Toward real-time quality analysis measurement of metal laser cutting

Alippi, Cesare; Bono, Valentina; Piuri, Vincenzo; Scotti, Fabio

doi:10.1109/vims.2002.1009354

Cited by 9 publications

(5 citation statements)

References 5 publications

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“…In addition, some elaborate approaches like using a fiber Fabry-Pérot cavity microphone have been demonstrated [ 3 ]. To use such sensors as an industrial product, the sensor must be able to detect cut interruptions independent from the cutting direction, which is not feasible by below-bed sensors, as shown in [ 4 , 5 ]. For cut direction independent detection systems, in many publications, the sensor systems are integrated into the cutting head.…”

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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“…In addition, the models were not directly deployed on the industrial machines and they were not always tested for computational times. Clearly, the real-time assessment of the production quality in laser cutting production lines is still challenging [9], [10]. Domain Adaptation.…”

Section: Related Workmentioning

confidence: 99%

Real-Time Cross-Dataset Quality Production Assessment in Industrial Laser Cutting Machines

Peghini

Zignoli

Gandolfi³

et al. 2021

Pattern Recognition. ICPR International Workshops and Challenges

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In laser cutting processes, cutting failure is one of the most common causes of faulty productions. Monitoring cutting failure events is extremely complex, as failures might be initiated by several factors, the most prominent probably being the high production speeds required by modern standards. The present work aims at creating and deploying a classifier able to assess the status of a production cutting quality in a realtime fashion. To this aim, multiple datasets were collected in different environmental conditions and with different sensors. Model inputs include photo-sensors and production parameters. At first, different algorithms were tested and rated by prediction ability. Second, the selected algorithm was deployed on a GPU embedded system and added to the current machine configuration. The final system can receive the input data from the sensors, perform the inference, and send back the results to the computer numerical control. The data management is based on a clientserver architecture. The selected algorithm and hardware showed good performances despite multiple changes in the environmental conditions (domain adaptation ability) both in terms of prediction ability (accuracy) and computational times.

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“…With this system, it can be proven that the striation formation is caused by cyclic melt removal. Alippi et al [15] demonstrated the possibility to construe the cut quality by measuring the flying sparks underneath the workpiece. A camera measures the width and angle of the flying sparks during CO2 laser cutting and the cut quality is evaluated by applying an artificial neural network to the measured data.…”

Section: Introductionmentioning

confidence: 99%

Optical Cutting Interruption Sensor for Fiber Lasers

Adelmann

Schleier

Neumeier³

et al. 2015

Applied Sciences

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Abstract:We report on an optical sensor system attached to a 4 kW fiber laser cutting machine to detect cutting interruptions. The sensor records the thermal radiation from the process zone with a modified ring mirror and optical filter arrangement, which is placed between the cutting head and the collimator. The process radiation is sensed by a Si and InGaAs diode combination with the detected signals being digitalized with 20 kHz. To demonstrate the function of the sensor, signals arising during fusion cutting of 1 mm stainless steel and mild steel with and without cutting interruptions are evaluated and typical signatures derived. In the recorded signals the piercing process, the laser switch on and switch off point and waiting period are clearly resolved. To identify the cutting interruption, the signals of both Si and InGaAs diodes are high pass filtered and the signal fluctuation ranges being subsequently calculated. Introducing a correction factor, we identify that only in case of a cutting interruption the fluctuation range of the Si diode exceeds the InGaAs diode. This characteristic signature was successfully used to detect 80 cutting interruptions of 83 incomplete cuts (alpha error 3.6%) and system recorded no cutting interruption from 110 faultless cuts (beta error of 0). This particularly high detection rate in combination with the easy integration of the sensor, highlight its potential for cutting interruption detection in industrial applications. OPEN ACCESSAppl. Sci. 2015, 5 545

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Toward real-time quality analysis measurement of metal laser cutting

Cited by 9 publications

References 5 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

Real-Time Cross-Dataset Quality Production Assessment in Industrial Laser Cutting Machines

Optical Cutting Interruption Sensor for Fiber Lasers

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