Burr formation detector for fiber laser cutting based on a photodiode sensor system

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

doi:10.1016/j.optlastec.2017.04.027

Cited by 25 publications

(6 citation statements)

References 16 publications

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“…For cut direction independent detection systems, in many publications, the sensor systems are integrated into the cutting head. Results by using photodiode-based sensors [ 6 ] showed that the mean photodiode’s current increases with lower cut qualities, while similar experiments [ 7 ] revealed increasing mean photodiode currents at lower cut surface roughness. Additionally, by using photodiodes [ 8 ], the burr height during laser cutting is calculated from the standard deviation of the photodiode current.…”

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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“…As early as in the 1980s, scientific studies have been conducted to develop a process monitoring system for laser cutting, with one of the first approaches being to monitor the shower of sparks underneath the sheet during cutting [3,4]. Later many studies have opted for a coaxial photodiode-based monitoring system [5,6]. Several advantages favor the use of coaxial photodiodes for industrial applications, such as the low price, the easy integration into existing beam paths, and the detection of process signals independent of the cutting direction.…”

Section: Introductionmentioning

confidence: 99%

Analysis of response signals to minimally invasive laser modulation in laser cutting

de Oliveira Lopes,

Schneider,

Petring

et al. 2024

Materialwissenschaft Werkst

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A robust real‐time monitoring system for the laser cutting process has been targeted by researchers and users for several decades without significant breakthrough. The use of coaxial photodiodes provides a cost‐effective and straightforward system; however, the assessment of the process state based on the spatially integrated detected signals can lead to misinterpretation due to the numerous and interacting parameter dependencies of the signals. To obtain a clear correlation of the signals to the process state, minimally invasive laser modulation stimulates characteristic state‐dependent response signals. In this study, the response signals to a laser power modulation are analyzed in the visible and infrared range. Furthermore, a spatially and temporally resolved analysis of the process response is enabled by means of high‐speed videography. This provides a comprehensive understanding of the process state in terms of temperature and geometry response to the modulation and offers a reliable exploitation of process emissions for real‐time monitoring and control applications.

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“…Therefore, several types of sensors/ sensing systems have been employed in burr formation monitoring during machining. Schleier, M. et al used a photodiode sensor to detect burr formation during near-infrared fiber laser cutting [5]. Rimpault et al predicted the height of burrs produced during drilling for materials made of carbon fiber reinforced polymers (CFRP), titanium and aluminum alloys in terms of the thrust, torque, hole diameter, circularity, and clearance tool wear using force sensors [6].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Burr Types in Drilling of Al-7075 Using Acoustic Emission and Convolution Neural Networks

Kim

Lee²

2022

IEEE Access

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The formation of exit burrs during the drilling of ductile metals such as aluminum is critical in precision manufacturing and manufacturing automation. Because drilling burrs are difficult to remove, methods to predict various burr types and/or implement burr minimization schemes that consider the various attributes of burrs must be devised. In this study, not only drilling process conditions, including feed, cutting speed, and drill diameter, but also an artificial neural network is implemented to predict the formation of burrs during the drilling of aluminum-7075, which is widely used in the aerospace and automobile industries. Based on the drilling conditions, the main exit burr characteristics, such as burr size and type, were classified experimentally. Three different types of exit burrs (uniform burrs, uniform burrs with caps, and transient burrs) were observed from the aluminum 7075 workpieces. The classification results were further analyzed using burr control charts and an empirical equation, which enables the understanding of the overall influence of the drilling conditions over the burr types. Moreover, acoustic emission (AE) sensor monitoring scheme was utilized to sample the sensitive time-series signals during drilling burr formation. Subsequently, burr types were predicted using artificial intelligence techniques, namely machining learning and deep learning. First, backpropagation (BP) neural networks were constructed using the drilling conditions and AE signals as input vectors. For a comparative prediction, a convolution neural network (CNN) was implemented to obtain spectrogram image inputs from the sampled AE data. The proposed scheme is useful in predicting drilling burr types by employing a sensitive sensor monitoring setup and advanced artificial intelligence techniques, where both prediction results are well matched with experimental results. In addition, the CNN model shows effectiveness for a commonly practiced manufacturing process as it predicts the burr types with better accuracy than the BP network model (0.9375 over 0.8571).INDEX TERMS Drilling burr, burr type prediction, acoustic emission monitoring, AI, CNN.

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Burr formation detector for fiber laser cutting based on a photodiode sensor system

Cited by 25 publications

References 16 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

Analysis of response signals to minimally invasive laser modulation in laser cutting

Prediction of Burr Types in Drilling of Al-7075 Using Acoustic Emission and Convolution Neural Networks

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