Applying high-speed machining technology in shop floor has many benefits, such as manufacturing more accurate parts with better surface finishes. The selection of the appropriate machining parameters plays a very impo...Applying high-speed machining technology in shop floor has many benefits, such as manufacturing more accurate parts with better surface finishes. The selection of the appropriate machining parameters plays a very important role in the implementation of high-speed machining technology. The case-based reasoning is used in the developing of high-speed machining database to overcome the shortage of available high-speed cutting parameters in machining data handbooks and shop floors. The high-speed machining database developed in this paper includes two main components: the machining database and the case-base. The machining database stores the cutting parameters, cutting tool data, work pieces and their materials data, and other relative data, while the case-base stores mainly the successfully solved cases that are problems of work pieces and their machining. The case description and case retrieval methods are described to establish the case-based reasoning high-speed machining database. With the case retrieval method, some succeeded cases similar to the new machining problem can be retrieved from the case-base. The solution of the most matched case is evaluated and modified, and then it is regarded as the proposed solution to the new machining problem. After verification, the problem and its solution are packed up into a new case, and are stored in the case-base for future applications.展开更多
The gears of new energy vehicles are required to withstand higher rotational speeds and greater loads,which puts forward higher precision essentials for gear manufacturing.However,machining process parameters can caus...The gears of new energy vehicles are required to withstand higher rotational speeds and greater loads,which puts forward higher precision essentials for gear manufacturing.However,machining process parameters can cause changes in cutting force/heat,resulting in affecting gear machining precision.Therefore,this paper studies the effect of different process parameters on gear machining precision.A multi-objective optimization model is established for the relationship between process parameters and tooth surface deviations,tooth profile deviations,and tooth lead deviations through the cutting speed,feed rate,and cutting depth of the worm wheel gear grinding machine.The response surface method(RSM)is used for experimental design,and the corresponding experimental results and optimal process parameters are obtained.Subsequently,gray relational analysis-principal component analysis(GRA-PCA),particle swarm optimization(PSO),and genetic algorithm-particle swarm optimization(GA-PSO)methods are used to analyze the experimental results and obtain different optimal process parameters.The results show that optimal process parameters obtained by the GRA-PCA,PSO,and GA-PSO methods improve the gear machining precision.Moreover,the gear machining precision obtained by GA-PSO is superior to other methods.展开更多
The influence of ramps on the transient rolling contact characteristics and damage mechanisms of switch rails remains unclear,presenting substantial challenges to the safety of railway operations.To this end,this pape...The influence of ramps on the transient rolling contact characteristics and damage mechanisms of switch rails remains unclear,presenting substantial challenges to the safety of railway operations.To this end,this paper constructs a transient rolling contact finite element model of the wheel-rail in switch under different ramps using ANSYS/LSDYNA method,and analyzes the tribology and damage characteristics when the wheel passes through the switch at a uniform speed.Our research findings reveal that the vibration induced in the switch rail during the wheel load transfer process leads to a step-like increase in the contact force.Moreover,the interaction between the wheel and the rail primarily involves slip contact,which may significantly contribute to the formation of corrugations on the switch rail.Additionally,the presence of large ramps exacerbates switch rail wear and rolling contact fatigue,resulting in a notable 13.2%increase in switch rail damage under 40‰ramp conditions compared to flat(0‰ramp)conditions.Furthermore,the large ramps can alter the direction of crack propagation,ultimately causing surface spalling of the rail.Therefore,large ramps intensify the dynamic interactions during the wheel load transfer process,further aggravating the crack and spalling damage to the switch rails.展开更多
In this study,samples obtained from 1.3343 high-speed steel punches with TiN coatings were tested.The samples were subjected to heat treatment at different cryogenic temperatures(<196℃)and durations(12,24 and 36 h...In this study,samples obtained from 1.3343 high-speed steel punches with TiN coatings were tested.The samples were subjected to heat treatment at different cryogenic temperatures(<196℃)and durations(12,24 and 36 h),followed by tempering at two different temperatures(200,500℃).For performance testing,a ball-on-disk wear test setup was utilized and a total of 6 groups of samples were examined.The effects of cryo-treatment and tempering on microstructure were revealed through microstructural analysis with scanning electron microscopy(SEM),X-ray(XRD diffraction),and Rietveld analysis.Additionally,the hardness of the punches was measured with microhardness measurements.The optimal wear resistance was observed in the 36 h deep cryo-treated and 200℃tempered samples.The characterization study indicates that by cryogenic treatment a significant portion of the retained austenite transformed into martensite and secondary carbides formed,resulting in improved wear resistance and a slight increase in hardness.展开更多
Following the fundamental characteristics of the porosity windbreak,this study suggests a new numerical investigation method for the wind field of the windbreak based on the porous medium physical model.This method ca...Following the fundamental characteristics of the porosity windbreak,this study suggests a new numerical investigation method for the wind field of the windbreak based on the porous medium physical model.This method can transform the reasonable matching problem of the porosity and windproof performance of the windbreak into a study of the relationship between the resistance coefficient of the porous medium and the aerodynamic load of the train.This study examines the influence of the hole type on the wind field behind the porosity windbreak.Then,the relationship between the resistance coefficient of the porous medium,the porosity of the windbreak,and the aerodynamic loads of the train is investigated.The results show that the porous media physical model can be used instead of the windbreak geometry to study the windbreak-train aerodynamic performance,and the process of using this method is suggested.展开更多
A high-speed train travelling from the open air into a narrow tunnel will cause the“sonic boom”at tunnel exit.When the maglev train’s speed reaches 600 km/h,the train-tunnel aerodynamic effect is intensified,so a n...A high-speed train travelling from the open air into a narrow tunnel will cause the“sonic boom”at tunnel exit.When the maglev train’s speed reaches 600 km/h,the train-tunnel aerodynamic effect is intensified,so a new mitigation method is urgently expected to be explored.This study proposed a novel asymptotic linear method(ALM)for micro pressure wave(MPW)mitigation to achieve a constant gradient of initial c ompression waves(ICWs),via a study with various open ratios on hoods.The properties of ICWs and MPWs under various open ratios of hoods were analyzed.The results show that as the open ratio increases,the MPW amplitude at the tunnel exit initially decreases before rising.At the open ratio of 2.28%,the slope of the ICW curve is linearly coincident with a supposed straight line in the ALM,which further reduces the MPW amplitude by 26.9%at 20 m and 20.0%at 50 m from the exit,as compared to the unvented hood.Therefore,the proposed method effectively mitigates MPW and quickly determines the upper limit of alleviation for the MPW amplitude at a fixed train-tunnel operation condition.All achievements provide a ne w potential measure for the adaptive design of tunnel hoods.展开更多
