Monitoring of high-power CO2 laser cutting by means of an acoustic microphone and photodiodes

Keuster, Johan De; Duflou, Joost R.; Kruth, Jean Pierre

doi:10.1007/s00170-006-0695-z

Cited by 29 publications

(7 citation statements)

References 10 publications

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“…In previous publications the authors of this paper reported on the use of acoustic and photodiode based systems for process monitoring of higher power CO 2 flame cutting [14,15,16]. In line with observations made by the researchers referred to above, the authors concluded that photodiodes can provide useful information for quality assurance purposes with a very limited time delay.…”

Section: Introductionsupporting

confidence: 80%

Development of a real time monitoring and adaptive control system for laser flame cutting

Duflou¹,

Sichani²,

Keuster³

et al. 2009

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

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A real-time, adaptive control and optimization system for laser flame cutting of thick plates of mild steel has been developed. The proposed system consists of two subsystems: a process monitoring system and a control and optimization system. The first subsystem aims at the on-line observation of the process status and corresponding cut quality. Since the different cut quality characteristics (e.g. cutting edge striations or dross) cannot be measured directly, the proposed system is based on so-called sensing parameters, which are easily-observable physical parameters that correlate well with the quality characteristics of the cut surface. The applicability of different optical sensors (photodiodes and a NIR-camera) has been investigated. The most optimal configuration of the process monitoring system is presented, including an overview of the selected set of sensing parameters. The second subsystem, the real-time control and optimization system, supports the adaptation of the process parameters, based on the cut quality information obtained from the process monitoring system. A suitable hardware configuration for the real-time control and optimization is presented, starting from the original platform (i.e. an industrial 2D laser cutting machine). A generic expert strategy, has been designed for the control and optimization purpose. Using the developed experimental platform, the performance of the expert system was verified and optimized for different material-thickness combinations. The obtained results demonstrate the effectiveness of the chosen approach in terms of increased autonomy, productivity, and efficiency of the process, as well as elimination of the need for manual quality control and the possibility to automatically generate quality reports.

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Section: Introductionsupporting

confidence: 80%

Development of a real time monitoring and adaptive control system for laser flame cutting

Duflou¹,

Sichani²,

Keuster³

et al. 2009

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

Self Cite

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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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“…Nagels [10], concentrated on the influence of sulphur content in steel on oxygen assisted CO 2 laser cutting. De-Keuster [11], alluded to the applicability for monitoring purposes of two types of sensors is indicated the microphone and the photodiode. For two types, relation between the sensor output and the cut quality is presented.…”

Section: Introductionmentioning

confidence: 99%

Modelling and Optimization of CO2 Laser Turning Process for Armored Steel Assisted by Oxygen

faruk

rezk

Marzouk

et al. 2022

ERJ. Engineering Research Journal

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The current paper investigates the performance of armored steel (500HB) when machined by CO 2 laser turning process (LTP) with oxygen gas assistance for grooving operation. The different working parameters were investigated which include; laser power (P), gas pressure (GP) feed rate (F) and motor speed (S). The different groove dimensions and were considered as the model responses which include; depth of cut (DC), upper cut width (UC) and lower cut width (LC), in addition to the performance aspects of root round error and metal removal rate (MRR). For this purpose, a test rig was designed and fabricated. The experimental results are mathematically modeled by response surface methodology. The results are modeled and illustrated to closely understand the effect and surface quality of CO 2 -LTMP. The optimum process conditions for grooving Armored Steel (500HB) were identified. Results showed that, DC and width of cut and roundness error increase with increasing P and GP, but MRR decreases with increasing F. The motor speed has a moderate effect on the machining process of armored steel under investigation. The optimal combination of machining conditions maximized MRR, DC, LC, UC and minimized root round error to 0.0384 gm, 0.6632 mm, 0.2583 mm, 0.4684 mm and 15.7832 µm respectively. The error between optimum experimental results and the optimum predicted values for MRR, DC, LC, UC and root round error lie within 2.8%, 2.6%, 2.9%, 1% and 6.9%, respectively.

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Monitoring of high-power CO2 laser cutting by means of an acoustic microphone and photodiodes

Cited by 29 publications

References 10 publications

Development of a real time monitoring and adaptive control system for laser flame cutting

Development of a real time monitoring and adaptive control system for laser flame cutting

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

Modelling and Optimization of CO2 Laser Turning Process for Armored Steel Assisted by Oxygen

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