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
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
The graded density impactor(GDI)dynamic loading technique is crucial for acquiring the dynamic physical property parameters of materials used in weapons.The accuracy and timeliness of GDI structural design are key to ...The graded density impactor(GDI)dynamic loading technique is crucial for acquiring the dynamic physical property parameters of materials used in weapons.The accuracy and timeliness of GDI structural design are key to achieving controllable stress-strain rate loading.In this study,we have,for the first time,combined one-dimensional fluid computational software with machine learning methods.We first elucidated the mechanisms by which GDI structures control stress and strain rates.Subsequently,we constructed a machine learning model to create a structure-property response surface.The results show that altering the loading velocity and interlayer thickness has a pronounced regulatory effect on stress and strain rates.In contrast,the impedance distribution index and target thickness have less significant effects on stress regulation,although there is a matching relationship between target thickness and interlayer thickness.Compared with traditional design methods,the machine learning approach offers a10^(4)—10^(5)times increase in efficiency and the potential to achieve a global optimum,holding promise for guiding the design of GDI.展开更多
3-Nitro-1,2,4-triazol-5-one(NTO)is a typical high-energy,low-sensitivity explosive,and accurate concentration monitoring is critical for crystallization process control.In this study,a high-precision quantitative anal...3-Nitro-1,2,4-triazol-5-one(NTO)is a typical high-energy,low-sensitivity explosive,and accurate concentration monitoring is critical for crystallization process control.In this study,a high-precision quantitative analytical model for NTO concentration in ethanol solutions was developed by integrating real-time ATR-FTIR spectroscopy with chemometric and machine learning techniques.Dynamic spectral data were obtained by designing multi-concentration gradient heating-cooling cycle experiments,abnormal samples were eliminated using the isolation forest algorithm,and the effects of various preprocessing methods on model performance were systematically evaluated.The results show that partial least squares regression(PLSR)exhibits superior generalization ability compared to other models.Vibrational bands corresponding to C=O and–NO_(2)were identified as key predictors for concentration estimation.This work provides an efficient and reliable solution for real-time concentration monitoring during NTO crystallization and holds significant potential for process analytical applications in energetic material manufacturing.展开更多
Background The geo-traceability of cotton is crucial for ensuring the quality and integrity of cotton brands. However, effective methods for achieving this traceability are currently lacking. This study investigates t...Background The geo-traceability of cotton is crucial for ensuring the quality and integrity of cotton brands. However, effective methods for achieving this traceability are currently lacking. This study investigates the potential of explainable machine learning for the geo-traceability of raw cotton.Results The findings indicate that principal component analysis(PCA) exhibits limited effectiveness in tracing cotton origins. In contrast, partial least squares discriminant analysis(PLS-DA) demonstrates superior classification performance, identifying seven discriminating variables: Na, Mn, Ba, Rb, Al, As, and Pb. The use of decision tree(DT), support vector machine(SVM), and random forest(RF) models for origin discrimination yielded accuracies of 90%, 87%, and 97%, respectively. Notably, the light gradient boosting machine(Light GBM) model achieved perfect performance metrics, with accuracy, precision, and recall rate all reaching 100% on the test set. The output of the Light GBM model was further evaluated using the SHapley Additive ex Planation(SHAP) technique, which highlighted differences in the elemental composition of raw cotton from various countries. Specifically, the elements Pb, Ni, Na, Al, As, Ba, and Rb significantly influenced the model's predictions.Conclusion These findings suggest that explainable machine learning techniques can provide insights into the complex relationships between geographic information and raw cotton. Consequently, these methodologies enhances the precision and reliability of geographic traceability for raw cotton.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
Blast-induced ground vibration,quantified by peak particle velocity(PPV),is a crucial factor in mitigating environmental and structural risks in mining and geotechnical engineering.Accurate PPV prediction facilitates ...Blast-induced ground vibration,quantified by peak particle velocity(PPV),is a crucial factor in mitigating environmental and structural risks in mining and geotechnical engineering.Accurate PPV prediction facilitates safer and more sustainable blasting operations by minimizing adverse impacts and ensuring regulatory compliance.This study presents an advanced predictive framework integrating Cat Boost(CB)with nature-inspired optimization algorithms,including the Bat Algorithm(BAT),Sparrow Search Algorithm(SSA),Butterfly Optimization Algorithm(BOA),and Grasshopper Optimization Algorithm(GOA).A comprehensive dataset from the Sarcheshmeh Copper Mine in Iran was utilized to develop and evaluate these models using key performance metrics such as the Index of Agreement(IoA),Nash-Sutcliffe Efficiency(NSE),and the coefficient of determination(R^(2)).The hybrid CB-BOA model outperformed other approaches,achieving the highest accuracy(R^(2)=0.989)and the lowest prediction errors.SHAP analysis identified Distance(Di)as the most influential variable affecting PPV,while uncertainty analysis confirmed CB-BOA as the most reliable model,featuring the narrowest prediction interval.These findings highlight the effectiveness of hybrid machine learning models in refining PPV predictions,contributing to improved blast design strategies,enhanced structural safety,and reduced environmental impacts in mining and geotechnical engineering.展开更多