The majority of the projectiles used in the hypersonic penetration study are solid flat-nosed cylindrical projectiles with a diameter of less than 20 mm.This study aims to fill the gap in the experimental and analytic...The majority of the projectiles used in the hypersonic penetration study are solid flat-nosed cylindrical projectiles with a diameter of less than 20 mm.This study aims to fill the gap in the experimental and analytical study of the evolution of the nose shape of larger hollow projectiles under hypersonic penetration.In the hypersonic penetration test,eight ogive-nose AerMet100 steel projectiles with a diameter of 40 mm were launched to hit concrete targets with impact velocities that ranged from 1351 to 1877 m/s.Severe erosion of the projectiles was observed during high-speed penetration of heterogeneous targets,and apparent localized mushrooming occurred in the front nose of recovered projectiles.By examining the damage to projectiles,a linear relationship was found between the relative length reduction rate and the initial kinetic energy of projectiles in different penetration tests.Furthermore,microscopic analysis revealed the forming mechanism of the localized mushrooming phenomenon for eroding penetration,i.e.,material spall erosion abrasion mechanism,material flow and redistribution abrasion mechanism and localized radial upsetting deformation mechanism.Finally,a model of highspeed penetration that included erosion was established on the basis of a model of the evolution of the projectile nose that considers radial upsetting;the model was validated by test data from the literature and the present study.Depending upon the impact velocity,v0,the projectile nose may behave as undistorted,radially distorted or hemispherical.Due to the effects of abrasion of the projectile and enhancement of radial upsetting on the duration and amplitude of the secondary rising segment in the pulse shape of projectile deceleration,the predicted DOP had an upper limit.展开更多
The seismic damage to ancillary facilities on high-speed railway(HSR)bridges can affect the normal movement of trains.To propose the bridge deck acceleration response spectra of the typical HSR simply-supported girder...The seismic damage to ancillary facilities on high-speed railway(HSR)bridges can affect the normal movement of trains.To propose the bridge deck acceleration response spectra of the typical HSR simply-supported girder bridge for simplifying the seismic responses analysis of the facilities on bridges,the finite element models of the HSR multi-span simply-supported girder bridges with CRTSII track were established,and the numerical model was validated by tests.Besides,the effects of the span number,peak ground acceleration(PGA),pier height on the seismic acceleration and response spectra of the bridge deck were investigated.Afterward,the bridge acceleration amplification factor curves and bridge deck response spectra with different PGAs and pier heights were obtained.The formula for bridge deck acceleration amplification factor,with a 95%guarantee rate,was fitted.Moreover,the finite element models of the overhead contact lines(OCL)mounted on rigid base and bridges were established to validate the fitted formula.The results indicated that the maximum seismic acceleration response is in the midspan of the beam.The proposed formula for the bridge deck acceleration response spectra can be used to analyze the earthquake response of the OCL and other ancillary facilities on HSR simply-supported girder bridges.The bridge deck acceleration response spectra are conservative in terms of structural safety and can significantly improving the analysis efficiency.展开更多
The complex structure of the bottom of a high-speed train is an important source of train aerodynamic drag.Thus,improving the bottom structure is of great significance to reduce the aerodynamic drag of the train.In th...The complex structure of the bottom of a high-speed train is an important source of train aerodynamic drag.Thus,improving the bottom structure is of great significance to reduce the aerodynamic drag of the train.In this study,computational fluid dynamics(CFD)based on three-dimensional steady incompressible Reynolds-average Naiver-Stokes(RANS)equations and Realizable k-εturbulence model were utilized for numerical simulations.Inspired by the concept of streamlined design and the idea of bottom flow field control,this study iteratively designed the bogies in a streamlined shape and combined them with the bottom deflectors to investigate the joint drag reduction mechanism.Three models,i.e.,single-bogie model,simplified train model,and eight-car high-speed train model,were created and their aerodynamic characteristics were analyzed.The results show that the single-bogie model with streamlined design shows a noticeable drag reduction,whose power bogie and trailer bogie experience 13.92%and 7.63%drag reduction,respectively.The range of positive pressure area on the bogie is reduced.The aerodynamic drag can be further reduced to 15.01%by installing both the streamlined bogie and the deflector on the simplified train model.When the streamlined bogies and deflectors are used on the eight-car model together,the total drag reduction rate reaches 2.90%.Therefore,the proposed aerodynamic kit for the high-speed train bottom is capable to improve the flow structure around the bogie regions,reduce the bottom flow velocity,and narrow the scope of the train’s influence on the surrounding environment,achieving the appreciable reduction of aerodynamic drag.This paper can provide a new idea for the drag reduction of high-speed trains.展开更多
Sudden earthquakes pose a threat to the running safety of trains on high-speed railway bridges,and the stiffness of piers is one of the factors affecting the dynamic response of train-track-bridge system.In this paper...Sudden earthquakes pose a threat to the running safety of trains on high-speed railway bridges,and the stiffness of piers is one of the factors affecting the dynamic response of train-track-bridge system.In this paper,a experiment of a train running on a high-speed railway bridge is performed based on a dynamic experiment system,and the corresponding numerical model is established.The reliability of the numerical model is verified by experiments.Then,the experiment and numerical data are analyzed to reveal the pier height effects on the running safety of trains on bridges.The results show that when the pier height changes,the frequency of the bridge below the 30 m pier height changes greater;the increase of pier height causes the transverse fundamental frequency of the bridge close to that of the train,and the shaking angle and lateral displacement of the train are the largest for bridge with 50 m pier,which increases the risk of derailment;with the pier height increases from 8 m to 50 m,the derailment coefficient obtained by numerical simulations increases by 75% on average,and the spectral intensity obtained by experiments increases by 120% on average,two indicators exhibit logarithmic variation.展开更多
Highly efficient organic solar cells(OSCs)are normally produced using the halogenated solvents chloroform or chlorobenzene,which present challenges for scalable manufacturing due to their toxicity,narrow processing wi...Highly efficient organic solar cells(OSCs)are normally produced using the halogenated solvents chloroform or chlorobenzene,which present challenges for scalable manufacturing due to their toxicity,narrow processing window and low boiling point.Herein,we develop a novel high-speed doctor-blading technique that significantly reduces the required concentration,facilitating the use of eco-friendly,non-halogenated solvents as alternatives to chloroform or chlorobenzene.By utilizing two widely used high-boiling,non-halogenated green solvents-o-xylene(o-XY)and toluene(Tol)-in the fabrication of PM 6:L 8-BO,we achieve power conversion efficiencies(PCEs)of 18.20%and 17.36%,respectively.Additionally,a module fabricated with o-XY demonstrates a notable PCE of 16.07%.In-situ testing and morphological analysis reveal that the o-XY coating process extends the liquid-to-solid transition stage to 6 s,significantly longer than the 1.7 s observed with Tol processing.This prolonged transition phase is crucial for improving the crystallinity of the thin film,reducing defect-mediated recombination,and enhancing carrier mobility,which collectively contribute to superior PCEs.展开更多
