitoring system is desired. Within the stream of vision based monitoring systems, in this work we use high frequency images generated by a precision camera, mounted on the laser head, to capture the cutting process light emission. A CNN-based classification system is developed, where captured images are fed into the trained network with the aim of automatically recognize if a predetermined dross attachment level is exceeded. To our best knowledge, this is the first work where a CNN is used for monitoring the quality of laser cutting process via dross attachment classification.

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Section: Introductionmentioning

confidence: 99%

Dross attachment estimation in the laser-cutting process via Convolutional Neural Networks (CNN)

Franceschetti

Pacher²,

Tanelli

et al. 2020

2020 28th Mediterranean Conference on Control and Automation (MED)

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“…Also for cut interruption detection, most approaches are based on photodiode signals or camera images. Photodiode-based methods for cut interruption detection are signal threshold-based [ 10 ], done by the comparison of different photodiodes [ 11 ] or based on cross-correlations [ 12 ]. However, all those methods have the disadvantage of requiring thresholds that vary with the sheet thickness or laser parameters.…”

Section: Introductionmentioning

confidence: 99%

Simultaneous Burr and Cut Interruption Detection during Laser Cutting with Neural Networks

Adelmann

Hellmann

2021

Sensors

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In this contribution, we compare basic neural networks with convolutional neural networks for cut failure classification during fiber laser cutting. The experiments are performed by cutting thin electrical sheets with a 500 W single-mode fiber laser while taking coaxial camera images for the classification. The quality is grouped in the categories good cut, cuts with burr formation and cut interruptions. Indeed, our results reveal that both cut failures can be detected with one system. Independent of the neural network design and size, a minimum classification accuracy of 92.8% is achieved, which could be increased with more complex networks to 95.8%. Thus, convolutional neural networks reveal a slight performance advantage over basic neural networks, which yet is accompanied by a higher calculation time, which nevertheless is still below 2 ms. In a separated examination, cut interruptions can be detected with much higher accuracy as compared to burr formation. Overall, the results reveal the possibility to detect burr formations and cut interruptions during laser cutting simultaneously with high accuracy, as being desirable for industrial applications.

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“…This increase in their use is mainly based on the enormous potential of these sensors, together with their easy mounting, and the existing demand to incorporate monitoring systems in industrial applications. Adelmann et al [5] and Schleier et al [6] demonstrated the capability of InGaAs photodiodes for detecting penetration losses and the appearance of cutting defects, respectively, whereas Garmendia et al [7] employed Si photodiodes for monitoring the laser cutting process and evaluating quality. In all cases, these sensors provided a high detection rate and minimal errors.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Photodiode Monitoring in Laser Cutting

Garcia¹,

Ramos²,

Arrizubieta

et al. 2020

Applied Sciences

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The paper presents the results of an analysis based on the photodiode monitoring signals obtained during the laser cutting of aluminum and stainless steel plates. The mean level of the monitoring signal was measured and related to the process parameters and the quality achieved. The investigation was conducted in the visible and infrared spectra simultaneously for each experiment and a similar behavior at both spectra was observed, concluding the existence of a relationship between the monitoring signal, the quality of the performed cut, and the characteristics of the cutting scenario. Both visible and infrared monitoring signals were found not to vary as long as the parameter used values ensuring that the cut quality was good. Nevertheless, their mean values tended to increase as the cutting quality became worse. The measured intensity of the visible spectrum signal was associated with the vapor plume formation during the cutting process, whereas the infrared signal was related to the temperatures reached.

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Photodiode-based cutting interruption sensor for near-infrared lasers

Cited by 14 publications

References 11 publications

Dross attachment estimation in the laser-cutting process via Convolutional Neural Networks (CNN)

Dross attachment estimation in the laser-cutting process via Convolutional Neural Networks (CNN)

Simultaneous Burr and Cut Interruption Detection during Laser Cutting with Neural Networks

Analysis of Photodiode Monitoring in Laser Cutting

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