Polycrystalline materials are extensively employed in industry.Its surface roughness significantly affects the working performance.Material defects,particularly grain boundaries,have a great impact on the achieved sur...Polycrystalline materials are extensively employed in industry.Its surface roughness significantly affects the working performance.Material defects,particularly grain boundaries,have a great impact on the achieved surface roughness of polycrystalline materials.However,it is difficult to establish a purely theoretical model for surface roughness with consideration of the grain boundary effect using conventional analytical methods.In this work,a theoretical and deep learning hybrid model for predicting the surface roughness of diamond-turned polycrystalline materials is proposed.The kinematic–dynamic roughness component in relation to the tool profile duplication effect,work material plastic side flow,relative vibration between the diamond tool and workpiece,etc,is theoretically calculated.The material-defect roughness component is modeled with a cascade forward neural network.In the neural network,the ratio of maximum undeformed chip thickness to cutting edge radius RT S,work material properties(misorientation angle θ_(g) and grain size d_(g)),and spindle rotation speed n s are configured as input variables.The material-defect roughness component is set as the output variable.To validate the developed model,polycrystalline copper with a gradient distribution of grains prepared by friction stir processing is machined with various processing parameters and different diamond tools.Compared with the previously developed model,obvious improvement in the prediction accuracy is observed with this hybrid prediction model.Based on this model,the influences of different factors on the surface roughness of polycrystalline materials are discussed.The influencing mechanism of the misorientation angle and grain size is quantitatively analyzed.Two fracture modes,including transcrystalline and intercrystalline fractures at different RTS values,are observed.Meanwhile,optimal processing parameters are obtained with a simulated annealing algorithm.Cutting experiments are performed with the optimal parameters,and a flat surface finish with Sa 1.314 nm is finally achieved.The developed model and corresponding new findings in this work are beneficial for accurately predicting the surface roughness of polycrystalline materials and understanding the impacting mechanism of material defects in diamond turning.展开更多
Superhydrophobic aluminum surfaces have been prepared by means of electrodeposition of copper on aluminum surfaces, followed by electrochemical modification using stearic acid organic molecules. Scanning electron micr...Superhydrophobic aluminum surfaces have been prepared by means of electrodeposition of copper on aluminum surfaces, followed by electrochemical modification using stearic acid organic molecules. Scanning electron microscopy(SEM) images show that the electrodeposited copper films follow "island growth mode" in the form of microdots and their number densities increase with the rise of the negative deposition potentials. At an electrodeposition potential of-0.2 V the number density of the copper microdots are found to be 4.5×104cm^(-2)that are increased to 2.9×105cm^(-2)at a potential of-0.8 V. Systematically, the distances between the microdots are found to be reduced from 26.6 μm to 11.03 μm with the increase of negative electrochemical potential from-0.2 V to-0.8 V. X-ray diffraction(XRD) analyses have confirmed the formation of copper stearate on the stearic acid modified copper films. The roughness of the stearic acid modified electrodeposited copper films is found to increase with the increase in the density of the copper microdots. A critical copper deposition potential of-0.6 V in conjunction with the stearic acid modification provides a surface roughness of 6.2 μm with a water contact angle of 157?, resulting in superhydrophobic properties on the aluminum substrates.展开更多
Nanocrystalline copper films were prepared on the glass by electroless plating technique. The surface characterization of copper films with different deposition time was studied by field emission scanning electron mic...Nanocrystalline copper films were prepared on the glass by electroless plating technique. The surface characterization of copper films with different deposition time was studied by field emission scanning electron microscopy(FESEM) and atomic force microscopy(AFM). The results indicate that the copper films have a(111) texture. A continuous and smooth film forms on the glass substrate at deposition times of 5 min. The surface roughness of as-deposited copper films becomes rougher with large nodules as the deposition time increases. According to Fuchs-Sondheimer(F-S),Mayadas-Shatzkes(M-S) theory and a combined model,the grain boundary reflection coefficient(R) is calculated in the range of 0.40-0.75. The theoretical analysis based on the experimental results show that the grain boundaries contribute mainly to the increase of electrical resistivity of nanocrystalline copper film compared with the film surfaces.展开更多