During high-speed flight,both thermal and mechani-cal loads can degrade the electrical performance of the antenna-radome system,which can subsequently affect the performance of the guidance system.This paper presents ...During high-speed flight,both thermal and mechani-cal loads can degrade the electrical performance of the antenna-radome system,which can subsequently affect the performance of the guidance system.This paper presents a method for evalu-ating the electrical performance of the radome when subjected to thermo-mechanical-electrical(TME)coupling.The method involves establishing a TME coupling model(TME-CM)based on the TME sharing mesh model(TME-SMM)generated by the tetrahedral mesh partitioning of the radome structure.The effects of dielectric temperature drift and structural deformation on the radome’s electrical performance are also considered.Firstly,the temperature field of the radome is obtained by tran-sient thermal analysis while the deformation field of the radome is obtained by static analysis.Subsequently,the dielectric varia-tion and structural deformation of the radome are accurately incorporated into the electrical simulation model based on the TME-SMM.The three-dimensional(3D)ray tracing method with the aperture integration technique is used to calculate the radome’s electrical performance.A representative example is provided to illustrate the superiority and necessity of the pro-posed method.This is achieved by calculating and analyzing the changes in the radome’s electrical performance over time dur-ing high-speed flight.展开更多
In the realm of high-speed railway bridge engineering,managing the intricacies of the track-bridge system model(TBSM)during seismic events remains a formidable challenge.This study pioneers an innovative approach by p...In the realm of high-speed railway bridge engineering,managing the intricacies of the track-bridge system model(TBSM)during seismic events remains a formidable challenge.This study pioneers an innovative approach by presenting a simplified bridge model(SBM)optimized for both computational efficiency and precise representation,a seminal contribution to the engineering design landscape.Central to this innovation is a novel model-updating methodology that synergistically melds artificial neural networks with an augmented particle swarm optimization.The neural networks adeptly map update parameters to seismic responses,while enhancements to the particle swarm algorithm’s inertial and learning weights lead to superior SBM parameter updates.Verification via a 4-span high-speed railway bridge revealed that the optimized SBM and TBSM exhibit a highly consistent structural natural period and seismic response,with errors controlled within 7%.Additionally,the computational efficiency improved by over 100%.Leveraging the peak displacement and shear force residuals from the seismic TBSM and SBM as optimization objectives,SBM parameters are adeptly revised.Furthermore,the incorporation of elastoplastic springs at the beam ends of the simplified model effectively captures the additional mass,stiffness,and constraint effects exerted by the track system on the bridge structure.展开更多
Background Plant tissue culture has emerged as a tool for improving cotton propagation and genetics,but recalcitrance nature of cotton makes it difficult to develop in vitro regeneration.Cotton’s recalcitrance is inf...Background Plant tissue culture has emerged as a tool for improving cotton propagation and genetics,but recalcitrance nature of cotton makes it difficult to develop in vitro regeneration.Cotton’s recalcitrance is influenced by genotype,explant type,and environmental conditions.To overcome these issues,this study uses different machine learning-based predictive models by employing multiple input factors.Cotyledonary node explants of two commercial cotton cultivars(STN-468 and GSN-12)were isolated from 7–8 days old seedlings,preconditioned with 5,10,and 20 mg·L^(-1) kinetin(KIN)for 10 days.Thereafter,explants were postconditioned on full Murashige and Skoog(MS),1/2MS,1/4MS,and full MS+0.05 mg·L^(-1) KIN,cultured in growth room enlightened with red and blue light-emitting diodes(LED)combination.Statistical analysis(analysis of variance,regression analysis)was employed to assess the impact of different treatments on shoot regeneration,with artificial intelligence(AI)models used for confirming the findings.Results GSN-12 exhibited superior shoot regeneration potential compared with STN-468,with an average of 4.99 shoots per explant versus 3.97.Optimal results were achieved with 5 mg·L^(-1) KIN preconditioning,1/4MS postconditioning,and 80%red LED,with maximum of 7.75 shoot count for GSN-12 under these conditions;while STN-468 reached 6.00 shoots under the conditions of 10 mg·L^(-1) KIN preconditioning,MS with 0.05 mg·L^(-1) KIN(postconditioning)and 75.0%red LED.Rooting was successfully achieved with naphthalene acetic acid and activated charcoal.Additionally,three different powerful AI-based models,namely,extreme gradient boost(XGBoost),random forest(RF),and the artificial neural network-based multilayer perceptron(MLP)regression models validated the findings.Conclusion GSN-12 outperformed STN-468 with optimal results from 5 mg·L^(-1) KIN+1/4MS+80%red LED.Application of machine learning-based prediction models to optimize cotton tissue culture protocols for shoot regeneration is helpful to improve cotton regeneration efficiency.展开更多
Background Cotton is one of the most important commercial crops after food crops,especially in countries like India,where it’s grown extensively under rainfed conditions.Because of its usage in multiple industries,su...Background Cotton is one of the most important commercial crops after food crops,especially in countries like India,where it’s grown extensively under rainfed conditions.Because of its usage in multiple industries,such as textile,medicine,and automobile industries,it has greater commercial importance.The crop’s performance is greatly influenced by prevailing weather dynamics.As climate changes,assessing how weather changes affect crop performance is essential.Among various techniques that are available,crop models are the most effective and widely used tools for predicting yields.Results This study compares statistical and machine learning models to assess their ability to predict cotton yield across major producing districts of Karnataka,India,utilizing a long-term dataset spanning from 1990 to 2023 that includes yield and weather factors.The artificial neural networks(ANNs)performed superiorly with acceptable yield deviations ranging within±10%during both vegetative stage(F1)and mid stage(F2)for cotton.The model evaluation metrics such as root mean square error(RMSE),normalized root mean square error(nRMSE),and modelling efficiency(EF)were also within the acceptance limits in most districts.Furthermore,the tested ANN model was used to assess the importance of the dominant weather factors influencing crop yield in each district.Specifically,the use of morning relative humidity as an individual parameter and its interaction with maximum and minimum tempera-ture had a major influence on cotton yield in most of the yield predicted districts.These differences highlighted the differential interactions of weather factors in each district for cotton yield formation,highlighting individual response of each weather factor under different soils and management conditions over the major cotton growing districts of Karnataka.Conclusions Compared with statistical models,machine learning models such as ANNs proved higher efficiency in forecasting the cotton yield due to their ability to consider the interactive effects of weather factors on yield forma-tion at different growth stages.This highlights the best suitability of ANNs for yield forecasting in rainfed conditions and for the study on relative impacts of weather factors on yield.Thus,the study aims to provide valuable insights to support stakeholders in planning effective crop management strategies and formulating relevant policies.展开更多
Machine picking in cotton is an emerging practice in India,to solve the problems of labour shortages and production costs increasing.Cotton production has been declining in recent years;however,the high density planti...Machine picking in cotton is an emerging practice in India,to solve the problems of labour shortages and production costs increasing.Cotton production has been declining in recent years;however,the high density planting system(HDPS)offers a viable method to enhance productivity by increasing plant populations per unit area,optimizing resource utilization,and facilitating machine picking.Cotton is an indeterminate plant that produce excessive vegeta-tive growth in favorable soil fertility and moisture conditions,which posing challenges for efficient machine picking.To address this issue,the application of plant growth retardants(PGRs)is essential for controlling canopy architecture.PGRs reduce internode elongation,promote regulated branching,and increase plant compactness,making cotton plants better suited for machine picking.PGRs application also optimizes photosynthates distribution between veg-etative and reproductive growth,resulting in higher yields and improved fibre quality.The integration of HDPS and PGRs applications results in an optimal plant architecture for improving machine picking efficiency.However,the success of this integration is determined by some factors,including cotton variety,environmental conditions,and geographical variations.These approaches not only address yield stagnation and labour shortages but also help to establish more effective and sustainable cotton farming practices,resulting in higher cotton productivity.展开更多
