The effects of laser shock peening(LSP)on the microstructural evolution and mechanical properties of the Ti6242 alloy,including the residual stress,surface roughness,Vickers microhardness,tensile mechanical response,a...The effects of laser shock peening(LSP)on the microstructural evolution and mechanical properties of the Ti6242 alloy,including the residual stress,surface roughness,Vickers microhardness,tensile mechanical response,and high-cycle fatigue properties,were studied.The results showed that the LSP induced residual compressive stresses on the surface and near surface of the material.The maximum surface residual compressive stress was−661 MPa,and the compressive-stress-affected depth was greater than 1000μm.The roughness and Vickers micro-hardness increased with the number of shocks,and the maximum hardness-affected depth was about 700μm after three LSP treatments.LSP enhanced the fraction of low-angle grain boundaries,changed the grain preferred orientations,and notably increased the pole density ofαphase on the near surface from 2.41 to 3.46.The surface hardness values of the LSP samples increased with the increase of the number of shocks due to work hardening,while the LSP had a limited effect on the tensile properties.The high-cycle fatigue life of the LSP-treated sample was significantly enhanced by more than 20%compared with that of the untreated sample,which was caused by the suppression of the initiation and propagation of fatigue cracks.展开更多
Sparse-representation-based single-channel source separation,which aims to recover each source’s signal using its corresponding sub-dictionary,has attracted many scholars’attention.The basic premise of this model is...Sparse-representation-based single-channel source separation,which aims to recover each source’s signal using its corresponding sub-dictionary,has attracted many scholars’attention.The basic premise of this model is that each sub-dictionary possesses discriminative information about its corresponding source,and this information can be used to recover almost every sample from that source.However,in a more general sense,the samples from a source are composed not only of discriminative information but also common information shared with other sources.This paper proposes learning a discriminative high-fidelity dictionary to improve the separation performance.The innovations are threefold.Firstly,an extra sub-dictionary was combined into a conventional union dictionary to ensure that the source-specific sub-dictionaries can capture only the purely discriminative information for their corresponding sources because the common information is collected in the additional sub-dictionary.Secondly,a task-driven learning algorithm is designed to optimize the new union dictionary and a set of weights that indicate how much of the common information should be allocated to each source.Thirdly,a source separation scheme based on the learned dictionary is presented.Experimental results on a human speech dataset yield evidence that our algorithm can achieve better separation performance than either state-of-the-art or traditional algorithms.展开更多
The unmanned combat aerial vehicle(UCAV)is a research hot issue in the world,and the situation assessment is an important part of it.To overcome shortcomings of the existing situation assessment methods,such as low ac...The unmanned combat aerial vehicle(UCAV)is a research hot issue in the world,and the situation assessment is an important part of it.To overcome shortcomings of the existing situation assessment methods,such as low accuracy and strong dependence on prior knowledge,a datadriven situation assessment method is proposed.The clustering and classification are combined,the former is used to mine situational knowledge,and the latter is used to realize rapid assessment.Angle evaluation factor and distance evaluation factor are proposed to transform multi-dimensional air combat information into two-dimensional features.A convolution success-history based adaptive differential evolution with linear population size reduc-tion-means(C-LSHADE-Means)algorithm is proposed.The convolutional pooling layer is used to compress the size of data and preserve the distribution characteristics.The LSHADE algorithm is used to initialize the center of the mean clustering,which over-comes the defect of initialization sensitivity.Comparing experi-ment with the seven clustering algorithms is done on the UCI data set,through four clustering indexes,and it proves that the method proposed in this paper has better clustering performance.A situation assessment model based on stacked autoen-coder and learning vector quantization(SAE-LVQ)network is constructed,and it uses SAE to reconstruct air combat data fea-tures,and uses the self-competition layer of the LVQ to achieve efficient classification.Compared with the five kinds of assess-ments models,the SAE-LVQ model has the highest accuracy.Finally,three kinds of confrontation processes from air combat maneuvering instrumentation(ACMI)are selected,and the model in this paper is used for situation assessment.The assessment results are in line with the actual situation.展开更多
Titanium alloys have a wide application in aerospace industries as it has greater strength and low density, but it has poor tribological properties. To improve its friction and wear performance, in present work, a fem...Titanium alloys have a wide application in aerospace industries as it has greater strength and low density, but it has poor tribological properties. To improve its friction and wear performance, in present work, a femtosecond laser is used to directly irradiate the Ti6Al4V titanium alloy surface in air conditioning, which results in localized ablation and the formation of periodic microstructures but also a strong pressure wave, propagating the material inside. Through the optimization of processing parameters, surface modification and periodic micropatterning with effective anti-friction properties were successfully induced on the surface. After a treatment of femtosecond laser-induced surface modification(FsLSM), the surface microhardness was improved by 16.6% and compressive residual stress reached-746 MPa. Besides, laser-induced periodic surface structures(LIPSS) with a titanium oxide outer coating were fabricated uniformly on the titanium alloy surface. Rotary ball-on-disk wear experiments revealed that the average coefficient of friction(COF) and wear mass loss of the specimen with Fs LSM treatment were largely reduced by 68.9% and 90% as compared to that of untreated specimens, respectively. It was analyzed that the reason for the remarkable wear resistance was attributed to the comprehensive action of the generation of LIPSS, the titanium oxide outer coating, high amplitude compressive residual stress and gradient grain size distribution on the subsurface during the laser surface treatment. Since the findings here are broadly applicable to a wide spectrum of engineering metals and alloys, the present results offer unique pathways to enhancing the tribological performance of materials.展开更多
基金the National Natural Science Foundation of China(No.52205240).
