The machinability tests were conducted by using various process parameters on a CA6164 lathe with a dynamometer. The metallurgical properties, machinability and mechanical properties of the developed alloy were compar...The machinability tests were conducted by using various process parameters on a CA6164 lathe with a dynamometer. The metallurgical properties, machinability and mechanical properties of the developed alloy were compared with those of an austenite stainless steel 1Cr18Ni9Ti. The results show that the machinability of the austenitic stainless steels with free cutting additives is much better than that of 1Cr18Ni9Ti. This is attributed to the existence of machinable additives. The inclusions might be composed of MnS. Sulfur and copper addition contributes to the improvement of the machinability of austenitic stainless steel. Bismuth is an important factor to improve the machinability of austenitic stainless steel, and it has a distinct advantage over lead. The mechanical properties of the free cutting austenitic stainless steel are similar to those of 1Cr18Ni9Ti. A new Pb-free austenitic stainless steel with high machinability as well as satisfactory mechanical properties has been developed.展开更多
Ti2AlC and Ti3AlC2 are the most light-weight and oxidation resistant layered ternary carbides belonging to the MAX phases.This review highlights recent achievements on the processing,microstructure,physical,mechanical...Ti2AlC and Ti3AlC2 are the most light-weight and oxidation resistant layered ternary carbides belonging to the MAX phases.This review highlights recent achievements on the processing,microstructure,physical,mechanical and chemical properties of these two machinable and electrically conductive carbides.Ti2AlC and Ti3AlC2 display superior properties such as fracture toughness,electrical and thermal conductivities,and oxidation resistance over their binary counterpart.This paper provides a comprehensive overview of the processing-microstructure-property correlations of these two carbides.Potential fields of applications for Ti2AlC and Ti3AlC2 are surveyed.In addition,we point out methods for further improving their properties in some specific applications through appropriate structural design and modification.展开更多
Tailored surface textures at the micro- or nanoscale dimensions are widely used to get required functional performances. Rotary ultrasonic texturing （RUT） technique has been proved to be capable of fabricating perio...Tailored surface textures at the micro- or nanoscale dimensions are widely used to get required functional performances. Rotary ultrasonic texturing （RUT） technique has been proved to be capable of fabricating periodic micro- and nanostructures. In the present study, diamond tools with geometrically defined cutting edges were designed for fabricating different types of tailored surface textures using the RUT method. Surface generation mechanisms and machinable structures of the RUT process are analyzed and simulated with a 3D-CAD program. Textured surfaces generated by using a triangular pyramid cutting tip are constructed. Different textural patterns from several micrometers to several tens of micrometers with few burrs were successfully fabricated, which proved that tools with a proper two-rake-face design are capable of removing cutting chips efficiently along a sinusoidal cutting locus in the RUT process. Technical applications of the textured surfaces are also discussed. Wetting properties of textured aluminum surfaces were evaluated by combining the test of surface roughness features. The results show that the real surface area of the textured aluminum surfaces almost doubled by comparing with that of a flat surface, and anisotropic wetting properties were obtained due to the obvious directional textural features.展开更多
For pulsed power devices, surface flashover phenomena across solid insulators greatly restrict their overall performance. In recent decades, much attention has been paid on enhancing the surface electric withstanding ...For pulsed power devices, surface flashover phenomena across solid insulators greatly restrict their overall performance. In recent decades, much attention has been paid on enhancing the surface electric withstanding strength of insulators, and it is found that surface treatment of material is useful to improve the surface flashover voltage. The carburization treatment is employed to modify the surface components of newly-developed machinable ceramics （MC） materials. A series of MC samples with different glucose solution concentration （0%, 10%, 20%, 30% and 40%） are prepared by chemical reactions for surface carburization modification, and their surface fiashover characteristics are investigated under pulsed voltage in vacuum. It is found that the surface carburization treatment greatly modifies the surface resistivity of MCs and hence the flashover behaviors. Based on the reduction of surface resistivity and the secondary electron emission avalanche （SEEA） theory, the adjustment of flashover withstanding ability can be reasonably explained.展开更多
A new type of machinable bioactive glass-ceramics for bone substitution has been developed in the glass system SiO_2-MgO-K_2O-F^--CaO-P_2O_5, which contains Mg- muscovite [K_2Mg_5 (Si_8O_(20)) F_4] and fluorapatite as...A new type of machinable bioactive glass-ceramics for bone substitution has been developed in the glass system SiO_2-MgO-K_2O-F^--CaO-P_2O_5, which contains Mg- muscovite [K_2Mg_5 (Si_8O_(20)) F_4] and fluorapatite as the two main crystal phases. The phase separation and the crystallization of the glass have been studied. A series of tests have showed that the material is good at mechanical property and bioactivity. Espe- cially, by analysing the structure of the interface layer between the material and the bone of animal with scanning electron microscope, electron probe, etc., it has been found that the new bone hydroxya- patite is formed on the surface of the material so that the material is connected firmly with the bone.展开更多
The present paper discusses the development of the first and second order model for predicting the chemical etching variables, namely, etching rate, surface roughness and accuracy of advanced ceramics. The first and s...The present paper discusses the development of the first and second order model for predicting the chemical etching variables, namely, etching rate, surface roughness and accuracy of advanced ceramics. The first and second order etching rate, surface roughness and accuracy equations were developed using the Response Surface Method (RSM). The etching variables included etching temperature, etching duration, solution and solution concentration. The predictive models’ analyses were supported with the aid of the statistical software package – Design Expert (DE 7). The effects of the individual etching variables and interaction between these variables were also investigated. The study showed that predictive models successfully predicted the etching rate, surface roughness and accuracy readings recorded experimentally with 95% confident interval. The results obtained from the predictive models were also compared with Multilayer Perceptron Artificial Neural Network (ANN). Chemical Etching variables predictive by ANN were in good agreement with those with those obtained by RSM. This observation indicated the potential of ANN in predicting chemical etching variables thus eliminating the need for exhaustive chemical etching in optimization.展开更多