A typical Whipple shield consists of double-layered plates with a certain gap.The space debris impacts the outer plate and is broken into a debris cloud(shattered,molten,vaporized)with dispersed energy and momentum,wh...A typical Whipple shield consists of double-layered plates with a certain gap.The space debris impacts the outer plate and is broken into a debris cloud(shattered,molten,vaporized)with dispersed energy and momentum,which reduces the risk of penetrating the bulkhead.In the realm of hypervelocity impact,strain rate(>10^(5)s^(-1))effects are negligible,and fluid dynamics is employed to describe the impact process.Efficient numerical tools for precisely predicting the damage degree can greatly accelerate the design and optimization of advanced protective structures.Current hypervelocity impact research primarily focuses on the interaction between projectile and front plate and the movement of debris cloud.However,the damage mechanism of debris cloud impacts on rear plates-the critical threat component-remains underexplored owing to complex multi-physics processes and prohibitive computational costs.Existing approaches,ranging from semi-empirical equations to a machine learningbased ballistic limit prediction method,are constrained to binary penetration classification.Alternatively,the uneven data from experiments and simulations caused these methods to be ineffective when the projectile has irregular shapes and complicate flight attitude.Therefore,it is urgent to develop a new damage prediction method for predicting the rear plate damage,which can help to gain a deeper understanding of the damage mechanism.In this study,a machine learning(ML)method is developed to predict the damage distribution in the rear plate.Based on the unit velocity space,the discretized information of debris cloud and rear plate damage from rare simulation cases is used as input data for training the ML models,while the generalization ability for damage distribution prediction is tested by other simulation cases with different attack angles.The results demonstrate that the training and prediction accuracies using the Random Forest(RF)algorithm significantly surpass those using Artificial Neural Networks(ANNs)and Support Vector Machine(SVM).The RF-based model effectively identifies damage features in sparsely distributed debris cloud and cumulative effect.This study establishes an expandable new dataset that accommodates additional parameters to improve the prediction accuracy.Results demonstrate the model's ability to overcome data imbalance limitations through debris cloud features,enabling rapid and accurate rear plate damage prediction across wider scenarios with minimal data requirements.展开更多
介绍了STEP-NC的概念、数据模型及其结构特点,然后通过对比MLP(Machining Line Planner)和STEP-NC数控程序对特征和操作的不同定义方法,分析了在MLP中特征及加工工艺与STEP-NC的对应关系,探讨了在MLP中实现输出STEP-NC格式的零件加工程...介绍了STEP-NC的概念、数据模型及其结构特点,然后通过对比MLP(Machining Line Planner)和STEP-NC数控程序对特征和操作的不同定义方法,分析了在MLP中特征及加工工艺与STEP-NC的对应关系,探讨了在MLP中实现输出STEP-NC格式的零件加工程序的方法。展开更多
Electrochemical machining (ECM) is one of the best al ternatives for producing complex shapes in advanced materials used in aircraft a nd aerospace industries. However, the reduction of the stray material removal co n...Electrochemical machining (ECM) is one of the best al ternatives for producing complex shapes in advanced materials used in aircraft a nd aerospace industries. However, the reduction of the stray material removal co ntinues to be major challenges for industries in addressing accuracy improvement . This study presents a method of improving machining accuracy in ECM by using a dual pole tool with a metallic bush outside the insulated coating of a cathode tool. The bush is connected with anode and so the electric field at the side gap area is substantially weakened. The modeling and simulation indicate that the p ositive bush brings down the current density at the side gap area of the machine d hole and hence reduces the stray material removal there. It has been experimen tally observed that the machining accuracy and the process stability are signifi cantly improved.展开更多
The Al 2O 3 particles reinforced aluminum matrix composite (Al 2O 3p/Al) are more and more widely used for their excellent physical and chemical properties. However, their poor machinability leads to severe tool wear ...The Al 2O 3 particles reinforced aluminum matrix composite (Al 2O 3p/Al) are more and more widely used for their excellent physical and chemical properties. However, their poor machinability leads to severe tool wear and bad machined surface. In this paper laser assisted machining is adopted in machining Al 2O 3p/Al composite and good result was obtained. The result of experiment shows in machining Al 2O 3p/Al composites the cutting force is reduced in 30%～50%, the tool wear is reduced in 20%～30% and machined surface quality is improved in laser assisted machining as compared with conventional cutting. The physical model of the cutting process is set up and explains the reason why the cutting forced are reduced. The state of the particles is the main influence of the change. When the material of cutting zone is heating by laser, the aluminum matrix becomes softer and easier in plastic deformation, which leads to the reduction of the pushing force from the tool to the machined surface. The soften aluminum matrix is more easy to be squeezed out from the machined surface, and it leads the concentration of the Al 2O 3 particles in the surface layer of machined surface. The softening effect of laser heating on aluminum matrix reduces the pushing forces of the Al 2O 3 particles on the clearance face of cutting tool, which is just the reason for the severe cutting tool wear in conventional machining of Al 2O 3p/Al composite. Because the Al 2O 3 particles were pushed in during the cutting process, the particles increased in the surface layer. Because of the difference in thermal conductivity and thermal expansion between the Al-matrix and Al 2O 3 particle, residual stress is changed in the matrix after machining due to the extrusion of the tool, deformation of the matrix and displacement of the Al 2O 3 particle in the matrix. Temperature gradient comes into the cutting zone and the work-piece surface layer, it will lead to the increase of thermal stress and misfit dislocation in the matrix. The residual stress is compressive in the laser assisted hot cutting surface, the compressive stress is nearly triple times than that in the conventional cutting surface. Some analysis on the mechanism of laser heat assisted machining of Al 2O 3p/Al composite is given in the paper too.展开更多
文摘Applying high-speed machining technology in shop floor has many benefits, such as manufacturing more accurate parts with better surface finishes. The selection of the appropriate machining parameters plays a very important role in the implementation of high-speed machining technology. The case-based reasoning is used in the developing of high-speed machining database to overcome the shortage of available high-speed cutting parameters in machining data handbooks and shop floors. The high-speed machining database developed in this paper includes two main components: the machining database and the case-base. The machining database stores the cutting parameters, cutting tool data, work pieces and their materials data, and other relative data, while the case-base stores mainly the successfully solved cases that are problems of work pieces and their machining. The case description and case retrieval methods are described to establish the case-based reasoning high-speed machining database. With the case retrieval method, some succeeded cases similar to the new machining problem can be retrieved from the case-base. The solution of the most matched case is evaluated and modified, and then it is regarded as the proposed solution to the new machining problem. After verification, the problem and its solution are packed up into a new case, and are stored in the case-base for future applications.