基金National Natural Science Foundation of China(Nos.52175430,51935008 and 52105478)China National Postdoctoral Program for Innovative Talents(BX20200234)Open Fund of Tianjin Key Laboratory of Equipment Design and Manufacturing Technology(EDMT)for the support of this work。
文摘Polycrystalline materials are extensively employed in industry.Its surface roughness significantly affects the working performance.Material defects,particularly grain boundaries,have a great impact on the achieved surface roughness of polycrystalline materials.However,it is difficult to establish a purely theoretical model for surface roughness with consideration of the grain boundary effect using conventional analytical methods.In this work,a theoretical and deep learning hybrid model for predicting the surface roughness of diamond-turned polycrystalline materials is proposed.The kinematic–dynamic roughness component in relation to the tool profile duplication effect,work material plastic side flow,relative vibration between the diamond tool and workpiece,etc,is theoretically calculated.The material-defect roughness component is modeled with a cascade forward neural network.In the neural network,the ratio of maximum undeformed chip thickness to cutting edge radius RT S,work material properties(misorientation angle θ_(g) and grain size d_(g)),and spindle rotation speed n s are configured as input variables.The material-defect roughness component is set as the output variable.To validate the developed model,polycrystalline copper with a gradient distribution of grains prepared by friction stir processing is machined with various processing parameters and different diamond tools.Compared with the previously developed model,obvious improvement in the prediction accuracy is observed with this hybrid prediction model.Based on this model,the influences of different factors on the surface roughness of polycrystalline materials are discussed.The influencing mechanism of the misorientation angle and grain size is quantitatively analyzed.Two fracture modes,including transcrystalline and intercrystalline fractures at different RTS values,are observed.Meanwhile,optimal processing parameters are obtained with a simulated annealing algorithm.Cutting experiments are performed with the optimal parameters,and a flat surface finish with Sa 1.314 nm is finally achieved.The developed model and corresponding new findings in this work are beneficial for accurately predicting the surface roughness of polycrystalline materials and understanding the impacting mechanism of material defects in diamond turning.
基金the financial support provided by the Natural Sciences and Engineering Research Council of Canada(NSERC)
文摘Superhydrophobic aluminum surfaces have been prepared by means of electrodeposition of copper on aluminum surfaces, followed by electrochemical modification using stearic acid organic molecules. Scanning electron microscopy(SEM) images show that the electrodeposited copper films follow "island growth mode" in the form of microdots and their number densities increase with the rise of the negative deposition potentials. At an electrodeposition potential of-0.2 V the number density of the copper microdots are found to be 4.5×104cm^(-2)that are increased to 2.9×105cm^(-2)at a potential of-0.8 V. Systematically, the distances between the microdots are found to be reduced from 26.6 μm to 11.03 μm with the increase of negative electrochemical potential from-0.2 V to-0.8 V. X-ray diffraction(XRD) analyses have confirmed the formation of copper stearate on the stearic acid modified copper films. The roughness of the stearic acid modified electrodeposited copper films is found to increase with the increase in the density of the copper microdots. A critical copper deposition potential of-0.6 V in conjunction with the stearic acid modification provides a surface roughness of 6.2 μm with a water contact angle of 157?, resulting in superhydrophobic properties on the aluminum substrates.
基金Project (2004CB619301) supported by the National Basic Research and Development Program and Project 985-Automotive Engineering of Jilin University
文摘Nanocrystalline copper films were prepared on the glass by electroless plating technique. The surface characterization of copper films with different deposition time was studied by field emission scanning electron microscopy(FESEM) and atomic force microscopy(AFM). The results indicate that the copper films have a(111) texture. A continuous and smooth film forms on the glass substrate at deposition times of 5 min. The surface roughness of as-deposited copper films becomes rougher with large nodules as the deposition time increases. According to Fuchs-Sondheimer(F-S),Mayadas-Shatzkes(M-S) theory and a combined model,the grain boundary reflection coefficient(R) is calculated in the range of 0.40-0.75. The theoretical analysis based on the experimental results show that the grain boundaries contribute mainly to the increase of electrical resistivity of nanocrystalline copper film compared with the film surfaces.