A machinable Y TZP/LaPO 4 composite ceramic was prepared by infiltrating LaPO 4 liquid precursor into Y TZP porous ceramic. Sintered Y TZP ceramic preformed with 35% (volume fraction) open pore volume was made by...A machinable Y TZP/LaPO 4 composite ceramic was prepared by infiltrating LaPO 4 liquid precursor into Y TZP porous ceramic. Sintered Y TZP ceramic preformed with 35% (volume fraction) open pore volume was made by adding graphite (30%, volume fraction). The Y TZP/LaPO 4 composite ceramics containing different LaPO 4 contents were obtained by infiltration and pyrolysis cycles. The machinability and mechanical properties of materials were investigated. The results show that the machinable Y TZP/LaPO 4 composite ceramics containing 2 3% to 7.5% (volume fraction) LaPO 4 has good machinability as well as outstanding mechanical properties.展开更多
The purpose of this research was to prepare machinable bioactive glass-ceramics by sol-gel method. A multi-component composite sol with great uniformity and stability was first prepared by a 2-step method. The compos...The purpose of this research was to prepare machinable bioactive glass-ceramics by sol-gel method. A multi-component composite sol with great uniformity and stability was first prepared by a 2-step method. The composite sol was then transformed into gel by aging under different temperatures. The gel was dried finally by super critically drying method and sintered to obtain the machinable bioactive glass-ceramics. Effect of thermal treatment on crystallization of the glass-ceramics was investigated by X-ray diffraction （ XRD ） analysis. Microstructure of the glass- ceramics was observed by Scanning Electron Microscopy （SEM） and the mechanism of machinability was discussed. Phlogopite and hydroxylapatite were identified as main crystal phases by XRD analysis under thermal treatment at 750℃ and 950℃ for 1.5 h separately. The relative bulk density could achieve 99% under 1050℃ for 4 h. Microstructure of the glass-ceramics showed that the randomly distributed phlogopite and hydroxylapatite phases were favorable to the machinability of the glass-ceramics. A mean bending strength of about 160- 180 MPa and a fracture toughness parameter KIC of aboat 2.1-2.3 were determined for the glass-ceramics.展开更多
2024年日本机床展览会(JIMTOF2024)于2024年11月5~10日在东京Tokyo Big Sight举办,展出面积118540平方米。展会以“技术传承提供无限可能(Technologies passed down to the future offer unlimited possibilities)”为主题。
With the rapid development of artificial intelligence,the Internet of Things(IoT)can deploy various machine learning algorithms for network and application management.In the IoT environment,many sensors and devices ge...With the rapid development of artificial intelligence,the Internet of Things(IoT)can deploy various machine learning algorithms for network and application management.In the IoT environment,many sensors and devices generatemassive data,but data security and privacy protection have become a serious challenge.Federated learning(FL)can achieve many intelligent IoT applications by training models on local devices and allowing AI training on distributed IoT devices without data sharing.This review aims to deeply explore the combination of FL and the IoT,and analyze the application of federated learning in the IoT from the aspects of security and privacy protection.In this paper,we first describe the potential advantages of FL and the challenges faced by current IoT systems in the fields of network burden and privacy security.Next,we focus on exploring and analyzing the advantages of the combination of FL on the Internet,including privacy security,attack detection,efficient communication of the IoT,and enhanced learning quality.We also list various application scenarios of FL on the IoT.Finally,we propose several open research challenges and possible solutions.展开更多
To fully leverage the advantages of mechanization and informatization in tunnel boring machine(TBM)operations,the authors aim to promote the advancement of tunnel construction technology toward intelligent development...To fully leverage the advantages of mechanization and informatization in tunnel boring machine(TBM)operations,the authors aim to promote the advancement of tunnel construction technology toward intelligent development.This involved exploring the deep integration of next-generation artificial intelligence technologies,such as sensing technology,automatic control technology,big data technology,deep learning,and machine vision,with key operational processes,including TBM excavation,direction adjustment,step changes,inverted arch block assembly,material transportation,and operation status assurance.The results of this integration are summarized as follows.(1)TBM key excavation parameter prediction algorithm was developed with an accuracy rate exceeding 90%.The TBM intelligent step-change control algorithm,based on machine vision,achieved an image segmentation accuracy rate of 95%and gripper shoe positioning error of±5 mm.(2)An automatic positioning system for inverted arch blocks was developed,enabling real-time perception of the spatial position and deviation during the assembly process.The system maintains an elevation positioning deviation within±3 mm and a horizontal positioning deviation within±10 mm,reducing the number of surveyors in each work team.(3)A TBM intelligent rail transportation system that achieves real-time human-machine positioning,automatic switch opening and closing,automatic obstacle avoidance,intelligent transportation planning,and integrated scheduling and command was designed.Each locomotive formation reduces one shunter and improves comprehensive transportation efficiency by more than 20%.(4)Intelligent analysis and prediction algorithms were developed to monitor and predict the trends of the hydraulic and gear oil parameters in real time,enhancing the proactive maintenance and system reliability.展开更多
Accurately estimating the State of Health(SOH)and Remaining Useful Life(RUL)of lithium-ion batteries(LIBs)is crucial for the continuous and stable operation of battery management systems.However,due to the complex int...Accurately estimating the State of Health(SOH)and Remaining Useful Life(RUL)of lithium-ion batteries(LIBs)is crucial for the continuous and stable operation of battery management systems.However,due to the complex internal chemical systems of LIBs and the nonlinear degradation of their performance,direct measurement of SOH and RUL is challenging.To address these issues,the Twin Support Vector Machine(TWSVM)method is proposed to predict SOH and RUL.Initially,the constant current charging time of the lithium battery is extracted as a health indicator(HI),decomposed using Variational Modal Decomposition(VMD),and feature correlations are computed using Importance of Random Forest Features(RF)to maximize the extraction of critical factors influencing battery performance degradation.Furthermore,to enhance the global search capability of the Convolution Optimization Algorithm(COA),improvements are made using Good Point Set theory and the Differential Evolution method.The Improved Convolution Optimization Algorithm(ICOA)is employed to optimize TWSVM parameters for constructing SOH and RUL prediction models.Finally,the proposed models are validated using NASA and CALCE lithium-ion battery datasets.Experimental results demonstrate that the proposed models achieve an RMSE not exceeding 0.007 and an MAPE not exceeding 0.0082 for SOH and RUL prediction,with a relative error in RUL prediction within the range of[-1.8%,2%].Compared to other models,the proposed model not only exhibits superior fitting capability but also demonstrates robust performance.展开更多