基金Projects(U22B2084,52275483,52075142)supported by the National Natural Science Foundation of ChinaProject(2023ZY01050)supported by the Ministry of Industry and Information Technology High Quality Development,China。
文摘The gears of new energy vehicles are required to withstand higher rotational speeds and greater loads,which puts forward higher precision essentials for gear manufacturing.However,machining process parameters can cause changes in cutting force/heat,resulting in affecting gear machining precision.Therefore,this paper studies the effect of different process parameters on gear machining precision.A multi-objective optimization model is established for the relationship between process parameters and tooth surface deviations,tooth profile deviations,and tooth lead deviations through the cutting speed,feed rate,and cutting depth of the worm wheel gear grinding machine.The response surface method(RSM)is used for experimental design,and the corresponding experimental results and optimal process parameters are obtained.Subsequently,gray relational analysis-principal component analysis(GRA-PCA),particle swarm optimization(PSO),and genetic algorithm-particle swarm optimization(GA-PSO)methods are used to analyze the experimental results and obtain different optimal process parameters.The results show that optimal process parameters obtained by the GRA-PCA,PSO,and GA-PSO methods improve the gear machining precision.Moreover,the gear machining precision obtained by GA-PSO is superior to other methods.
基金Project(2023YFB2604304)supported by the National Key R&D Program of ChinaProjects(52122810,51978586,51778542,U23A20666,52472458)supported by the National Natural Science Foundation of China+1 种基金Project(K2022G034)supported by the Technology Research and Development Program of China National Railway Group Co.Ltd.Projects(2020JDJQ0033,2023NSFSC0884)supported by Sichuan Province Science and Technology Support Program,China。
文摘The influence of ramps on the transient rolling contact characteristics and damage mechanisms of switch rails remains unclear,presenting substantial challenges to the safety of railway operations.To this end,this paper constructs a transient rolling contact finite element model of the wheel-rail in switch under different ramps using ANSYS/LSDYNA method,and analyzes the tribology and damage characteristics when the wheel passes through the switch at a uniform speed.Our research findings reveal that the vibration induced in the switch rail during the wheel load transfer process leads to a step-like increase in the contact force.Moreover,the interaction between the wheel and the rail primarily involves slip contact,which may significantly contribute to the formation of corrugations on the switch rail.Additionally,the presence of large ramps exacerbates switch rail wear and rolling contact fatigue,resulting in a notable 13.2%increase in switch rail damage under 40‰ramp conditions compared to flat(0‰ramp)conditions.Furthermore,the large ramps can alter the direction of crack propagation,ultimately causing surface spalling of the rail.Therefore,large ramps intensify the dynamic interactions during the wheel load transfer process,further aggravating the crack and spalling damage to the switch rails.
基金Project supported by the Haier GroupProject supported by the Eskisehir Osmangazi University,Türkiye。
文摘In this study,samples obtained from 1.3343 high-speed steel punches with TiN coatings were tested.The samples were subjected to heat treatment at different cryogenic temperatures(<196℃)and durations(12,24 and 36 h),followed by tempering at two different temperatures(200,500℃).For performance testing,a ball-on-disk wear test setup was utilized and a total of 6 groups of samples were examined.The effects of cryo-treatment and tempering on microstructure were revealed through microstructural analysis with scanning electron microscopy(SEM),X-ray(XRD diffraction),and Rietveld analysis.Additionally,the hardness of the punches was measured with microhardness measurements.The optimal wear resistance was observed in the 36 h deep cryo-treated and 200℃tempered samples.The characterization study indicates that by cryogenic treatment a significant portion of the retained austenite transformed into martensite and secondary carbides formed,resulting in improved wear resistance and a slight increase in hardness.
基金Projects(52302447,52388102,52372369)supported by the National Natural Science Foundation of China。
文摘Following the fundamental characteristics of the porosity windbreak,this study suggests a new numerical investigation method for the wind field of the windbreak based on the porous medium physical model.This method can transform the reasonable matching problem of the porosity and windproof performance of the windbreak into a study of the relationship between the resistance coefficient of the porous medium and the aerodynamic load of the train.This study examines the influence of the hole type on the wind field behind the porosity windbreak.Then,the relationship between the resistance coefficient of the porous medium,the porosity of the windbreak,and the aerodynamic loads of the train is investigated.The results show that the porous media physical model can be used instead of the windbreak geometry to study the windbreak-train aerodynamic performance,and the process of using this method is suggested.
基金Project(24A0006)supported by the Key Project of Scientific Research Fund of Hunan Provincial Department of Education,ChinaProject(2024JJ5430)supported by the Natural Science Foundation of Hunan Province,ChinaProjects(2024JK2045,2023RC3061)supported by the Science and Technology Innovation Program of Hunan Province,China。
文摘A high-speed train travelling from the open air into a narrow tunnel will cause the“sonic boom”at tunnel exit.When the maglev train’s speed reaches 600 km/h,the train-tunnel aerodynamic effect is intensified,so a new mitigation method is urgently expected to be explored.This study proposed a novel asymptotic linear method(ALM)for micro pressure wave(MPW)mitigation to achieve a constant gradient of initial c ompression waves(ICWs),via a study with various open ratios on hoods.The properties of ICWs and MPWs under various open ratios of hoods were analyzed.The results show that as the open ratio increases,the MPW amplitude at the tunnel exit initially decreases before rising.At the open ratio of 2.28%,the slope of the ICW curve is linearly coincident with a supposed straight line in the ALM,which further reduces the MPW amplitude by 26.9%at 20 m and 20.0%at 50 m from the exit,as compared to the unvented hood.Therefore,the proposed method effectively mitigates MPW and quickly determines the upper limit of alleviation for the MPW amplitude at a fixed train-tunnel operation condition.All achievements provide a ne w potential measure for the adaptive design of tunnel hoods.
