Nanofluid minimum quantity lubrication(NMQL)is a green processing technology.Cottonseed oil is suitable as base oil because of excellent lubrication performance,low freezing temperature,and high yield.Al_(2)O_(3)nanop...Nanofluid minimum quantity lubrication(NMQL)is a green processing technology.Cottonseed oil is suitable as base oil because of excellent lubrication performance,low freezing temperature,and high yield.Al_(2)O_(3)nanoparticles improve not only the heat transfer capacity but also the lubrication performance.The physical and chemical proper-ties of nanofluid change when Al_(2)O_(3)nanoparticles are added.However,the effects of the concentration of nanofluid on lubrication performance remain unknown.Furthermore,the mechanisms of interaction between Al_(2)O_(3)nanoparti-cles and cottonseed oil are unclear.In this research,nanofluid is prepared by adding different mass concentrations of Al_(2)O_(3)nanoparticles(0,0.2%,0.5%,1%,1.5%,and 2%wt)to cottonseed oil during minimum quantity lubrication(MQL)milling 45 steel.The tribological properties of nanofluid with different concentrations at the tool/workpiece interface are studied through macro-evaluation parameters(milling force,specific energy)and micro-evaluation parameters(surface roughness,micro morphology,contact angle).The result show that the specific energy is at the minimum(114 J/mm^(3)),and the roughness value is the lowest(1.63μm)when the concentration is 0.5 wt%.The surfaces of the chip and workpiece are the smoothest,and the contact angle is the lowest,indicating that the tribological proper-ties are the best under 0.5 wt%.This research investigates the intercoupling mechanisms of Al_(2)O_(3)nanoparticles and cottonseed base oil,and acquires the optimal Al_(2)O_(3)nanofluid concentration to receive satisfactory tribological properties.展开更多
Graphene has superhigh thermal conductivity up to 5000 W/(m·K),extremely thin thickness,superhigh mechanical strength and nano-lamellar structure with low interlayer shear strength,making it possess great potenti...Graphene has superhigh thermal conductivity up to 5000 W/(m·K),extremely thin thickness,superhigh mechanical strength and nano-lamellar structure with low interlayer shear strength,making it possess great potential in mini-mum quantity lubrication(MQL)grinding.Meanwhile,ionic liquids(ILs)have higher thermal conductivity and better thermal stability than vegetable oils,which are frequently used as MQL grinding fluids.And ILs have extremely low vapor pressure,thereby avoiding film boiling in grinding.These excellent properties make ILs also have immense potential in MQL grinding.However,the grinding performance of graphene and ionic liquid mixed fluid under nano-fluid minimum quantity lubrication(NMQL),and its tribological mechanism on abrasive grain/workpiece grinding interface,are still unclear.This research firstly evaluates the grinding performance of graphene and ionic liquid mixed nanofluids(graphene/IL nanofluids)under NMQL experimentally.The evaluation shows that graphene/IL nanofluids can further strengthen both the cooling and lubricating performances compared with MQL grinding using ILs only.The specific grinding energy and grinding force ratio can be reduced by over 40%at grinding depth of 10μm.Work-piece machined surface roughness can be decreased by over 10%,and grinding temperature can be lowered over 50℃at grinding depth of 30μm.Aiming at the unclear tribological mechanism of graphene/IL nanofluids,molecular dynamics simulations for abrasive grain/workpiece grinding interface are performed to explore the formation mechanism of physical adsorption film.The simulations show that the grinding interface is in a boundary lubrication state.IL molecules absorb in groove-like fractures on grain wear flat face to form boundary lubrication film,and graphene nanosheets can enter into the grinding interface to further decrease the contact area between abrasive grain and workpiece.Compared with MQL grinding,the average tangential grinding force of graphene/IL nanofluids can decrease up to 10.8%.The interlayer shear effect and low interlayer shear strength of graphene nanosheets are the principal causes of enhanced lubricating performance on the grinding interface.EDS and XPS analyses are further carried out to explore the formation mechanism of chemical reaction film.The analyses show that IL base fluid happens chemical reactions with workpiece material,producing FeF_(2),CrF_(3),and BN.The fresh machined surface of workpiece is oxidized by air,producing NiO,Cr_(2)O_(3) and Fe_(2)O_(3).The chemical reaction film is constituted by fluorides,nitrides and oxides together.The combined action of physical adsorption film and chemical reaction film make graphene/IL nano-fluids obtain excellent grinding performance.展开更多