Urban tree species provide various essential ecosystem services in cities,such as regulating urban temperatures,reducing noise,capturing carbon,and mitigating the urban heat island effect.The quality of these services...Urban tree species provide various essential ecosystem services in cities,such as regulating urban temperatures,reducing noise,capturing carbon,and mitigating the urban heat island effect.The quality of these services is influenced by species diversity,tree health,and the distribution and the composition of trees.Traditionally,data on urban trees has been collected through field surveys and manual interpretation of remote sensing images.In this study,we evaluated the effectiveness of multispectral airborne laser scanning(ALS)data in classifying 24 common urban roadside tree species in Espoo,Finland.Tree crown structure information,intensity features,and spectral data were used for classification.Eight different machine learning algorithms were tested,with the extra trees(ET)algorithm performing the best,achieving an overall accuracy of 71.7%using multispectral LiDAR data.This result highlights that integrating structural and spectral information within a single framework can improve the classification accuracy.Future research will focus on identifying the most important features for species classification and developing algorithms with greater efficiency and accuracy.展开更多
Permanent-magnet(PM)machines are the important driving components of various mechanical equipment and industrial applications,such as robot joints,aerospace equipment,electric vehicles,actuators,wind generators and el...Permanent-magnet(PM)machines are the important driving components of various mechanical equipment and industrial applications,such as robot joints,aerospace equipment,electric vehicles,actuators,wind generators and electric traction systems.The PM machines are usually expected to have high torque/power density,low torque ripple,reduced rotor mass,a large constant power speed range or strong anti-magnetization capability to match different requirements of industrial applications.The structural topology of the electric machines,including stator/rotor arrangements and magnet patterns of rotor,is one major concern to improve their electromagnetic performance.However,systematic reviews of structural topology are seldom found in literature.Therefore,the objective of this paper is to summarize the stator/rotor arrangements and magnet patterns of the permanent-magnet brushless machines,in depth.Specifically,the stator/rotor arrangements of the PM machines including radial-flux,axialflux and emerging hybrid axial-radial flux configurations are presented,and pros and cons of these topologies are discussed regarding their electromagnetic performance.The magnet patterns including various surface-mounted and interior magnet patterns,such as parallel magnetization pole pattern,Halbach arrays,spoke-type designs and their variants are summarized,and the characteristics of those magnet patterns in terms of flux-focusing effect,magnetic self-shielding effect,torque ripple,reluctance torque,magnet utilization ratio,and anti-demagnetization capability are compared.This paper can provide guidance and suggestion for the structure selection and design of PM brushless machines for high-performance industrial applications.展开更多
The remarkable ability of titanium alloys to preserve their superior physical and chemical characteristics when subjected to extreme conditions significantly enhances their importance in the aerospace,military,and med...The remarkable ability of titanium alloys to preserve their superior physical and chemical characteristics when subjected to extreme conditions significantly enhances their importance in the aerospace,military,and medical sectors.However,conventional machining of titanium alloys leads to elevated tool wear,development of surface defects,and reduced machining efficiency due to their low heat conductivity,and chemical affinity.These issues can be somewhat counteracted by integrating ultrasonic vibration in the conventional machining of titanium alloys and also enhance sustainability.This review article offers a holistic evaluation of the influence of ultrasonic vibration-assisted milling and turning on cutting forces,temperature,tool wear,and surface integrity,encompassing surface morphology,surface roughness,surface residual stress,surface hardness,and surface tribological properties during titanium alloys machining.Furthermore,it investigates the sustainability aspect that has not been previously examined.Studies on the performance of ultrasonic-assisted cutting revealed several advantages,including decreased cutting forces and cutting temperature,improved tool life,and a better-machined surface during machining.Consequently,the sustainability factor is improved due to minimized energy consumption and residual waste.In conclusion,the key challenges and future prospects in the ultrasonic-assisted cutting of titanium alloys are also discussed.This review article provides beneficial knowledge for manufactur-ers and researchers regarding ultrasonic vibration-assisted cutting of titanium alloy and will play an important role in achieving sustainability in the industry.展开更多
Photonic platforms are gradually emerging as a promising option to encounter the ever-growing demand for artificial intelligence,among which photonic time-delay reservoir computing(TDRC)is widely anticipated.While suc...Photonic platforms are gradually emerging as a promising option to encounter the ever-growing demand for artificial intelligence,among which photonic time-delay reservoir computing(TDRC)is widely anticipated.While such a computing paradigm can only employ a single photonic device as the nonlinear node for data processing,the performance highly relies on the fading memory provided by the delay feedback loop(FL),which sets a restriction on the extensibility of physical implementation,especially for highly integrated chips.Here,we present a simplified photonic scheme for more flexible parameter configurations leveraging the designed quasi-convolution coding(QC),which completely gets rid of the dependence on FL.Unlike delay-based TDRC,encoded data in QC-based RC(QRC)enables temporal feature extraction,facilitating augmented memory capabilities.Thus,our proposed QRC is enabled to deal with time-related tasks or sequential data without the implementation of FL.Furthermore,we can implement this hardware with a low-power,easily integrable vertical-cavity surface-emitting laser for high-performance parallel processing.We illustrate the concept validation through simulation and experimental comparison of QRC and TDRC,wherein the simpler-structured QRC outperforms across various benchmark tasks.Our results may underscore an auspicious solution for the hardware implementation of deep neural networks.展开更多