基金the National Natural Science Foundation of China(Grant No.12102050)the Open Fund of State Key Laboratory of Explosion Science and Technology(Grant No.SKLEST-ZZ-21-18).
文摘The majority of the projectiles used in the hypersonic penetration study are solid flat-nosed cylindrical projectiles with a diameter of less than 20 mm.This study aims to fill the gap in the experimental and analytical study of the evolution of the nose shape of larger hollow projectiles under hypersonic penetration.In the hypersonic penetration test,eight ogive-nose AerMet100 steel projectiles with a diameter of 40 mm were launched to hit concrete targets with impact velocities that ranged from 1351 to 1877 m/s.Severe erosion of the projectiles was observed during high-speed penetration of heterogeneous targets,and apparent localized mushrooming occurred in the front nose of recovered projectiles.By examining the damage to projectiles,a linear relationship was found between the relative length reduction rate and the initial kinetic energy of projectiles in different penetration tests.Furthermore,microscopic analysis revealed the forming mechanism of the localized mushrooming phenomenon for eroding penetration,i.e.,material spall erosion abrasion mechanism,material flow and redistribution abrasion mechanism and localized radial upsetting deformation mechanism.Finally,a model of highspeed penetration that included erosion was established on the basis of a model of the evolution of the projectile nose that considers radial upsetting;the model was validated by test data from the literature and the present study.Depending upon the impact velocity,v0,the projectile nose may behave as undistorted,radially distorted or hemispherical.Due to the effects of abrasion of the projectile and enhancement of radial upsetting on the duration and amplitude of the secondary rising segment in the pulse shape of projectile deceleration,the predicted DOP had an upper limit.
基金Project(HNTY2022K03)supported by the Hunan Tieyuan Civil Engineering Testing Co.,Ltd.,ChinaProject(52478573)supported by the National Natural Science Foundation of China。
文摘The seismic damage to ancillary facilities on high-speed railway(HSR)bridges can affect the normal movement of trains.To propose the bridge deck acceleration response spectra of the typical HSR simply-supported girder bridge for simplifying the seismic responses analysis of the facilities on bridges,the finite element models of the HSR multi-span simply-supported girder bridges with CRTSII track were established,and the numerical model was validated by tests.Besides,the effects of the span number,peak ground acceleration(PGA),pier height on the seismic acceleration and response spectra of the bridge deck were investigated.Afterward,the bridge acceleration amplification factor curves and bridge deck response spectra with different PGAs and pier heights were obtained.The formula for bridge deck acceleration amplification factor,with a 95%guarantee rate,was fitted.Moreover,the finite element models of the overhead contact lines(OCL)mounted on rigid base and bridges were established to validate the fitted formula.The results indicated that the maximum seismic acceleration response is in the midspan of the beam.The proposed formula for the bridge deck acceleration response spectra can be used to analyze the earthquake response of the OCL and other ancillary facilities on HSR simply-supported girder bridges.The bridge deck acceleration response spectra are conservative in terms of structural safety and can significantly improving the analysis efficiency.
基金Project(2020YFA0710901)supported by the National Key Research and Development Program of ChinaProject(2023JJ30643)supported by the Natural Science Foundation of Hunan Province,China+1 种基金Project(12372204)supported by the National Natural Science Foundation of ChinaProject(2022ZZTS0725)supported by the Self-exploration and Innovation Project for Postgraduates of Central South University,China。
文摘The complex structure of the bottom of a high-speed train is an important source of train aerodynamic drag.Thus,improving the bottom structure is of great significance to reduce the aerodynamic drag of the train.In this study,computational fluid dynamics(CFD)based on three-dimensional steady incompressible Reynolds-average Naiver-Stokes(RANS)equations and Realizable k-εturbulence model were utilized for numerical simulations.Inspired by the concept of streamlined design and the idea of bottom flow field control,this study iteratively designed the bogies in a streamlined shape and combined them with the bottom deflectors to investigate the joint drag reduction mechanism.Three models,i.e.,single-bogie model,simplified train model,and eight-car high-speed train model,were created and their aerodynamic characteristics were analyzed.The results show that the single-bogie model with streamlined design shows a noticeable drag reduction,whose power bogie and trailer bogie experience 13.92%and 7.63%drag reduction,respectively.The range of positive pressure area on the bogie is reduced.The aerodynamic drag can be further reduced to 15.01%by installing both the streamlined bogie and the deflector on the simplified train model.When the streamlined bogies and deflectors are used on the eight-car model together,the total drag reduction rate reaches 2.90%.Therefore,the proposed aerodynamic kit for the high-speed train bottom is capable to improve the flow structure around the bogie regions,reduce the bottom flow velocity,and narrow the scope of the train’s influence on the surrounding environment,achieving the appreciable reduction of aerodynamic drag.This paper can provide a new idea for the drag reduction of high-speed trains.
基金Projects(52022113,52278546)supported by the National Natural Science Foundation of ChinaProject(2020EEEVL0403)supported by the China Earthquake Administration。
文摘Sudden earthquakes pose a threat to the running safety of trains on high-speed railway bridges,and the stiffness of piers is one of the factors affecting the dynamic response of train-track-bridge system.In this paper,a experiment of a train running on a high-speed railway bridge is performed based on a dynamic experiment system,and the corresponding numerical model is established.The reliability of the numerical model is verified by experiments.Then,the experiment and numerical data are analyzed to reveal the pier height effects on the running safety of trains on bridges.The results show that when the pier height changes,the frequency of the bridge below the 30 m pier height changes greater;the increase of pier height causes the transverse fundamental frequency of the bridge close to that of the train,and the shaking angle and lateral displacement of the train are the largest for bridge with 50 m pier,which increases the risk of derailment;with the pier height increases from 8 m to 50 m,the derailment coefficient obtained by numerical simulations increases by 75% on average,and the spectral intensity obtained by experiments increases by 120% on average,two indicators exhibit logarithmic variation.
