Monitoring and adaptive control of CO2 laser flame cutting

Sichani, Ehsan Fallahi; Keuster, Johan De; Kruth, Jean‐Pierre; Duflou, Joost R.

doi:10.1016/j.phpro.2010.08.076

Cited by 14 publications

(4 citation statements)

References 8 publications

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“…Additionally, by using photodiodes [ 8 ], the burr height during laser cutting is calculated from the standard deviation of the photodiode current. From camera images of flame cutting with a CO 2 laser, it is already possible to calculate the roughness, striation angle and the burr formation [ 3 , 9 ]. In detail, a NIR camera with a 40-Hz sampling rate was used, and the quality was calculated by the size of the hot process zone and the size of its circumscribed rectangle.…”

Section: Introductionmentioning

confidence: 99%

Laser Cut Interruption Detection from Small Images by Using Convolutional Neural Network

Adelmann

Schleier

Hellmann

2021

Sensors

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In this publication, we use a small convolutional neural network to detect cut interruptions during laser cutting from single images of a high-speed camera. A camera takes images without additional illumination at a resolution of 32 × 64 pixels from cutting steel sheets of varying thicknesses with different laser parameter combinations and classifies them into cuts and cut interruptions. After a short learning period of five epochs on a certain sheet thickness, the images are classified with a low error rate of 0.05%. The use of color images reveals slight advantages with lower error rates over greyscale images, since, during cut interruptions, the image color changes towards blue. A training set on all sheet thicknesses in one network results in tests error rates below 0.1%. This low error rate and the short calculation time of 120 µs on a standard CPU makes the system industrially applicable.

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Section: Introductionmentioning

confidence: 99%

Laser Cut Interruption Detection from Small Images by Using Convolutional Neural Network

Adelmann

Schleier

Hellmann

2021

Sensors

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“…Experimental architecture for process monitoring. [12]. The present study addresses the problem of determining whether a significant and non negligible dross attachment is present or not in a performed cut, based on the high resolution camera images, which are collected at high speed during the cutting process.…”

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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“…For detecting burr formation during laser cutting, different approaches using cameras, phododiodes or acoustic emission were investigated. In [ 2 , 3 ] burr formation, roughness and striation angle during laser cutting with a 6 kW CO 2 laser are determined by using a NIR camera sampling with 40 Hz. By using two cameras in [ 4 ], laser cutting with a CO 2 laser is monitored by observing the spark trajectories underneath the sheet and melt bath geometries and correlate this to the burr formation or overburning defects.…”

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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Monitoring and adaptive control of CO2 laser flame cutting

Cited by 14 publications

References 8 publications

Laser Cut Interruption Detection from Small Images by Using Convolutional Neural Network

Laser Cut Interruption Detection from Small Images by Using Convolutional Neural Network

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

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