Multimodal sensor fusion can make full use of the advantages of various sensors,make up for the shortcomings of a single sensor,achieve information verification or information security through information redundancy,a...Multimodal sensor fusion can make full use of the advantages of various sensors,make up for the shortcomings of a single sensor,achieve information verification or information security through information redundancy,and improve the reliability and safety of the system.Artificial intelligence(AI),referring to the simulation of human intelligence in machines that are programmed to think and learn like humans,represents a pivotal frontier in modern scientific research.With the continuous development and promotion of AI technology in Sensor 4.0 age,multimodal sensor fusion is becoming more and more intelligent and automated,and is expected to go further in the future.With this context,this review article takes a comprehensive look at the recent progress on AI-enhanced multimodal sensors and their integrated devices and systems.Based on the concept and principle of sensor technologies and AI algorithms,the theoretical underpinnings,technological breakthroughs,and pragmatic applications of AI-enhanced multimodal sensors in various fields such as robotics,healthcare,and environmental monitoring are highlighted.Through a comparative study of the dual/tri-modal sensors with and without using AI technologies(especially machine learning and deep learning),AI-enhanced multimodal sensors highlight the potential of AI to improve sensor performance,data processing,and decision-making capabilities.Furthermore,the review analyzes the challenges and opportunities afforded by AI-enhanced multimodal sensors,and offers a prospective outlook on the forthcoming advancements.展开更多
Rock fragmentation is an important indicator for assessing the quality of blasting operations.However,accurate prediction of rock fragmentation after blasting is challenging due to the complicated blasting parameters ...Rock fragmentation is an important indicator for assessing the quality of blasting operations.However,accurate prediction of rock fragmentation after blasting is challenging due to the complicated blasting parameters and rock properties.For this reason,optimized by the Bayesian optimization algorithm(BOA),four hybrid machine learning models,including random forest,adaptive boosting,gradient boosting,and extremely randomized trees,were developed in this study.A total of 102 data sets with seven input parameters(spacing-to-burden ratio,hole depth-to-burden ratio,burden-to-hole diameter ratio,stemming length-to-burden ratio,powder factor,in situ block size,and elastic modulus)and one output parameter(rock fragment mean size,X_(50))were adopted to train and validate the predictive models.The root mean square error(RMSE),the mean absolute error(MAE),and the coefficient of determination(R^(2))were used as the evaluation metrics.The evaluation results demonstrated that the hybrid models showed superior performance than the standalone models.The hybrid model consisting of gradient boosting and BOA(GBoost-BOA)achieved the best prediction results compared with the other hybrid models,with the highest R^(2)value of 0.96 and the smallest values of RMSE and MAE of 0.03 and 0.02,respectively.Furthermore,sensitivity analysis was carried out to study the effects of input variables on rock fragmentation.In situ block size(XB),elastic modulus(E),and stemming length-to-burden ratio(T/B)were set as the main influencing factors.The proposed hybrid model provided a reliable prediction result and thus could be considered an alternative approach for rock fragment prediction in mining engineering.展开更多
Since antiquity,humans have been involved in designing materials through alloying strategies to meet the ever-growing technological demands.In 2004,this endeavor witnessed a significant breakthrough with the discovery...Since antiquity,humans have been involved in designing materials through alloying strategies to meet the ever-growing technological demands.In 2004,this endeavor witnessed a significant breakthrough with the discovery of high-entropy alloys(HEAs)comprising multi-principal elements.Owing to the four“core-effects”,these alloys exhibit exceptional properties including better structural stability,high strength and ductility,improved fatigue/fracture toughness,high corrosion and oxidation resistance,superconductiv-ity,magnetic properties,and good thermal properties.Different synthesis routes have been designed and used to meet the properties of interest for particular applications with varying dimensions.How-ever,HEAs are providing new opportunities and challenges for computational modelling of the complex structure-property correlations and in predictions of phase stability necessary for optimum performance of the alloy.Several attempts have been made to understand these alloys by empirical and computa-tional models,and data-driven approaches to accelerate the materials discovery with a desired set of properties.The present review discusses advances and inferences from simulations and models spanning multiple length and time scales explaining a comprehensive set of structure-properties relations.Addi-tionally,the role of machine learning approaches is also reviewed,underscoring the transformative role of computational modelling in unravelling the multifaceted properties and applications of HEAs,and the scope for future efforts in this direction.展开更多
基金Item Sponsored by National Natural Science Foundation of China (50334010)
文摘The machinability tests were conducted by using various process parameters on a CA6164 lathe with a dynamometer. The metallurgical properties, machinability and mechanical properties of the developed alloy were compared with those of an austenite stainless steel 1Cr18Ni9Ti. The results show that the machinability of the austenitic stainless steels with free cutting additives is much better than that of 1Cr18Ni9Ti. This is attributed to the existence of machinable additives. The inclusions might be composed of MnS. Sulfur and copper addition contributes to the improvement of the machinability of austenitic stainless steel. Bismuth is an important factor to improve the machinability of austenitic stainless steel, and it has a distinct advantage over lead. The mechanical properties of the free cutting austenitic stainless steel are similar to those of 1Cr18Ni9Ti. A new Pb-free austenitic stainless steel with high machinability as well as satisfactory mechanical properties has been developed.
