Machine learning for 3D simulated visualization of laser machining

Heath, Daniel J.; Grant-Jacob, James; Xie, Yunhui; MacKay, Benita; Baker, James A.; Eason, R.W.; Mills, B.

doi:10.1364/oe.26.021574

Cited by 42 publications

(26 citation statements)

References 19 publications

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“…The NN variant chosen in this work was a conditional generative adversarial network (cGAN) [18]. Previous work has shown that, when applied to laser machining, a cGAN can transform a spatial intensity profile of a laser pulse into a predicted depth profile that includes realistic randomly generated effects, such as debris [9,19]. Critically, as experimental data was used as the training data, all laser instabilities, such as variations in pulse energy (determined to be ∼0.5%) and spatial intensity profile inhomogeneities, were also included in the learning process for the neural network.…”

Section: Application Of the Neural Networkmentioning

confidence: 99%

“…1 where profile predictions based on a naive method of removing material evenly where the laser is incident on the material are shown to be very different to those observed experimentally. Previously, we have demonstrated a deep learning approach for the simulation of a sample surface after a single, shaped, ultrafast laser pulse exposure [9]. Experimental examples of spatially shaped intensity profiles and their resulting interferometrically depth-mapped structures in electroless nickel were used with a deep learning approach to train a neural network (NN).…”

Section: Introductionmentioning

confidence: 99%

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Modelling laser machining of nickel with spatially shaped three pulse sequences using deep learning

McDonnell¹,

Grant-Jacob²,

Xie³

et al. 2020

Opt. Express

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Femtosecond laser machining is a complex process, owing to the high peak intensities involved. Modelling approaches for the prediction of final sample quality based on photon-atom interactions are therefore challenging to extrapolate up to the microscale and beyond. The problem is compounded when multiple exposures are used to produce a final structure, where surface modifications from previous exposures must be taken into consideration. Neural network approaches allow for the automatic creation of a model that accounts for these challenging processes, without any physical knowledge of the processes being programmed by a specialist. We present such a network for the prediction of surface quality for multi-exposure femtosecond machining on a 5µm electroless nickel layer deposited on copper, where each pulse is uniquely spatially shaped using a spatial light modulator. This neural network modelling method accurately predicts the surface profile after three, sequential, overlapping exposures of dissimilar intensity patterns. It successfully reproduces such effects as the sub-diffraction limit machining feasible with multiple exposures, and the smoothing effect on edge-burr from previous exposures expected in multi-exposure laser machining.

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Section: Application Of the Neural Networkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Modelling laser machining of nickel with spatially shaped three pulse sequences using deep learning

McDonnell¹,

Grant-Jacob²,

Xie³

et al. 2020

Opt. Express

Self Cite

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“…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].…”

Section: Neural Networkmentioning

confidence: 99%

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

Grant-Jacob

Xie

MacKay

et al. 2019

Environ. Res. Commun.

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The identification of mixtures of particles in a solution via analysis of scattered light can be a complex task, due to the multiple scattering effects between different sizes and types of particles. Deep learning offers the capability for solving complex problems without the need for a physical understanding of the underlying system, and hence offers an elegant solution. Here, we demonstrate the application of convolutional neural networks for the identification of the concentration of microparticles (silicon dioxide and melamine resin) and the solution salinity, directly from the scattered light. The measurements were carried out in real-time using a Raspberry Pi, light source, camera, and neural network computation, hence demonstrating a portable and low-cost environmental marine sensor.

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“…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].…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2019

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Whilst advances in lasers now allow the processing of practically any material, further optimisation in precision and efficiency is highly desirable, in particular via the development of real-time detection and feedback systems. Here, we demonstrate the application of neural networks for system monitoring via visual observation of the work-piece during laser processing. Specifically, we show quantification of unintended laser beam modifications, namely translation and rotation, along with real-time closed-loop feedback capable of halting laser processing immediately after machining through a ∼450 nm thick copper layer. We show that this approach can detect translations in beam position that are smaller than the pixels of the camera used for observation. We also show a method of data augmentation that can be used to significantly reduce the quantity of experimental data needed for training a neural network. Unintentional beam translations and rotations are detected concurrently, hence demonstrating the feasibility for simultaneous identification of many laser machining parameters. Neural networks are an ideal solution, as they require zero understanding of the physical properties of laser machining, and instead are trained directly from experimental data.

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Machine learning for 3D simulated visualization of laser machining

Cited by 42 publications

References 19 publications

Modelling laser machining of nickel with spatially shaped three pulse sequences using deep learning

Modelling laser machining of nickel with spatially shaped three pulse sequences using deep learning

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

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

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