Laser machining can depend on the combination of many complex and nonlinear physical processes.Simulations laser that are built from first-principles, such as photon-atom interaction, therefore challenging to scale-up experimentally useful dimensions.Here, we demonstrate a simulation approach using neural network, which requires zero knowledge underlying processes instead uses experimental data directly create model experiment.The network modelling was shown accurately predict 3D surface...

Abstract Whilst advances in lasers now allow the processing of practically any material, further optimisation precision and efficiency is highly desirable, particular via development real-time detection feedback systems. Here, we demonstrate application neural networks for system monitoring visual observation work-piece during laser processing. Specifically, show quantification unintended beam modifications, namely translation rotation, along with closed-loop capable halting immediately...

Visualizing structures smaller than the eye can see has been a driving force in scientific research since invention of optical microscope. Here, we use network neural networks to create lens that ability transform 20× microscope images into resolution comparable 1500× scanning electron image. In addition magnification, simultaneously identifies types objects present, and hence label, colour-enhance remove specific magnified The was used for imaging Iva xanthiifolia Galanthus pollen grains,...

Particle pollution is a global health challenge that linked to around three million premature deaths per year. There therefore great interest in the development of sensors capable precisely quantifying both number and type particles. Here, we demonstrate an approach leverages machine learning order identify particulates directly from their scattering patterns. We show capability for producing 2D sample map spherical particles present on coverslip, also real-time identification range...

Abstract The identification of mixtures particles in a solution via analysis scattered light can be complex task, due to the multiple scattering effects between different sizes and types particles. Deep learning offers capability for solving problems without need physical understanding underlying system, hence an elegant solution. Here, we demonstrate application convolutional neural networks concentration microparticles (silicon dioxide melamine resin) salinity, directly from light....

Abstract We demonstrate the capability for identification of single particles, via a neural network, directly from backscattered light collected by 30-core optical fibre, when particles are illuminated using mode fibre-coupled laser source. The network was shown to be able determine specific species pollen with ∼97% accuracy, along distance between end sensing fibre and an associated error ±6 μ m. ability classify has potential in environments which transmission imaging is neither possible...

The response of adult human bone marrow stromal stem cells to surface topographies generated through femtosecond laser machining can be predicted by a deep neural network. network is capable predicting cell statistically significant level, including positioning predictions with probability P < 0.001, and therefore used as model determine the minimum line separation required for alignment, implications tissue structure development engineering. application network, model, reduces amount...

Femtosecond laser machining is a complex process, owing to the high peak intensities involved. Modelling approaches for prediction of final sample quality based on photon-atom interactions are therefore challenging extrapolate up microscale and beyond. The problem compounded when multiple exposures used produce structure, where surface modifications from previous must be taken into consideration. Neural network allow automatic creation model that accounts these processes, without any...

Laser processing is a widely used contactless machining technique, with ultrashort pulses affording the intensity to machine almost any material. However, micro-patterning over curved surfaces can be difficult, as fixed beam shape will necessarily skewed when directed at non-orthogonal sample surface. Here, we show that this aberration compensated via closed-loop adaptive shaping, use of MEMS device (Texas Instruments Digital Micromirror device) acting an spatial light modulator create...

Analysis of fibroblasts within placenta is necessary for research into placental growth-factors, which are linked to lifelong health and chronic disease risk.2D analysis can be challenging due the variation complexity their structure.3D imaging provide important visualisation, but images produced extremely labour intensive construct because extensive manual processing required.Machine learning used automate labelling process faster 3D analysis.Here, a deep neural network trained label...

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Normand Laberge has courted his fair share of controversy during 7 years as CEO the Canadian Association Radiologists, but even he was surprised at backlash when argued in favour offshore teleradiology a solution to Canada's chronic radiologist shortage and growing wait times

To provide improvements in efficiency and the ability to respond changing external conditions, an artificial neural network (ANN) model is used characterize contents of reservoir(s) water tower(s) sufficient meet demands.Maintaining levels daily treatment quantities can be effective reduce formation disinfection byproducts (DBPs).Predictive models are developed investigate effects maximum temperatures, incoming solar radiation total precipitation (which influences demands) DBP at plant.ANNs...

High-repetition-rate femtosecond lasers enable the precise production of nanofoam from a wide range materials. Here, laser-based fabrication silicon, borosilicate glass, sodalime gallium lanthanum sulphide and lithium niobate is demonstrated, where pore size shown to depend strongly on material used, such that width nanofibre appear increase with density thermal expansion coefficient material. In addition, patterning glass slide, fabricated pattern pixel resolution ~35 μm, demonstrated.

We demonstrate the application of deep learning for identification particles, directly from their backscattered light. The particles were illuminated using a single-mode fibre-coupled laser light source and scattered was collected by 30-core optical fibre. technique enabled specific species pollen grains with an accuracy ~97%, even in presence high levels background equivalent to daytime sunlight. In addition, determined distance between fibre tip ± 6 µm.

Predictive visualisation for laser-processing of materials can be challenging, as the nonlinear interaction light and matter is complicated to model, particularly when scaling up from atom-level bulk material. Here, we demonstrate a predictive approach that uses pair neural networks (NNs) are trained using data obtained laser machining digital micromirror device (DMD) acting an intensity spatial modulator. The DMD enables many beam shapes, hence used produce significant amounts training NNs....

Materials processing using femtosecond laser pulses offers the potential for high-precision manufacturing. However, due to associated nonlinear processes, even small levels of experimental noise (e.g. instability in power, or unexpected debris) can result substantial deviations from desired machined structures. There is therefore much interest development closed-loop feedback processes. Recent advances algorithms behind neural networks, and particular convolutional networks (CNNs) have led...