基金Project(2022YFB3803300)supported by the National Key Research and Development Program of ChinaProjects(U23A20138,52173192)supported by the National Natural Science Foundation of China+1 种基金Project(GZC20233148)supported by the Postdoctoral Fellowship Program of CPSF,ChinaProject(140050043)supported by the Central South University Postdoctoral Research Funding,China。
文摘Highly efficient organic solar cells(OSCs)are normally produced using the halogenated solvents chloroform or chlorobenzene,which present challenges for scalable manufacturing due to their toxicity,narrow processing window and low boiling point.Herein,we develop a novel high-speed doctor-blading technique that significantly reduces the required concentration,facilitating the use of eco-friendly,non-halogenated solvents as alternatives to chloroform or chlorobenzene.By utilizing two widely used high-boiling,non-halogenated green solvents-o-xylene(o-XY)and toluene(Tol)-in the fabrication of PM 6:L 8-BO,we achieve power conversion efficiencies(PCEs)of 18.20%and 17.36%,respectively.Additionally,a module fabricated with o-XY demonstrates a notable PCE of 16.07%.In-situ testing and morphological analysis reveal that the o-XY coating process extends the liquid-to-solid transition stage to 6 s,significantly longer than the 1.7 s observed with Tol processing.This prolonged transition phase is crucial for improving the crystallinity of the thin film,reducing defect-mediated recombination,and enhancing carrier mobility,which collectively contribute to superior PCEs.
文摘During high-speed flight,both thermal and mechani-cal loads can degrade the electrical performance of the antenna-radome system,which can subsequently affect the performance of the guidance system.This paper presents a method for evalu-ating the electrical performance of the radome when subjected to thermo-mechanical-electrical(TME)coupling.The method involves establishing a TME coupling model(TME-CM)based on the TME sharing mesh model(TME-SMM)generated by the tetrahedral mesh partitioning of the radome structure.The effects of dielectric temperature drift and structural deformation on the radome’s electrical performance are also considered.Firstly,the temperature field of the radome is obtained by tran-sient thermal analysis while the deformation field of the radome is obtained by static analysis.Subsequently,the dielectric varia-tion and structural deformation of the radome are accurately incorporated into the electrical simulation model based on the TME-SMM.The three-dimensional(3D)ray tracing method with the aperture integration technique is used to calculate the radome’s electrical performance.A representative example is provided to illustrate the superiority and necessity of the pro-posed method.This is achieved by calculating and analyzing the changes in the radome’s electrical performance over time dur-ing high-speed flight.
基金Project(2022YFC3004304)supported by the National Key Research and Development Program of ChinaProjects(52078487,U1934207,52178180)supported by the National Natural Science Foundation of China+2 种基金Project(2022TJ-Y10)supported by the Hunan Province Science and Technology Talent Lifting Project,ChinaProject(2023QYJC006)supported by the Frontier Cross Research Project of Central South University,ChinaProject(SKL-IoTSC(UM)-2024-2026/ORP/GA08/2023)supported by the Science and Technology Development Fund and the State Key Laboratory of Internet of Things for Smart City(University of Macao),China。
文摘In the realm of high-speed railway bridge engineering,managing the intricacies of the track-bridge system model(TBSM)during seismic events remains a formidable challenge.This study pioneers an innovative approach by presenting a simplified bridge model(SBM)optimized for both computational efficiency and precise representation,a seminal contribution to the engineering design landscape.Central to this innovation is a novel model-updating methodology that synergistically melds artificial neural networks with an augmented particle swarm optimization.The neural networks adeptly map update parameters to seismic responses,while enhancements to the particle swarm algorithm’s inertial and learning weights lead to superior SBM parameter updates.Verification via a 4-span high-speed railway bridge revealed that the optimized SBM and TBSM exhibit a highly consistent structural natural period and seismic response,with errors controlled within 7%.Additionally,the computational efficiency improved by over 100%.Leveraging the peak displacement and shear force residuals from the seismic TBSM and SBM as optimization objectives,SBM parameters are adeptly revised.Furthermore,the incorporation of elastoplastic springs at the beam ends of the simplified model effectively captures the additional mass,stiffness,and constraint effects exerted by the track system on the bridge structure.
文摘Background Plant tissue culture has emerged as a tool for improving cotton propagation and genetics,but recalcitrance nature of cotton makes it difficult to develop in vitro regeneration.Cotton’s recalcitrance is influenced by genotype,explant type,and environmental conditions.To overcome these issues,this study uses different machine learning-based predictive models by employing multiple input factors.Cotyledonary node explants of two commercial cotton cultivars(STN-468 and GSN-12)were isolated from 7–8 days old seedlings,preconditioned with 5,10,and 20 mg·L^(-1) kinetin(KIN)for 10 days.Thereafter,explants were postconditioned on full Murashige and Skoog(MS),1/2MS,1/4MS,and full MS+0.05 mg·L^(-1) KIN,cultured in growth room enlightened with red and blue light-emitting diodes(LED)combination.Statistical analysis(analysis of variance,regression analysis)was employed to assess the impact of different treatments on shoot regeneration,with artificial intelligence(AI)models used for confirming the findings.Results GSN-12 exhibited superior shoot regeneration potential compared with STN-468,with an average of 4.99 shoots per explant versus 3.97.Optimal results were achieved with 5 mg·L^(-1) KIN preconditioning,1/4MS postconditioning,and 80%red LED,with maximum of 7.75 shoot count for GSN-12 under these conditions;while STN-468 reached 6.00 shoots under the conditions of 10 mg·L^(-1) KIN preconditioning,MS with 0.05 mg·L^(-1) KIN(postconditioning)and 75.0%red LED.Rooting was successfully achieved with naphthalene acetic acid and activated charcoal.Additionally,three different powerful AI-based models,namely,extreme gradient boost(XGBoost),random forest(RF),and the artificial neural network-based multilayer perceptron(MLP)regression models validated the findings.Conclusion GSN-12 outperformed STN-468 with optimal results from 5 mg·L^(-1) KIN+1/4MS+80%red LED.Application of machine learning-based prediction models to optimize cotton tissue culture protocols for shoot regeneration is helpful to improve cotton regeneration efficiency.
基金funded through India Meteorological Department,New Delhi,India under the Forecasting Agricultural output using Space,Agrometeorol ogy and Land based observations(FASAL)project and fund number:No.ASC/FASAL/KT-11/01/HQ-2010.