基金funded by the National Natural Science Foundation of China (NSFC) under Grant Nos. 50802097,50832008the IMR Innovative Research Foundation
文摘Ti2AlC and Ti3AlC2 are the most light-weight and oxidation resistant layered ternary carbides belonging to the MAX phases.This review highlights recent achievements on the processing,microstructure,physical,mechanical and chemical properties of these two machinable and electrically conductive carbides.Ti2AlC and Ti3AlC2 display superior properties such as fracture toughness,electrical and thermal conductivities,and oxidation resistance over their binary counterpart.This paper provides a comprehensive overview of the processing-microstructure-property correlations of these two carbides.Potential fields of applications for Ti2AlC and Ti3AlC2 are surveyed.In addition,we point out methods for further improving their properties in some specific applications through appropriate structural design and modification.
基金Supported by Japan Society for the Promotion of Science(Grant Nos.14J04115,16K17990)
文摘Tailored surface textures at the micro- or nanoscale dimensions are widely used to get required functional performances. Rotary ultrasonic texturing （RUT） technique has been proved to be capable of fabricating periodic micro- and nanostructures. In the present study, diamond tools with geometrically defined cutting edges were designed for fabricating different types of tailored surface textures using the RUT method. Surface generation mechanisms and machinable structures of the RUT process are analyzed and simulated with a 3D-CAD program. Textured surfaces generated by using a triangular pyramid cutting tip are constructed. Different textural patterns from several micrometers to several tens of micrometers with few burrs were successfully fabricated, which proved that tools with a proper two-rake-face design are capable of removing cutting chips efficiently along a sinusoidal cutting locus in the RUT process. Technical applications of the textured surfaces are also discussed. Wetting properties of textured aluminum surfaces were evaluated by combining the test of surface roughness features. The results show that the real surface area of the textured aluminum surfaces almost doubled by comparing with that of a flat surface, and anisotropic wetting properties were obtained due to the obvious directional textural features.
基金supported in part by National Natural Science Foundation of China(Nos.50937004,50777051)NSFC-JSPS Joint Project(50911140103)
文摘For pulsed power devices, surface flashover phenomena across solid insulators greatly restrict their overall performance. In recent decades, much attention has been paid on enhancing the surface electric withstanding strength of insulators, and it is found that surface treatment of material is useful to improve the surface flashover voltage. The carburization treatment is employed to modify the surface components of newly-developed machinable ceramics （MC） materials. A series of MC samples with different glucose solution concentration （0%, 10%, 20%, 30% and 40%） are prepared by chemical reactions for surface carburization modification, and their surface fiashover characteristics are investigated under pulsed voltage in vacuum. It is found that the surface carburization treatment greatly modifies the surface resistivity of MCs and hence the flashover behaviors. Based on the reduction of surface resistivity and the secondary electron emission avalanche （SEEA） theory, the adjustment of flashover withstanding ability can be reasonably explained.
文摘A new type of machinable bioactive glass-ceramics for bone substitution has been developed in the glass system SiO_2-MgO-K_2O-F^--CaO-P_2O_5, which contains Mg- muscovite [K_2Mg_5 (Si_8O_(20)) F_4] and fluorapatite as the two main crystal phases. The phase separation and the crystallization of the glass have been studied. A series of tests have showed that the material is good at mechanical property and bioactivity. Espe- cially, by analysing the structure of the interface layer between the material and the bone of animal with scanning electron microscope, electron probe, etc., it has been found that the new bone hydroxya- patite is formed on the surface of the material so that the material is connected firmly with the bone.
文摘The present paper discusses the development of the first and second order model for predicting the chemical etching variables, namely, etching rate, surface roughness and accuracy of advanced ceramics. The first and second order etching rate, surface roughness and accuracy equations were developed using the Response Surface Method (RSM). The etching variables included etching temperature, etching duration, solution and solution concentration. The predictive models’ analyses were supported with the aid of the statistical software package – Design Expert (DE 7). The effects of the individual etching variables and interaction between these variables were also investigated. The study showed that predictive models successfully predicted the etching rate, surface roughness and accuracy readings recorded experimentally with 95% confident interval. The results obtained from the predictive models were also compared with Multilayer Perceptron Artificial Neural Network (ANN). Chemical Etching variables predictive by ANN were in good agreement with those with those obtained by RSM. This observation indicated the potential of ANN in predicting chemical etching variables thus eliminating the need for exhaustive chemical etching in optimization.