A qualia role-based entity-dependency graph(EDG)is proposed to represent and extract quantity relations for solving algebra story problems stated in Chinese.Traditional neural solvers use end-to-end models to translat...A qualia role-based entity-dependency graph(EDG)is proposed to represent and extract quantity relations for solving algebra story problems stated in Chinese.Traditional neural solvers use end-to-end models to translate problem texts into math expressions,which lack quantity relation acquisition in sophisticated scenarios.To address the problem,the proposed method leverages EDG to represent quantity relations hidden in qualia roles of math objects.Algorithms were designed for EDG generation and quantity relation extraction for solving algebra story problems.Experimental result shows that the proposedmethod achieved an average accuracy of 82.2%on quantity relation extraction compared to 74.5%of baseline method.Another prompt learning result shows a 5%increase obtained in problem solving by injecting the extracted quantity relations into the baseline neural solvers.展开更多
针对煤炭运输过程中,经常无法保持煤炭在带式输送机上的运量均匀,使得带式输送机长时间全速运转而造成电能浪费和设备无效磨损的问题,提出一种基于语义分割的带式输送机煤料运输区域检测算法。该算法在DeeplabV3+的基础上,根据特征通道...针对煤炭运输过程中,经常无法保持煤炭在带式输送机上的运量均匀,使得带式输送机长时间全速运转而造成电能浪费和设备无效磨损的问题,提出一种基于语义分割的带式输送机煤料运输区域检测算法。该算法在DeeplabV3+的基础上,根据特征通道之间的相互依赖关系,引入注意力机制,使用不同扩张率的卷积核获得多种尺度的语义信息,来精确分割出煤炭在带式输送机的运输区域。实验结果表明,该算法平均交并比(Mean Intersection over Union,MIoU)相比于DeeplabV3+算法提高1.24百分点,能够有效精准地分割出煤料的运输区域,为煤量估计工作提供有效的保障。展开更多
基金Supported by National Natural Science Foundation of China(Grant Nos.51806112,51975305)PhD Research Startup Foundation of Qingdao University of Technology,China(Grant Nos.JC2022-012,20312008).
文摘Nanofluid minimum quantity lubrication(NMQL)is a green processing technology.Cottonseed oil is suitable as base oil because of excellent lubrication performance,low freezing temperature,and high yield.Al_(2)O_(3)nanoparticles improve not only the heat transfer capacity but also the lubrication performance.The physical and chemical proper-ties of nanofluid change when Al_(2)O_(3)nanoparticles are added.However,the effects of the concentration of nanofluid on lubrication performance remain unknown.Furthermore,the mechanisms of interaction between Al_(2)O_(3)nanoparti-cles and cottonseed oil are unclear.In this research,nanofluid is prepared by adding different mass concentrations of Al_(2)O_(3)nanoparticles(0,0.2%,0.5%,1%,1.5%,and 2%wt)to cottonseed oil during minimum quantity lubrication(MQL)milling 45 steel.The tribological properties of nanofluid with different concentrations at the tool/workpiece interface are studied through macro-evaluation parameters(milling force,specific energy)and micro-evaluation parameters(surface roughness,micro morphology,contact angle).The result show that the specific energy is at the minimum(114 J/mm^(3)),and the roughness value is the lowest(1.63μm)when the concentration is 0.5 wt%.The surfaces of the chip and workpiece are the smoothest,and the contact angle is the lowest,indicating that the tribological proper-ties are the best under 0.5 wt%.This research investigates the intercoupling mechanisms of Al_(2)O_(3)nanoparticles and cottonseed base oil,and acquires the optimal Al_(2)O_(3)nanofluid concentration to receive satisfactory tribological properties.
基金Supported by Shandong Provincial Natural Science Foundation of China(Grant Nos.ZR2022ME208,ZR2020QE181)National Natural Science Foundation of China(Grant Nos.51705272,52005281)+1 种基金China Postdoctoral Science Foundation(Grant No.2018M642628)111 project(Grant No.D21017).
