A series of ablation experiments on silicon surface by femtosecond laser system of 775 nm and 150 fs duration pulses were carried out.The morphological characteristics and the associated effect in the ablation were te...A series of ablation experiments on silicon surface by femtosecond laser system of 775 nm and 150 fs duration pulses were carried out.The morphological characteristics and the associated effect in the ablation were tested by atomic force microscope(AFM),scanning electron microscope(SEM),focused ion beam(FIB),and the optic microscope.The single pulse threshold can be obtained directly.For the multiple pulses,the ablation threshold varies with the number of pulses applied to the surface due to the incubation effect.By analyzing the experimental data,the thresholds of laser fluences under various laser pulse numbers were obtained,and the relationships between ablation area and laser energy and laser pulse number were concluded.Meanwhile,the periodic ripple structure on silicon surface was found.Under the condition of certain laser power,the number of laser pulse can influence the formation of ripples.展开更多
The increased demand for superior materials has highlighted the need of investigating the mechanical properties of composites to achieve enhanced constitutive relationships.Fiber-reinforced polymer composites have eme...The increased demand for superior materials has highlighted the need of investigating the mechanical properties of composites to achieve enhanced constitutive relationships.Fiber-reinforced polymer composites have emerged as an integral part of materials development with tailored mechanical properties.However,the complexity and heterogeneity of such composites make it considerably more challenging to have precise quantification of properties and attain an optimal design of structures through experimental and computational approaches.In order to avoid the complex,cumbersome,and labor-intensive experimental and numerical modeling approaches,a machine learning(ML)model is proposed here such that it takes the microstructural image as input with a different range of Young’s modulus of carbon fibers and neat epoxy,and obtains output as visualization of the stress component S11(principal stress in the x-direction).For obtaining the training data of the ML model,a short carbon fiberfilled specimen under quasi-static tension is modeled based on 2D Representative Area Element(RAE)using finite element analysis.The composite is inclusive of short carbon fibers with an aspect ratio of 7.5that are infilled in the epoxy systems at various random orientations and positions generated using the Simple Sequential Inhibition(SSI)process.The study reveals that the pix2pix deep learning Convolutional Neural Network(CNN)model is robust enough to predict the stress fields in the composite for a given arrangement of short fibers filled in epoxy over the specified range of Young’s modulus with high accuracy.The CNN model achieves a correlation score of about 0.999 and L2 norm of less than 0.005 for a majority of the samples in the design spectrum,indicating excellent prediction capability.In this paper,we have focused on the stage-wise chronological development of the CNN model with optimized performance for predicting the full-field stress maps of the fiber-reinforced composite specimens.The development of such a robust and efficient algorithm would significantly reduce the amount of time and cost required to study and design new composite materials through the elimination of numerical inputs by direct microstructural images.展开更多
基金Supported by National High Technology Research and Development Program of China ("863"Program) (No.2006AA04Z327)National Natural Science Foundation of China (No.60372006)Program for New Century Excellent Talents in University
文摘A series of ablation experiments on silicon surface by femtosecond laser system of 775 nm and 150 fs duration pulses were carried out.The morphological characteristics and the associated effect in the ablation were tested by atomic force microscope(AFM),scanning electron microscope(SEM),focused ion beam(FIB),and the optic microscope.The single pulse threshold can be obtained directly.For the multiple pulses,the ablation threshold varies with the number of pulses applied to the surface due to the incubation effect.By analyzing the experimental data,the thresholds of laser fluences under various laser pulse numbers were obtained,and the relationships between ablation area and laser energy and laser pulse number were concluded.Meanwhile,the periodic ripple structure on silicon surface was found.Under the condition of certain laser power,the number of laser pulse can influence the formation of ripples.
基金financial support received from DST-SERBSRG/2020/000997,Indiathe initiation grant received from IIT Kanpur。
文摘The increased demand for superior materials has highlighted the need of investigating the mechanical properties of composites to achieve enhanced constitutive relationships.Fiber-reinforced polymer composites have emerged as an integral part of materials development with tailored mechanical properties.However,the complexity and heterogeneity of such composites make it considerably more challenging to have precise quantification of properties and attain an optimal design of structures through experimental and computational approaches.In order to avoid the complex,cumbersome,and labor-intensive experimental and numerical modeling approaches,a machine learning(ML)model is proposed here such that it takes the microstructural image as input with a different range of Young’s modulus of carbon fibers and neat epoxy,and obtains output as visualization of the stress component S11(principal stress in the x-direction).For obtaining the training data of the ML model,a short carbon fiberfilled specimen under quasi-static tension is modeled based on 2D Representative Area Element(RAE)using finite element analysis.The composite is inclusive of short carbon fibers with an aspect ratio of 7.5that are infilled in the epoxy systems at various random orientations and positions generated using the Simple Sequential Inhibition(SSI)process.The study reveals that the pix2pix deep learning Convolutional Neural Network(CNN)model is robust enough to predict the stress fields in the composite for a given arrangement of short fibers filled in epoxy over the specified range of Young’s modulus with high accuracy.The CNN model achieves a correlation score of about 0.999 and L2 norm of less than 0.005 for a majority of the samples in the design spectrum,indicating excellent prediction capability.In this paper,we have focused on the stage-wise chronological development of the CNN model with optimized performance for predicting the full-field stress maps of the fiber-reinforced composite specimens.The development of such a robust and efficient algorithm would significantly reduce the amount of time and cost required to study and design new composite materials through the elimination of numerical inputs by direct microstructural images.