Driven by rapid technological advancements and economic growth,mineral extraction and metal refining have increased dramatically,generating huge volumes of tailings and mine waste(TMWs).Investigating the morphological...Driven by rapid technological advancements and economic growth,mineral extraction and metal refining have increased dramatically,generating huge volumes of tailings and mine waste(TMWs).Investigating the morphological fractions of heavy metals and metalloids(HMMs)in TMWs is key to evaluating their leaching potential into the environment;however,traditional experiments are time-consuming and labor-intensive.In this study,10 machine learning(ML)algorithms were used and compared for rapidly predicting the morphological fractions of HMMs in TMWs.A dataset comprising 2376 data points was used,with mineral composition,elemental properties,and total concentration used as inputs and concentration of morphological fraction used as output.After grid search optimization,the extra tree model performed the best,achieving coefficient of determination(R2)of 0.946 and 0.942 on the validation and test sets,respectively.Electronegativity was found to have the greatest impact on the morphological fraction.The models’performance was enhanced by applying an ensemble method to the top three optimal ML models,including gradient boosting decision tree,extra trees and categorical boosting.Overall,the proposed framework can accurately predict the concentrations of different morphological fractions of HMMs in TMWs.This approach can minimize detection time,aid in the safe management and recovery of TMWs.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
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
For electro-discharge machining, only in the optimum state could the highest material removal rate be realized. In practical machining process, the timely elevation of the tool electrode is needed to eliminate chippin...For electro-discharge machining, only in the optimum state could the highest material removal rate be realized. In practical machining process, the timely elevation of the tool electrode is needed to eliminate chipping, which ordinarily occupies quite a lot of time. Therefore, besides the control of the machining parameters, the control of the optimum discharge gap and the conversion of different machining states is also needed. In this paper, the adaptive fuzzy control system of servomechanism for EDM combined with ultrasonic vibration is studied, the servomechanism of which is composed of the stepping motor comprising variable steps and the inductive synchronizer. The fuzzy control technology is used to realize the control of the frequency and the step of the servomechanism. The adaptive fuzzy controller has three inputs and two outputs, which can well meet the actual control requirements. The constitution of the fuzzy control regulation for the step frequency is the key to the design of the whole fuzzy control system of the servomechanism. The step frequency is mainly determined by the position error and the change rate of the position error. When the value of the position error is high or medium, the controlled parameters are selected to eliminate the error; when the position error is lower, the controlled parameters are selected to avoid the over-orientation and thus keep the stability of the system. According to these, a fuzzy control table is established in advanced, which is used to express the relations between the fuzzy input parameters and the fuzzy output parameters. The input parameters and the output parameters are all expressed by the level-values in fuzzy field. Therefore, the output parameters used for control can be obtained for the fuzzy control table according to the detected actual input parameters, by which the EDM combined with ultrasonic vibration is improved and the machining efficiency is increased. In addition, a stimulation program is designed by means of Microsoft Visual Basic展开更多
The influences of the mask wall angle on the current density distribution,shape of the evolving cavity and machining accuracy were investigated in electrochemical machining(ECM) by mask.A mathematical model was develo...The influences of the mask wall angle on the current density distribution,shape of the evolving cavity and machining accuracy were investigated in electrochemical machining(ECM) by mask.A mathematical model was developed to predict the shape evolution during the ECM by mask.The current density distribution is sensitive to mask wall angle.The evolution of cavity is determined by the current density distribution of evolving workpiece surface.The maximum depth is away from the center of holes machined,which leads to the island appearing at the center of cavity for mask wall angles greater than or equal to 90°(β≥90°).The experimental system was established and the simulation results were experimentally verified.The results indicate that the simulation results of cavity shape are consistent with the actual ones.The experiments also show that the repetition accuracy of matrix-hole for β≥90° is higher than that for β<90°.A hole taper is diminished,and the machining accuracy is improved with the mask wall angle increasing.展开更多
