ILAS 2019: DEEP LEARNING IN LASER PROCESSING
monitoring and process control via an NN is shown in Figure 1. Femtosecond laser pulses were focused down to a spot size of approximately 30 microns, while the sample was continuously imaged by the camera. Te camera images were then processed by the NN, which could output a wide range of experimental information, and also control the laser. To date, via an NN we are
able to determine; physical changes to the material, fluctuations in laser fluence, the number of pulses used for machining, and values quantifying the changes in the shape and position of the laser beam on the sample. Tis can be done with higher accuracy and speed than the human eye, requiring approximately only 10ms to process each camera image. Te NN was also able to control the laser in order to stop it exactly at the point it would
to predict the effect that laser beam shape has on machined samples… our early results have been staggering
We have used a NN
have machined through a random – and hence unknown – thickness of copper, preventing damage to an underlying layer of glass. Such process control could be applied to the laser cleaning of rust, for example, as the thickness of rust at each position would be unknown, while zero laser damage should occur to the underlying surface. Te NN was not only able to cease laser machining at exactly the point of complete removal of the copper, but was able to predict the time remaining until task completion. While direct interrogation of the internal workings of an NN is extremely challenging, we suspect that
the NN was able to correlate the amount of debris and the appearance of the machined surface, with some measure of the remaining depth of copper leſt to machine.
Predictive Capability Finding the optimal combination of laser machining parameters, such as laser power, laser wavelength, beam size and machining time, for a customer design specification, can be a costly and time-consuming process. Typically, a technician will systemically explore all combinations of laser machining parameters. It would therefore be convenient if an NN could instead be used to determine the optimal parameters automatically. Such an NN would need to comprehend
everything from the physical equations describing the interaction of light and matter, heat transfer, and the laws of diffraction, through to the properties of the sample and the distribution and probability of debris and burr, through to the particular nuances of the laser itself. However, this complexity actually isn’t a problem, as the NN demonstrated here was able to learn all this, by itself, exclusively from images of laser machined samples. Although we have currently only used a NN
Figure 2: Concept showing a neural network using an inputted beam shape to predict the 3D surface map of machined sample.
www.lasersystemseurope.com | @lasersystemsmag
to predict the effect that laser beam shape has on machined samples, our early results have been staggering. Figure 2 shows the concept of our work – a NN being shown a beam shape, which it then uses to predict how the 3D surface map of a machined sample would look. Figure 3 shows the results of this, and demonstrates how close the predicted 3D surface map is to the actual result. It is important to realise that the NN had never encountered anything like this particular shape during the training process, and therefore the accuracy of the predicted 3D
Figure 3: An inputted beam shape (top), was used to predict the 3D depth profile of a machined sample (middle), which was then observed to be very close to what was experimentally measured (bottom).
surface demonstrated here would apply to other beam shapes when first show to the NN. Te NN was so effective that it is almost impossible to tell which image is the genuine experimental result. Of particular interest are the slightly raised surfaces in the middle of the laser machined regions. Te sample material here, nickel, is known to melt and reform when irradiated with femtosecond laser pulses, and hence the NN had also learnt rules equivalent to fluid dynamics.
The future Computing power increases exponentially as we use the technology of today to build the technology of tomorrow, and it is now the graphics processing unit (GPU) paradigm that is driving innovation in deep learning. NNs have demonstrated basic creativity – for example, see AlphaZero and DeepMind – and hence the potential for NNs to discover new approaches for laser machining is probably not that far away – in fact we are already working on this. By devoting an entire network to a specialised task, NNs have already surpassed many specific human capabilities. Comprehending femtosecond laser machining can now also be added to this list.
ISSUE 42 • SPRING 2019 LASER SYSTEMS EUROPE 25
Page 1 |
Page 2 |
Page 3 |
Page 4 |
Page 5 |
Page 6 |
Page 7 |
Page 8 |
Page 9 |
Page 10 |
Page 11 |
Page 12 |
Page 13 |
Page 14 |
Page 15 |
Page 16 |
Page 17 |
Page 18 |
Page 19 |
Page 20 |
Page 21 |
Page 22 |
Page 23 |
Page 24 |
Page 25 |
Page 26 |
Page 27 |
Page 28 |
Page 29 |
Page 30 |
Page 31 |
Page 32 |
Page 33 |
Page 34 |
Page 35 |
Page 36