文摘The effects of laser shock peening(LSP)on the microstructural evolution and mechanical properties of the Ti6242 alloy,including the residual stress,surface roughness,Vickers microhardness,tensile mechanical response,and high-cycle fatigue properties,were studied.The results showed that the LSP induced residual compressive stresses on the surface and near surface of the material.The maximum surface residual compressive stress was−661 MPa,and the compressive-stress-affected depth was greater than 1000μm.The roughness and Vickers micro-hardness increased with the number of shocks,and the maximum hardness-affected depth was about 700μm after three LSP treatments.LSP enhanced the fraction of low-angle grain boundaries,changed the grain preferred orientations,and notably increased the pole density ofαphase on the near surface from 2.41 to 3.46.The surface hardness values of the LSP samples increased with the increase of the number of shocks due to work hardening,while the LSP had a limited effect on the tensile properties.The high-cycle fatigue life of the LSP-treated sample was significantly enhanced by more than 20%compared with that of the untreated sample,which was caused by the suppression of the initiation and propagation of fatigue cracks.
基金This work was supported by the National Natural Science Foundation of China(62001489)the scientific research planning project of National University of Defense Technology(JS19-04).
文摘Sparse-representation-based single-channel source separation,which aims to recover each source’s signal using its corresponding sub-dictionary,has attracted many scholars’attention.The basic premise of this model is that each sub-dictionary possesses discriminative information about its corresponding source,and this information can be used to recover almost every sample from that source.However,in a more general sense,the samples from a source are composed not only of discriminative information but also common information shared with other sources.This paper proposes learning a discriminative high-fidelity dictionary to improve the separation performance.The innovations are threefold.Firstly,an extra sub-dictionary was combined into a conventional union dictionary to ensure that the source-specific sub-dictionaries can capture only the purely discriminative information for their corresponding sources because the common information is collected in the additional sub-dictionary.Secondly,a task-driven learning algorithm is designed to optimize the new union dictionary and a set of weights that indicate how much of the common information should be allocated to each source.Thirdly,a source separation scheme based on the learned dictionary is presented.Experimental results on a human speech dataset yield evidence that our algorithm can achieve better separation performance than either state-of-the-art or traditional algorithms.
基金supported by the Natural Science Foundation of Shaanxi Province(2020JQ-481,2021JM-224)the Aeronautical Science Foundation of China(201951096002).
文摘The unmanned combat aerial vehicle(UCAV)is a research hot issue in the world,and the situation assessment is an important part of it.To overcome shortcomings of the existing situation assessment methods,such as low accuracy and strong dependence on prior knowledge,a datadriven situation assessment method is proposed.The clustering and classification are combined,the former is used to mine situational knowledge,and the latter is used to realize rapid assessment.Angle evaluation factor and distance evaluation factor are proposed to transform multi-dimensional air combat information into two-dimensional features.A convolution success-history based adaptive differential evolution with linear population size reduc-tion-means(C-LSHADE-Means)algorithm is proposed.The convolutional pooling layer is used to compress the size of data and preserve the distribution characteristics.The LSHADE algorithm is used to initialize the center of the mean clustering,which over-comes the defect of initialization sensitivity.Comparing experi-ment with the seven clustering algorithms is done on the UCI data set,through four clustering indexes,and it proves that the method proposed in this paper has better clustering performance.A situation assessment model based on stacked autoen-coder and learning vector quantization(SAE-LVQ)network is constructed,and it uses SAE to reconstruct air combat data fea-tures,and uses the self-competition layer of the LVQ to achieve efficient classification.Compared with the five kinds of assess-ments models,the SAE-LVQ model has the highest accuracy.Finally,three kinds of confrontation processes from air combat maneuvering instrumentation(ACMI)are selected,and the model in this paper is used for situation assessment.The assessment results are in line with the actual situation.
基金co-supported by the Key-Area Research and Development Program of Guangdong Province(No.2018B090906002)the National Natural Science Foundation of China(No.51875574)the National Science and Technology Major Project of China(No.2017-Ⅶ-0003-0096-1)。
文摘Titanium alloys have a wide application in aerospace industries as it has greater strength and low density, but it has poor tribological properties. To improve its friction and wear performance, in present work, a femtosecond laser is used to directly irradiate the Ti6Al4V titanium alloy surface in air conditioning, which results in localized ablation and the formation of periodic microstructures but also a strong pressure wave, propagating the material inside. Through the optimization of processing parameters, surface modification and periodic micropatterning with effective anti-friction properties were successfully induced on the surface. After a treatment of femtosecond laser-induced surface modification(FsLSM), the surface microhardness was improved by 16.6% and compressive residual stress reached-746 MPa. Besides, laser-induced periodic surface structures(LIPSS) with a titanium oxide outer coating were fabricated uniformly on the titanium alloy surface. Rotary ball-on-disk wear experiments revealed that the average coefficient of friction(COF) and wear mass loss of the specimen with Fs LSM treatment were largely reduced by 68.9% and 90% as compared to that of untreated specimens, respectively. It was analyzed that the reason for the remarkable wear resistance was attributed to the comprehensive action of the generation of LIPSS, the titanium oxide outer coating, high amplitude compressive residual stress and gradient grain size distribution on the subsurface during the laser surface treatment. Since the findings here are broadly applicable to a wide spectrum of engineering metals and alloys, the present results offer unique pathways to enhancing the tribological performance of materials.