文摘Graphene has superhigh thermal conductivity up to 5000 W/(m·K),extremely thin thickness,superhigh mechanical strength and nano-lamellar structure with low interlayer shear strength,making it possess great potential in mini-mum quantity lubrication(MQL)grinding.Meanwhile,ionic liquids(ILs)have higher thermal conductivity and better thermal stability than vegetable oils,which are frequently used as MQL grinding fluids.And ILs have extremely low vapor pressure,thereby avoiding film boiling in grinding.These excellent properties make ILs also have immense potential in MQL grinding.However,the grinding performance of graphene and ionic liquid mixed fluid under nano-fluid minimum quantity lubrication(NMQL),and its tribological mechanism on abrasive grain/workpiece grinding interface,are still unclear.This research firstly evaluates the grinding performance of graphene and ionic liquid mixed nanofluids(graphene/IL nanofluids)under NMQL experimentally.The evaluation shows that graphene/IL nanofluids can further strengthen both the cooling and lubricating performances compared with MQL grinding using ILs only.The specific grinding energy and grinding force ratio can be reduced by over 40%at grinding depth of 10μm.Work-piece machined surface roughness can be decreased by over 10%,and grinding temperature can be lowered over 50℃at grinding depth of 30μm.Aiming at the unclear tribological mechanism of graphene/IL nanofluids,molecular dynamics simulations for abrasive grain/workpiece grinding interface are performed to explore the formation mechanism of physical adsorption film.The simulations show that the grinding interface is in a boundary lubrication state.IL molecules absorb in groove-like fractures on grain wear flat face to form boundary lubrication film,and graphene nanosheets can enter into the grinding interface to further decrease the contact area between abrasive grain and workpiece.Compared with MQL grinding,the average tangential grinding force of graphene/IL nanofluids can decrease up to 10.8%.The interlayer shear effect and low interlayer shear strength of graphene nanosheets are the principal causes of enhanced lubricating performance on the grinding interface.EDS and XPS analyses are further carried out to explore the formation mechanism of chemical reaction film.The analyses show that IL base fluid happens chemical reactions with workpiece material,producing FeF_(2),CrF_(3),and BN.The fresh machined surface of workpiece is oxidized by air,producing NiO,Cr_(2)O_(3) and Fe_(2)O_(3).The chemical reaction film is constituted by fluorides,nitrides and oxides together.The combined action of physical adsorption film and chemical reaction film make graphene/IL nano-fluids obtain excellent grinding performance.
基金supported by the National Natural Science Foundation of China (Nos.62177024,62007014)the Humanities and Social Sciences Youth Fund of the Ministry of Education (No.20YJC880024)+1 种基金China Post Doctoral Science Foundation (No.2019M652678)the Fundamental Research Funds for the Central Universities (No.CCNU20ZT019).
文摘A qualia role-based entity-dependency graph(EDG)is proposed to represent and extract quantity relations for solving algebra story problems stated in Chinese.Traditional neural solvers use end-to-end models to translate problem texts into math expressions,which lack quantity relation acquisition in sophisticated scenarios.To address the problem,the proposed method leverages EDG to represent quantity relations hidden in qualia roles of math objects.Algorithms were designed for EDG generation and quantity relation extraction for solving algebra story problems.Experimental result shows that the proposedmethod achieved an average accuracy of 82.2%on quantity relation extraction compared to 74.5%of baseline method.Another prompt learning result shows a 5%increase obtained in problem solving by injecting the extracted quantity relations into the baseline neural solvers.
文摘针对煤炭运输过程中,经常无法保持煤炭在带式输送机上的运量均匀,使得带式输送机长时间全速运转而造成电能浪费和设备无效磨损的问题,提出一种基于语义分割的带式输送机煤料运输区域检测算法。该算法在DeeplabV3+的基础上,根据特征通道之间的相互依赖关系,引入注意力机制,使用不同扩张率的卷积核获得多种尺度的语义信息,来精确分割出煤炭在带式输送机的运输区域。实验结果表明,该算法平均交并比(Mean Intersection over Union,MIoU)相比于DeeplabV3+算法提高1.24百分点,能够有效精准地分割出煤料的运输区域,为煤量估计工作提供有效的保障。