文摘A machinable Y TZP/LaPO 4 composite ceramic was prepared by infiltrating LaPO 4 liquid precursor into Y TZP porous ceramic. Sintered Y TZP ceramic preformed with 35% (volume fraction) open pore volume was made by adding graphite (30%, volume fraction). The Y TZP/LaPO 4 composite ceramics containing different LaPO 4 contents were obtained by infiltration and pyrolysis cycles. The machinability and mechanical properties of materials were investigated. The results show that the machinable Y TZP/LaPO 4 composite ceramics containing 2 3% to 7.5% (volume fraction) LaPO 4 has good machinability as well as outstanding mechanical properties.
文摘The purpose of this research was to prepare machinable bioactive glass-ceramics by sol-gel method. A multi-component composite sol with great uniformity and stability was first prepared by a 2-step method. The composite sol was then transformed into gel by aging under different temperatures. The gel was dried finally by super critically drying method and sintered to obtain the machinable bioactive glass-ceramics. Effect of thermal treatment on crystallization of the glass-ceramics was investigated by X-ray diffraction （ XRD ） analysis. Microstructure of the glass- ceramics was observed by Scanning Electron Microscopy （SEM） and the mechanism of machinability was discussed. Phlogopite and hydroxylapatite were identified as main crystal phases by XRD analysis under thermal treatment at 750℃ and 950℃ for 1.5 h separately. The relative bulk density could achieve 99% under 1050℃ for 4 h. Microstructure of the glass-ceramics showed that the randomly distributed phlogopite and hydroxylapatite phases were favorable to the machinability of the glass-ceramics. A mean bending strength of about 160- 180 MPa and a fracture toughness parameter KIC of aboat 2.1-2.3 were determined for the glass-ceramics.
文摘2024年日本机床展览会(JIMTOF2024)于2024年11月5~10日在东京Tokyo Big Sight举办,展出面积118540平方米。展会以“技术传承提供无限可能(Technologies passed down to the future offer unlimited possibilities)”为主题。
基金supported by the Shandong Province Science and Technology Project(2023TSGC0509,2022TSGC2234)Qingdao Science and Technology Plan Project(23-1-5-yqpy-2-qy)Open Topic Grants of Anhui Province Key Laboratory of Intelligent Building&Building Energy Saving,Anhui Jianzhu University(IBES2024KF08).
文摘With the rapid development of artificial intelligence,the Internet of Things(IoT)can deploy various machine learning algorithms for network and application management.In the IoT environment,many sensors and devices generatemassive data,but data security and privacy protection have become a serious challenge.Federated learning(FL)can achieve many intelligent IoT applications by training models on local devices and allowing AI training on distributed IoT devices without data sharing.This review aims to deeply explore the combination of FL and the IoT,and analyze the application of federated learning in the IoT from the aspects of security and privacy protection.In this paper,we first describe the potential advantages of FL and the challenges faced by current IoT systems in the fields of network burden and privacy security.Next,we focus on exploring and analyzing the advantages of the combination of FL on the Internet,including privacy security,attack detection,efficient communication of the IoT,and enhanced learning quality.We also list various application scenarios of FL on the IoT.Finally,we propose several open research challenges and possible solutions.
文摘To fully leverage the advantages of mechanization and informatization in tunnel boring machine(TBM)operations,the authors aim to promote the advancement of tunnel construction technology toward intelligent development.This involved exploring the deep integration of next-generation artificial intelligence technologies,such as sensing technology,automatic control technology,big data technology,deep learning,and machine vision,with key operational processes,including TBM excavation,direction adjustment,step changes,inverted arch block assembly,material transportation,and operation status assurance.The results of this integration are summarized as follows.(1)TBM key excavation parameter prediction algorithm was developed with an accuracy rate exceeding 90%.The TBM intelligent step-change control algorithm,based on machine vision,achieved an image segmentation accuracy rate of 95%and gripper shoe positioning error of±5 mm.(2)An automatic positioning system for inverted arch blocks was developed,enabling real-time perception of the spatial position and deviation during the assembly process.The system maintains an elevation positioning deviation within±3 mm and a horizontal positioning deviation within±10 mm,reducing the number of surveyors in each work team.(3)A TBM intelligent rail transportation system that achieves real-time human-machine positioning,automatic switch opening and closing,automatic obstacle avoidance,intelligent transportation planning,and integrated scheduling and command was designed.Each locomotive formation reduces one shunter and improves comprehensive transportation efficiency by more than 20%.(4)Intelligent analysis and prediction algorithms were developed to monitor and predict the trends of the hydraulic and gear oil parameters in real time,enhancing the proactive maintenance and system reliability.
基金funded by the Pyramid Talent Training Project of Beijing University of Civil Engineering and Architecture under Grant GJZJ20220802。
文摘Accurately estimating the State of Health(SOH)and Remaining Useful Life(RUL)of lithium-ion batteries(LIBs)is crucial for the continuous and stable operation of battery management systems.However,due to the complex internal chemical systems of LIBs and the nonlinear degradation of their performance,direct measurement of SOH and RUL is challenging.To address these issues,the Twin Support Vector Machine(TWSVM)method is proposed to predict SOH and RUL.Initially,the constant current charging time of the lithium battery is extracted as a health indicator(HI),decomposed using Variational Modal Decomposition(VMD),and feature correlations are computed using Importance of Random Forest Features(RF)to maximize the extraction of critical factors influencing battery performance degradation.Furthermore,to enhance the global search capability of the Convolution Optimization Algorithm(COA),improvements are made using Good Point Set theory and the Differential Evolution method.The Improved Convolution Optimization Algorithm(ICOA)is employed to optimize TWSVM parameters for constructing SOH and RUL prediction models.Finally,the proposed models are validated using NASA and CALCE lithium-ion battery datasets.Experimental results demonstrate that the proposed models achieve an RMSE not exceeding 0.007 and an MAPE not exceeding 0.0082 for SOH and RUL prediction,with a relative error in RUL prediction within the range of[-1.8%,2%].Compared to other models,the proposed model not only exhibits superior fitting capability but also demonstrates robust performance.
