Search

• A powder-scale high precision multi-physics model is developed to depict the DED-LB process. • The single-track Inconel 718 alloy DED-LB experiments are used to validate the powder scale model and GPR model results. • The physically based-data driven agent model is developed to rapidly and accurately predict the geometry and dilution rate. • The DED-LB PS-ML model for optimal process maps is developed to achieve process parameter optimization using a comparatively small accurate data. The major challenge faced is the definition of optimal process variables for rich quality of fabricated parts in laser-based directed energy deposition (DED-LB) processes. However, predicting the optimal process scheme using machine learning models is still challenging owing to the need for a large amount of training experimental data with high costs in DED-LB. In view of this, a physical simulation (PS)-machine learning (ML) model using a relatively small accurate data set of 31 simulation data for optimal process schemes is proposed in DED-LB process in this study. Firstly, a powder-scale high precision phenomenon model incorporating the mass transfer, phase transformations and heat transfer and using Lagrangian particle model to add mass is developed to depict the DED-LB process. Then, a Gaussian process regression (GPR) agent model is established to rapidly and accurately predict the geometry and dilution rate of deposition tracks under different manufacturing parameters based on the high precision simulation results. Finally, a PS-ML model for process parameter optimization is developed using the particle swarm algorithm (PSO), and the optimized parameters are experimentally validated. The results show that the developed powder-scale model and GPR model results are consistent with the Inconel 718 alloy experimental results. The proposed PS-ML model can increase the accuracy of the ML models even with a small simulation data set. None of PS-ML model optimization results has a relative error of more than 3% to the experimental results, and the dilution rate is reduced by up to 61.66% compared to the experimental design parameters without optimization. The proposed physical simulation-machine learning model in this study enables inexpensive and accurate optimization of DED-LB process parameters. [ABSTRACT FROM AUTHOR]

• The transmittance of the hydrogel in visible light band is not much different from that of the human cornea. • The mechanical stiffness of the hydrogel can be enhanced after two-photon CXL. • The hydrogel's microstructure offers insights into the reasons about improvement in the mechanical stiffness. • The hydrogel materials may prove a good candidate for artificial cornea implants. Corneal disease seriously threatens the quality of human vision and even causes blindness, but the shortage of corneal donors is insufficient to meet the demand for corneal transplantation. Artificial corneas prepared by hydrogel materials have the characteristics of high-water content, good biocompatibility and low cytotoxicity, is an effective way to treat corneal diseases. Although hydrogel technology has advanced over the past few years, their application often suffers from limitations in mechanical properties and complicated fabrication procedures. Here, a novel high-transparent and stiffness-enhanced bio-adhesive hydrogel composed of porcine type I collagen is developed by near infrared femtosecond laser based two-photon collagen crosslinking (CXL). Transmission spectra show that the transparency of the prepared hydrogel in the visible band is not less than 90 %, and the mechanical stiffness can be enhanced by 265.27 % ± 27.08 % without significantly reducing transparency after two-photon CXL. Additionally, the hydrogel's microstructure offers insights into the reasons contributing to the observed improvement in the mechanical stiffness. Overall, this work presents a generalizable preparation strategy for hydrogel materials with high light transmission, mechanical strength, and also potential of biocompatibility and bioadhesion in terms of biocompatibility, that might be an excellent candidate for artificial cornea implants and will foster potential advances in soft materials. [ABSTRACT FROM AUTHOR]

• Filament-induced aerosol fluorescence emission appears a strong angular dependence. • The backward angular distribution of the aerosol fluorescence can be attributed to Mie scattering. • The divergence angle of aerosol fluorescence radiation is affected by the particle size distribution. Backward angular distributions of the sodium fluorescence in NaCl aerosols induced by femtosecond laser filamentation in air with a distance of 10 m are measured. The recorded signals appear as strong angular dependence. Further study indicates that it could be associated with the Mie scattering of the filamentation-induced "aerosol lasing" in the forward direction. These results will benefit the remote-sensing of air pollutants by femtosecond laser filamentation-based LIDAR (light detection and ranging). [ABSTRACT FROM AUTHOR]

• Acoustic method was adopted to quantitatively measure the long-distance filamentation. • A simple physical model was presented to analyze the spatial distribution of filament. • The onset and length of filament can be conveniently obtained from the model. • The increase of the filament length can be obtained for longer pulse with more energy. The promising application of femtosecond laser filamentation in atmospheric remote sensing brings imperative demand for diagnosing and controlling the spatiotemporal dynamics of long-distance filamentation. Here, the acoustic method was adopted to quantitatively diagnose the long-distance filamentation of femtosecond pulses manipulated by energy and temporal domain. The onset and length of filament can be conveniently obtained from a simple analytic formula based on reasonable approximation and further demonstrated by the experimental results. The influence of pulse energy and initial pulse chirp under three different focal lengths (∼10 m, 20 m, and 30 m) on the filamentation in the air were studied experimentally. These findings provide a guiding significance for the optimal control of the long-distance propagation of filament, thereby laying a firm foundation for femtosecond laser-based atmospheric remote sensing. [ABSTRACT FROM AUTHOR]

• A graphene foam-based MHz ultrasonic source is excited by femtosecond laser. • Its bandwidth is 100 times larger than that by the piezoelectric transducer. • The ultrasonic source has a dipole-like pressure distribution. The performance of a photo-thermo-acoustic (PTA) ultrasonic source can be significantly improved by utilizing graphene-based materials. Graphene is an excellent PTA material owing to its wide electromagnetic absorption spectrum, low heat capacity per unit area, and high thermal conductivity. In this study, a broadband graphene-foam-based PTA ultrasonic source covering the frequency range of 50 kHz to 1.8 MHz was excited by a near-infrared femtosecond laser beam. The lower and upper frequency limits of the acoustic wave that can be detected in the experiments were determined by the responsivity of the microphone and the attenuation of the acoustic wave in air, respectively. The experimental results show that the sound pressure of this ultrasonic source was independent of the laser polarization and incident angle. The peak-to-peak magnitude of the ultrasonic wave was proportional to laser energy when the single-pulse energy of the femtosecond laser varied from 0.4 to 1.0 mJ. Therefore, the sound intensity of the ultrasonic source could be easily controlled by modulating the laser energy. The experimental results also show that the ultrasonic wave emitted from the graphene foam had a dipole-like acoustic pressure distribution, and its principal emission direction was normal to the sample surface, regardless of the laser incidence angle. This characteristic may benefit future applications in directed message transfer/acquisition and nondestructive testing/imaging. [ABSTRACT FROM AUTHOR]