文摘Background Cotton is one of the most important commercial crops after food crops,especially in countries like India,where it’s grown extensively under rainfed conditions.Because of its usage in multiple industries,such as textile,medicine,and automobile industries,it has greater commercial importance.The crop’s performance is greatly influenced by prevailing weather dynamics.As climate changes,assessing how weather changes affect crop performance is essential.Among various techniques that are available,crop models are the most effective and widely used tools for predicting yields.Results This study compares statistical and machine learning models to assess their ability to predict cotton yield across major producing districts of Karnataka,India,utilizing a long-term dataset spanning from 1990 to 2023 that includes yield and weather factors.The artificial neural networks(ANNs)performed superiorly with acceptable yield deviations ranging within±10%during both vegetative stage(F1)and mid stage(F2)for cotton.The model evaluation metrics such as root mean square error(RMSE),normalized root mean square error(nRMSE),and modelling efficiency(EF)were also within the acceptance limits in most districts.Furthermore,the tested ANN model was used to assess the importance of the dominant weather factors influencing crop yield in each district.Specifically,the use of morning relative humidity as an individual parameter and its interaction with maximum and minimum tempera-ture had a major influence on cotton yield in most of the yield predicted districts.These differences highlighted the differential interactions of weather factors in each district for cotton yield formation,highlighting individual response of each weather factor under different soils and management conditions over the major cotton growing districts of Karnataka.Conclusions Compared with statistical models,machine learning models such as ANNs proved higher efficiency in forecasting the cotton yield due to their ability to consider the interactive effects of weather factors on yield forma-tion at different growth stages.This highlights the best suitability of ANNs for yield forecasting in rainfed conditions and for the study on relative impacts of weather factors on yield.Thus,the study aims to provide valuable insights to support stakeholders in planning effective crop management strategies and formulating relevant policies.
文摘Machine picking in cotton is an emerging practice in India,to solve the problems of labour shortages and production costs increasing.Cotton production has been declining in recent years;however,the high density planting system(HDPS)offers a viable method to enhance productivity by increasing plant populations per unit area,optimizing resource utilization,and facilitating machine picking.Cotton is an indeterminate plant that produce excessive vegeta-tive growth in favorable soil fertility and moisture conditions,which posing challenges for efficient machine picking.To address this issue,the application of plant growth retardants(PGRs)is essential for controlling canopy architecture.PGRs reduce internode elongation,promote regulated branching,and increase plant compactness,making cotton plants better suited for machine picking.PGRs application also optimizes photosynthates distribution between veg-etative and reproductive growth,resulting in higher yields and improved fibre quality.The integration of HDPS and PGRs applications results in an optimal plant architecture for improving machine picking efficiency.However,the success of this integration is determined by some factors,including cotton variety,environmental conditions,and geographical variations.These approaches not only address yield stagnation and labour shortages but also help to establish more effective and sustainable cotton farming practices,resulting in higher cotton productivity.
基金supported by National Natural Science Foundation of China(Grant No.12432018,12372346)the Innovative Research Groups of the National Natural Science Foundation of China(Grant No.12221002).
文摘A typical Whipple shield consists of double-layered plates with a certain gap.The space debris impacts the outer plate and is broken into a debris cloud(shattered,molten,vaporized)with dispersed energy and momentum,which reduces the risk of penetrating the bulkhead.In the realm of hypervelocity impact,strain rate(>10^(5)s^(-1))effects are negligible,and fluid dynamics is employed to describe the impact process.Efficient numerical tools for precisely predicting the damage degree can greatly accelerate the design and optimization of advanced protective structures.Current hypervelocity impact research primarily focuses on the interaction between projectile and front plate and the movement of debris cloud.However,the damage mechanism of debris cloud impacts on rear plates-the critical threat component-remains underexplored owing to complex multi-physics processes and prohibitive computational costs.Existing approaches,ranging from semi-empirical equations to a machine learningbased ballistic limit prediction method,are constrained to binary penetration classification.Alternatively,the uneven data from experiments and simulations caused these methods to be ineffective when the projectile has irregular shapes and complicate flight attitude.Therefore,it is urgent to develop a new damage prediction method for predicting the rear plate damage,which can help to gain a deeper understanding of the damage mechanism.In this study,a machine learning(ML)method is developed to predict the damage distribution in the rear plate.Based on the unit velocity space,the discretized information of debris cloud and rear plate damage from rare simulation cases is used as input data for training the ML models,while the generalization ability for damage distribution prediction is tested by other simulation cases with different attack angles.The results demonstrate that the training and prediction accuracies using the Random Forest(RF)algorithm significantly surpass those using Artificial Neural Networks(ANNs)and Support Vector Machine(SVM).The RF-based model effectively identifies damage features in sparsely distributed debris cloud and cumulative effect.This study establishes an expandable new dataset that accommodates additional parameters to improve the prediction accuracy.Results demonstrate the model's ability to overcome data imbalance limitations through debris cloud features,enabling rapid and accurate rear plate damage prediction across wider scenarios with minimal data requirements.
文摘Electrochemical machining (ECM) is one of the best al ternatives for producing complex shapes in advanced materials used in aircraft a nd aerospace industries. However, the reduction of the stray material removal co ntinues to be major challenges for industries in addressing accuracy improvement . This study presents a method of improving machining accuracy in ECM by using a dual pole tool with a metallic bush outside the insulated coating of a cathode tool. The bush is connected with anode and so the electric field at the side gap area is substantially weakened. The modeling and simulation indicate that the p ositive bush brings down the current density at the side gap area of the machine d hole and hence reduces the stray material removal there. It has been experimen tally observed that the machining accuracy and the process stability are signifi cantly improved.
文摘The Al 2O 3 particles reinforced aluminum matrix composite (Al 2O 3p/Al) are more and more widely used for their excellent physical and chemical properties. However, their poor machinability leads to severe tool wear and bad machined surface. In this paper laser assisted machining is adopted in machining Al 2O 3p/Al composite and good result was obtained. The result of experiment shows in machining Al 2O 3p/Al composites the cutting force is reduced in 30%～50%, the tool wear is reduced in 20%～30% and machined surface quality is improved in laser assisted machining as compared with conventional cutting. The physical model of the cutting process is set up and explains the reason why the cutting forced are reduced. The state of the particles is the main influence of the change. When the material of cutting zone is heating by laser, the aluminum matrix becomes softer and easier in plastic deformation, which leads to the reduction of the pushing force from the tool to the machined surface. The soften aluminum matrix is more easy to be squeezed out from the machined surface, and it leads the concentration of the Al 2O 3 particles in the surface layer of machined surface. The softening effect of laser heating on aluminum matrix reduces the pushing forces of the Al 2O 3 particles on the clearance face of cutting tool, which is just the reason for the severe cutting tool wear in conventional machining of Al 2O 3p/Al composite. Because the Al 2O 3 particles were pushed in during the cutting process, the particles increased in the surface layer. Because of the difference in thermal conductivity and thermal expansion between the Al-matrix and Al 2O 3 particle, residual stress is changed in the matrix after machining due to the extrusion of the tool, deformation of the matrix and displacement of the Al 2O 3 particle in the matrix. Temperature gradient comes into the cutting zone and the work-piece surface layer, it will lead to the increase of thermal stress and misfit dislocation in the matrix. The residual stress is compressive in the laser assisted hot cutting surface, the compressive stress is nearly triple times than that in the conventional cutting surface. Some analysis on the mechanism of laser heat assisted machining of Al 2O 3p/Al composite is given in the paper too.