文摘Urban tree species provide various essential ecosystem services in cities,such as regulating urban temperatures,reducing noise,capturing carbon,and mitigating the urban heat island effect.The quality of these services is influenced by species diversity,tree health,and the distribution and the composition of trees.Traditionally,data on urban trees has been collected through field surveys and manual interpretation of remote sensing images.In this study,we evaluated the effectiveness of multispectral airborne laser scanning(ALS)data in classifying 24 common urban roadside tree species in Espoo,Finland.Tree crown structure information,intensity features,and spectral data were used for classification.Eight different machine learning algorithms were tested,with the extra trees(ET)algorithm performing the best,achieving an overall accuracy of 71.7%using multispectral LiDAR data.This result highlights that integrating structural and spectral information within a single framework can improve the classification accuracy.Future research will focus on identifying the most important features for species classification and developing algorithms with greater efficiency and accuracy.
基金Supported by National Natural Science Foundation of China(NSFC)(Grant No.52130505)Zhejiang Provincial Natural Science Foundation of China(Grant No.LD24E050005)+1 种基金Ningbo Key Scientific and Technological Project of China(Grant No.2022Z040)Academic Excellence Foundation of BUAA for PhD Students.
文摘Permanent-magnet(PM)machines are the important driving components of various mechanical equipment and industrial applications,such as robot joints,aerospace equipment,electric vehicles,actuators,wind generators and electric traction systems.The PM machines are usually expected to have high torque/power density,low torque ripple,reduced rotor mass,a large constant power speed range or strong anti-magnetization capability to match different requirements of industrial applications.The structural topology of the electric machines,including stator/rotor arrangements and magnet patterns of rotor,is one major concern to improve their electromagnetic performance.However,systematic reviews of structural topology are seldom found in literature.Therefore,the objective of this paper is to summarize the stator/rotor arrangements and magnet patterns of the permanent-magnet brushless machines,in depth.Specifically,the stator/rotor arrangements of the PM machines including radial-flux,axialflux and emerging hybrid axial-radial flux configurations are presented,and pros and cons of these topologies are discussed regarding their electromagnetic performance.The magnet patterns including various surface-mounted and interior magnet patterns,such as parallel magnetization pole pattern,Halbach arrays,spoke-type designs and their variants are summarized,and the characteristics of those magnet patterns in terms of flux-focusing effect,magnetic self-shielding effect,torque ripple,reluctance torque,magnet utilization ratio,and anti-demagnetization capability are compared.This paper can provide guidance and suggestion for the structure selection and design of PM brushless machines for high-performance industrial applications.
基金financially supported by the National Natural Science Foundation of China(Nos.92160301,92060203,52175415 and 52205475)the Science Center for Gas Turbine Project(No.P2023-B-IV-003-001)+1 种基金the Natural Science Foundation of Jiangsu Province(No.BK20210295)the Huaqiao University Engineering Research Center of Brittle Materials Machining(MOE,2023IME-001)。
文摘The remarkable ability of titanium alloys to preserve their superior physical and chemical characteristics when subjected to extreme conditions significantly enhances their importance in the aerospace,military,and medical sectors.However,conventional machining of titanium alloys leads to elevated tool wear,development of surface defects,and reduced machining efficiency due to their low heat conductivity,and chemical affinity.These issues can be somewhat counteracted by integrating ultrasonic vibration in the conventional machining of titanium alloys and also enhance sustainability.This review article offers a holistic evaluation of the influence of ultrasonic vibration-assisted milling and turning on cutting forces,temperature,tool wear,and surface integrity,encompassing surface morphology,surface roughness,surface residual stress,surface hardness,and surface tribological properties during titanium alloys machining.Furthermore,it investigates the sustainability aspect that has not been previously examined.Studies on the performance of ultrasonic-assisted cutting revealed several advantages,including decreased cutting forces and cutting temperature,improved tool life,and a better-machined surface during machining.Consequently,the sustainability factor is improved due to minimized energy consumption and residual waste.In conclusion,the key challenges and future prospects in the ultrasonic-assisted cutting of titanium alloys are also discussed.This review article provides beneficial knowledge for manufactur-ers and researchers regarding ultrasonic vibration-assisted cutting of titanium alloy and will play an important role in achieving sustainability in the industry.
基金National Natural Science Foundation of China(62171305,62405206,62004135,62001317,62111530301)Natural Science Foundation of Jiangsu Province(BK20240778,BK20241917)+3 种基金State Key Laboratory of Advanced Optical Communication Systems and Networks,China(2023GZKF08)China Postdoctoral Science Foundation(2024M752314)Postdoctoral Fellowship Program of CPSF(GZC20231883)Innovative and Entrepreneurial Talent Program of Jiangsu Province(JSSCRC2021527).
