Predictive capabilities for laser machining via a neural network

Abstract

The interaction between light and matter during laser machining is particularly challenging to model via analytical approaches. Here, we show the application of a statistical approach that constructs a model of the machining process directly from experimental images of the laser machined sample, and hence negating the need for understanding the underlying physical processes. Specifically, we use a neural network to transform a laser spatial intensity profile into an equivalent scanning electron microscope image of the laser-machined target. This approach enables the simulated visualization of the result of laser machining with any laser spatial intensity profile, and hence demonstrates predictive capabilities for laser machining. The trained neural network was found to have encoded functionality that was consistent with the laws of diffraction, hence showing the potential of this approach for discovering physical laws directly from experimental data.

Patterned Nanofoam Fabrication from a Variety of Materials via Femtosecond Laser Pulses

“…Upon demonstration of the fabrication of patterned nanofoam, including a QR code of glass nanofoam, we found that we were able to successfully fabricate patterns of several hundred microns wide with a machined pattern pixel area of size 35 µm × 35 µm. Further work will focus on the optimisation of the size and structure of the nanofoam via machine learning [33].…”

Particle and salinity sensing for the marine environment via deep learning using a Raspberry Pi

“…A convolutional neural network was used, which is a type of NN designed mainly for image processing [57,67], with a regression output. The regression output enabled the capability for the NN to produce a continuous output within a certain range [68].…”

“…Deep learning, which is an approach based on the application of neural networks (NNs) [39][40][41], has already enabled advances in imaging [42,43] and enabled automated classification of objects in images [44,45], such as label-free cell classification [46], as well as object classification through scattering media [47][48][49] and through scattering pattern imaging [50,51]. Using NNs to determine particle size and refractive index from their scattering pattern was proposed by [52] and has been subsequently demonstrated experimentally on colloidal spherical particles [53][54][55][56], showing that NNs can bypass the need to develop complex modelling [57]. Moreover, the ability to update a NN [58], for example to monitor additional particles without the need to physically change a sensor, makes such an approach particularly desirable, especially when implemented on a micro-computer, such as a Raspberry Pi [59,60].…”

Deep learning for the monitoring and process control of femtosecond laser machining

“…Machine learning has allowed for predictive control for self-tuning mode-locked lasers [33], and in our previous work machine learning has shown the application of CNNs to produce realistic and accurate depth profiles and surface appearance predictions that would result from femtosecond laser ablation of metals, despite the extremely nonlinear nature of the process [34,35]. Other work with CNNs has shown identification of workpiece material, laser power, and number of pulses used to machine microstructures, directly from camera images of the work-piece during laser machining [35]. Here we demonstrate training data augmentation techniques to aid the detection of changes in beam translation and rotation, and real-time closed-loop feedback for efficient laser machining through thin films.…”