Enhanced Solar‐to‐Hydrogen Generation with Broadband Epsilon‐Near‐Zero Nanostructured Photocatalysts

The direct conversion of solar energy into fuels or feedstock is an attractive approach to address increasing demand of renewable energy sources. Photocatalytic systems relying on the direct photoexcitation of metals have been explored to this end, a strategy that exploits the decay of plasmonic resonances into hot carriers. An efficient hot carrier generation and collection requires, ideally, their generation to be enclosed within few tens of nanometers at the metal interface, but it is challenging to achieve this across the broadband solar spectrum. Here the authors demonstrate a new photocatalyst for hydrogen evolution based on metal epsilon‐near‐zero metamaterials. The authors have designed these to achieve broadband strong light confinement at the metal interface across the entire solar spectrum. Using electron energy loss spectroscopy, the authors prove that hot carriers are generated in a broadband fashion within 10 nm in this system. The resulting photocatalyst achieves a hydrogen production rate of 9.5 µmol h^?1 cm^?2 that exceeds, by a factor of 3.2, that of the best previously reported plasmonic‐based photocatalysts for the dissociation of H₂ with 50 h stable operation.     

Ensemble learning of diffractive optical networks

A plethora of research advances have emerged in the fields of optics and photonics that benefit from harnessing the power of machine learning.Specifically,there has been a revival of interest in optical computing hardware due to its potential advantages for machine learning tasks in terms of parallelization,power efficiency and computation speed.Diffractive deep neural networks(D²NNs)form such an optical computing framework that benefits from deep learning-based design of successive diffractive layers to all-optically process information as the input light diffracts through these passive layers.D²NNs have demonstrated success in various tasks,including object classification,the spectral encoding of information,optical pulse shaping and imaging.Here,we substantially improve the inference performance of diffractive optical networks using feature engineering and ensemble learning.After independently training 1252 D²NNs that were diversely engineered with a variety of passive input filters,we applied a pruning algorithm to select an optimized ensemble of D²NNs that collectively improved the image classification accuracy.Through this pruning,we numerically demonstrated that ensembles of N=14 and N=30 D²NNs achieve blind testing accuracies of 61.14±0.23%and 62.13±0.05%,respectively,on the classification of GFAR-10 test images,providing an inference improvennent of>16%compared to the average performance of the individual D²NNs within each ensemble.These results constitute the highest inference accuracies achieved to date by any diffractive optical neural network design on the same dataset and might provide a significant leap to extend the application space of diffractive optical image classification and machine vision systems.     

A data mining approach for machine fault diagnosis based on associated frequency patterns

Bearings play a crucial role in rotational machines and their failure is one of the foremost causes of breakdowns in rotary machinery. Their functionality is directly relevant to the operational performance, service life and efficiency of these machines. Therefore, bearing fault identification is very significant. The accuracy of fault or anomaly detection by the current techniques is not adequate. We propose a data mining-based framework for fault identification and anomaly detection from machine vibration data. In this framework, to capture the useful knowledge from the vibration data stream (VDS), we first pre-process the data using Fast Fourier Transform (FFT) to extract the frequency signature and then build a compact tree called SAFP-tree (sliding window associated frequency pattern tree), and propose a mining algorithm called SAFP. Our SAFP algorithm can mine associated frequency patterns (i.e., fault frequency signatures) in the current window of VDS and use them to identify faults in the bearing data. Finally, SAFP is further enhanced to SAFP-AD for anomaly detection by determining the normal behavior measure (NBM) from the extracted frequency patterns. The results show that our technique is very efficient in identifying faults and detecting anomalies over VDS and can be used for remote machine health diagnosis.     

Gene-expression programming to predict friction factor for Southern Italian rivers

This briefing article presents gene-expression programming (GEP), which is an extension to genetic programming, as an alternative approach to predict friction factor for Southern Italian rivers. Published data were compiled for the friction for 43 gravel-bed rivers of Calabria. The proposed GEP approach produces satisfactory results (R ² = 0.958 and RMSE = 0.079) compared with existing predictors.     

Tooling device design for vibration-assisted high speed shaping of PMMA

PMMA optical components that are used as one of the most important parts of high precision equipments and machines are increasingly replacing the glass due to the various advantages of PMMA. Especially in Light Guide Panels, the PMMA sheet that is used in Liquid Crystal Displays plays an important role in scattering the incident light and requires very fine machining as the sheet is directly related to the optical characteristics of the panels. The High Speed End milling and High Speed Shaping processes that are widely adopted and applied to the precise machining of Light Incident Plane still have quality problems, such as cracks, breakages, poor waviness, and straightness. This paper presents the tooling device design for machining a Light Incident Plane through vibration-assisted High Speed Shaping for increasing the optical quality by minimizing the above-mentioned problems. The cutting tool and the tool post presented in this paper are designed by the authors to increase the magnitude of the cutting stroke by adopting the resonant frequency without weakening the stiffness and to reduce vibrations during even high speed feeding. The dynamic characteristics of the cutting tool and the tool post are evaluated through simulation and experiment as well. The results reveal very appropriate dynamic characteristics for vibration-assisted High Speed Shaping.     

Three dimensional modeling and finite element simulation of a generic end mill

The geometry of cutting flutes and the surfaces of end mills is one of the crucial parameters affecting the quality of the machining in the case of end milling. These are usually represented by two-dimensional models. This paper describes in detail the methodology to model the geometry of a flat end mill in terms of three-dimensional parameters. The geometric definition of the end mill is developed in terms of surface patches; flutes as helicoidal surfaces, the shank as a surface of revolution and the blending surfaces as bicubic Bezier and biparametric sweep surfaces. The proposed model defines the end mill in terms of three-dimensional rotational angles rather than the conventional two dimensional angles. To validate the methodology, the flat end milling cutter is directly rendered in OpenGL environment in terms of three-dimensional parameters. Further, an interface is developed that directly pulls the proposed three-dimensional model defined with the help of parametric equations into a commercial CAD modeling environment. This facilitates a wide range of downstream technological applications. The modeled tool is used for finite element simulations to study the cutting flutes under static and transient dynamic load conditions. The results of stress distribution (von mises stress), translational displacement and deformation are presented for static and transient dynamic analysis for the end mill cutter flute and its body. The method described in this paper offers a simple and intuitive way of generating high-quality end mill models for use in machining process simulations. It can be easily extended to generate other tools without relying on analytical or numerical formulations.     

Laser and GTAW torch processing of Fe–Cr–B coatings on steel

A comparison has been made of the relationship between microstructure and microhardness developed by surface melting Nanosteel SHS 7170 Fe–Cr–B alloy powder onto a plain carbon steel surface. This powder was initially developed as a high velocity oxyfuel sprayed coating, giving a strength 10 times that of mild steel, and is particularly suitable for surface protection against wear and corrosion. In the present study, the alloy powder was injected into the laser melted surface, while a preplaced powder was melted using the gas tungsten arc welding (GTAW) technique. The laser track consisted of fine dendrites and needle-like microstructures, which produced a maximum hardness value of over 800 HV, while the GTAW track produced a mixture of equiaxed and columnar grain microstructures with a maximum hardness value of 670 HV. The lower hardness values are considered to be associated with dilution and grain size.