文摘Photonic platforms are gradually emerging as a promising option to encounter the ever-growing demand for artificial intelligence,among which photonic time-delay reservoir computing(TDRC)is widely anticipated.While such a computing paradigm can only employ a single photonic device as the nonlinear node for data processing,the performance highly relies on the fading memory provided by the delay feedback loop(FL),which sets a restriction on the extensibility of physical implementation,especially for highly integrated chips.Here,we present a simplified photonic scheme for more flexible parameter configurations leveraging the designed quasi-convolution coding(QC),which completely gets rid of the dependence on FL.Unlike delay-based TDRC,encoded data in QC-based RC(QRC)enables temporal feature extraction,facilitating augmented memory capabilities.Thus,our proposed QRC is enabled to deal with time-related tasks or sequential data without the implementation of FL.Furthermore,we can implement this hardware with a low-power,easily integrable vertical-cavity surface-emitting laser for high-performance parallel processing.We illustrate the concept validation through simulation and experimental comparison of QRC and TDRC,wherein the simpler-structured QRC outperforms across various benchmark tasks.Our results may underscore an auspicious solution for the hardware implementation of deep neural networks.
基金supported by the National Natural Science Foundation of China(No.62404111)Natural Science Foundation of Jiangsu Province(No.BK20240635)+2 种基金Natural Science Foundation of the Jiangsu Higher Education Institutions of China(No.24KJB510025)Natural Science Research Start-up Foundation of Recruiting Talents of Nanjing University of Posts and Telecommunications(No.NY223157 and NY223156)Opening Project of Advanced Inte-grated Circuit Package and Testing Research Center of Jiangsu Province(No.NTIKFJJ202303).
文摘Multimodal sensor fusion can make full use of the advantages of various sensors,make up for the shortcomings of a single sensor,achieve information verification or information security through information redundancy,and improve the reliability and safety of the system.Artificial intelligence(AI),referring to the simulation of human intelligence in machines that are programmed to think and learn like humans,represents a pivotal frontier in modern scientific research.With the continuous development and promotion of AI technology in Sensor 4.0 age,multimodal sensor fusion is becoming more and more intelligent and automated,and is expected to go further in the future.With this context,this review article takes a comprehensive look at the recent progress on AI-enhanced multimodal sensors and their integrated devices and systems.Based on the concept and principle of sensor technologies and AI algorithms,the theoretical underpinnings,technological breakthroughs,and pragmatic applications of AI-enhanced multimodal sensors in various fields such as robotics,healthcare,and environmental monitoring are highlighted.Through a comparative study of the dual/tri-modal sensors with and without using AI technologies(especially machine learning and deep learning),AI-enhanced multimodal sensors highlight the potential of AI to improve sensor performance,data processing,and decision-making capabilities.Furthermore,the review analyzes the challenges and opportunities afforded by AI-enhanced multimodal sensors,and offers a prospective outlook on the forthcoming advancements.
基金National Natural Science Foundation of China,Grant/Award Number:52374153。
文摘Rock fragmentation is an important indicator for assessing the quality of blasting operations.However,accurate prediction of rock fragmentation after blasting is challenging due to the complicated blasting parameters and rock properties.For this reason,optimized by the Bayesian optimization algorithm(BOA),four hybrid machine learning models,including random forest,adaptive boosting,gradient boosting,and extremely randomized trees,were developed in this study.A total of 102 data sets with seven input parameters(spacing-to-burden ratio,hole depth-to-burden ratio,burden-to-hole diameter ratio,stemming length-to-burden ratio,powder factor,in situ block size,and elastic modulus)and one output parameter(rock fragment mean size,X_(50))were adopted to train and validate the predictive models.The root mean square error(RMSE),the mean absolute error(MAE),and the coefficient of determination(R^(2))were used as the evaluation metrics.The evaluation results demonstrated that the hybrid models showed superior performance than the standalone models.The hybrid model consisting of gradient boosting and BOA(GBoost-BOA)achieved the best prediction results compared with the other hybrid models,with the highest R^(2)value of 0.96 and the smallest values of RMSE and MAE of 0.03 and 0.02,respectively.Furthermore,sensitivity analysis was carried out to study the effects of input variables on rock fragmentation.In situ block size(XB),elastic modulus(E),and stemming length-to-burden ratio(T/B)were set as the main influencing factors.The proposed hybrid model provided a reliable prediction result and thus could be considered an alternative approach for rock fragment prediction in mining engineering.
基金the Science and Engineering Re-search Board(SERB),India for providing the financial assistance to support this work(Project No.SRG/2020/002449).
文摘Since antiquity,humans have been involved in designing materials through alloying strategies to meet the ever-growing technological demands.In 2004,this endeavor witnessed a significant breakthrough with the discovery of high-entropy alloys(HEAs)comprising multi-principal elements.Owing to the four“core-effects”,these alloys exhibit exceptional properties including better structural stability,high strength and ductility,improved fatigue/fracture toughness,high corrosion and oxidation resistance,superconductiv-ity,magnetic properties,and good thermal properties.Different synthesis routes have been designed and used to meet the properties of interest for particular applications with varying dimensions.How-ever,HEAs are providing new opportunities and challenges for computational modelling of the complex structure-property correlations and in predictions of phase stability necessary for optimum performance of the alloy.Several attempts have been made to understand these alloys by empirical and computa-tional models,and data-driven approaches to accelerate the materials discovery with a desired set of properties.The present review discusses advances and inferences from simulations and models spanning multiple length and time scales explaining a comprehensive set of structure-properties relations.Addi-tionally,the role of machine learning approaches is also reviewed,underscoring the transformative role of computational modelling in unravelling the multifaceted properties and applications of HEAs,and the scope for future efforts in this direction.
