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

Abstract

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.

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

“…The reason for this can also be seen in Figure 3, where good cuts are much more similar to cuts with burr, while cut interruptions look very different to both of the other failure classes. Both values individually agree with the literature values, which are 99.9% for the cut interruptions [13] and 92% for the burr detection [5], yet for burr detection in the literature a more complex burr definition is chosen. This shows that cut interruptions are much easier to detect from camera images compared to burr formations.…”

“…Thinner sheet thicknesses require a higher production effort per stack and are more difficult to cut because they are very flexible, and warp under the gas pressure and thermal influence. In these experiments only one sheet thickness is used, but please note that in previous publications with similar systems an adaptation of the results to other sheet thicknesses was possible with only minor additional expenses [ 5 , 13 ].…”

“…In addition, an adaptation to other materials or sheet thicknesses requires a large engineering effort to define respective threshold values by extensive investigations. To avoid this problem, [ 13 ] uses a convolutional neural network to calculate cut interruptions from camera images during fiber laser cutting of different sheet thicknesses with an accuracy of 99.9%. Another approach is performed by using a regression model based on polynomial logistics [ 14 ] to calculate the interruptions from laser machine parameters only.…”

“…In addition, many of the previous studies applied CO 2 lasers, which are often replaced nowadays by modern fiber or disk lasers, for which, in turn, fewer reports are available. To detect both failures with the same system, we chose the evaluation of camera images with neural networks, as they are able to achieve a high accuracy in detecting both cut failures [ 5 , 13 ]. The use of neural networks, especially for convolutional neural networks (CNN), has been demonstrated for various image classification purposes, such as face recognition and object detection [ 15 , 16 ], in medicine for cancer detection [ 17 ] and electroencephalogram (EEG) evaluations [ 18 ] or in geology for earthquake detection [ 19 ].…”

Generalisierung eines maschinellen Lernmodells für das Laserschneiden auf verschiedenen Blechdicken/Generalising a machine learning model for laser cutting on various sheet thicknesses

“…Die Arbeiten [6][7][8][9] zeigen, dass eine Fehlschnitterkennung mit ML umgesetzt werden kann. Dazu werden in [6][7][8] visuelle Sensorsignale (Photodioden oder Kamerabilder) verwendet, welche die Eingabedaten für das Trainieren Neuronaler Netze bereitstellen. Die Laserschneidmaschine ist eine vielseitige Werkzeugmaschine [10].…”