In heavy duty machine tools, hydrostatic turntable is often used as a means for providing rotational motion and supporting workpiece, so the accuracy of turntable is crucial for part machining. In order to analyze the...In heavy duty machine tools, hydrostatic turntable is often used as a means for providing rotational motion and supporting workpiece, so the accuracy of turntable is crucial for part machining. In order to analyze the influence of load-indcued errors on machining accuracy, an identification model of load-induced errors based on the deformation caused by applied load of hydrostatic turntable of computerized numerical control(CNC) gantry milling heavy machine is proposed. Based on multi-body system theory and screw theory, the space machining accuracy model of heavy duty machine tool is established with consideration of identified load-induced errors. And then, the influence of load-induced errors on space machining accuracy and the roundness error of a milled hole is analyzed. The analysis results show that load-induced errors have a big influence on the roundness error of machined hole, especially when the center of the milled hole is far from that of hydrostatic turntable.展开更多
In this study, a newly developed titanium superalloy, i.e., the Ti-5553 alloy has used for hot machining. This material replaced Ti-grade-5 alloy in the application of aerospace, automobile, and biomedical sector. How...In this study, a newly developed titanium superalloy, i.e., the Ti-5553 alloy has used for hot machining. This material replaced Ti-grade-5 alloy in the application of aerospace, automobile, and biomedical sector. However, similar to Ti-grade-5 alloy, the Ti-5553 alloy has a low thermal conductivity which makes it difficult-to-cut material categories hence, high tool wear, cutting force and bad surface finish. Hot machining of Ti-5553 has been studied at different machining condition (room and hot) using Deform-2D finite element analysis. The result from the simulation test was compared with the experimental value and reduction of cutting and thrust forces was observed. The experiment was carried out with the same input parameters as simulation, and good coherence between them observed. Additionally, cutting zone temperature, effective stress, etc. for both room and elevated the temperature are also discussed.展开更多
Powder Mixed Electric Discharge Machining (PMEDM) has different mechanism from conventional EDM, which can improve the surface roughness and surface quality distinctly and to obtain nearly mirror surface effects. It i...Powder Mixed Electric Discharge Machining (PMEDM) has different mechanism from conventional EDM, which can improve the surface roughness and surface quality distinctly and to obtain nearly mirror surface effects. It is a useful finish machining method and is researched and applied by many countries. However there are little research on rough machining of PMEDM. Experiments show that PMEDM machining makes discharge breakdown easier, enlarges the discharge gaps and widens discharge passage, and at last forms even distributed and "large and shadow" shaped etched cavities. Because of much loss of discharge energy in the discharge gaps and reduction of ejecting force on the melted material, the machining efficiency gets lower and the surface roughness gets small in PMEDM machining in comparison with conventional EDM machining. This paper performs experimental research on the machining efficiency and surface roughness of PMEDM in rough machining. The machining efficiency of PMEDM can be highly increased by selecting proper discharge parameters (increasing peak current, reducing pulse width) with approximate surface roughness in comparison with conventional EDM machining. Although PMEDM can improve machining efficiency in rough efficiency, but a series of problems like electrode wear, efficiently separation of machined scraps from the powder mixed working fluid, should be solved before PMEDM machining is really applied in rough machining. Experiments result shows that powder mixed EDM machining can obviously improve machining efficiency at the same surface roughness by selecting proper discharging parameters, and can provide reference accordingly for the application of PMEDM machining technology in rough machining.展开更多
This paper describes a new method of surface modification by Electrical Discharge Machining (EDM). By using ordinary EDM machine tool and kerosene fluid, a hard ceramic layer can be created on the workpiece surface wi...This paper describes a new method of surface modification by Electrical Discharge Machining (EDM). By using ordinary EDM machine tool and kerosene fluid, a hard ceramic layer can be created on the workpiece surface with Ti or other compressed powder electrode in a certain condition. This new revolutionary method is called Electrical Discharge Coating (EDC). The process of EDC begins with electrode wear during EDM,then a kind of hard carbide is created through the thermal and chemical reaction between the worn electrode material and the carbon particle decomposed from kerosene fluid under high temperature. The carbide is piled up on a workpiece quickly and becomes a hard layer of ceramic about 20 μm in several minutes. This paper studies the principle and process of EDC systemically by using Ti powder green compact electrode. In order to obtain a layer of compact ceramic film, it is very important to select proper electric pulse parameters, such as pulse width, pulse interval, peak current. Meantime, the electrode materials and its forming mode will effect the machining surface quality greatly. This paper presents a series of experiment results to study the EDC process by adopt different technology parameters. Experiments and analyses show that a compact TiC ceramic layer can be created on the surface of metal workpiece. The hardness of ceramic layer is more 3 times higher than the base body, and the hardness changes gradiently from surface to base body. The method will have a great future because many materials can be easily added to the electrode and then be coated on the workpiece surface. Gearing the parameters ceramic can be created with different thickness. The switch between deposition and removal process is carried out easily by changing the polarity, thus the gear to the thickness and shape of the composite ceramic layer is carried out easily. This kind of composite ceramic layer will be used to deal with the surface of the cutting tools or molds possibly, in order to lengthen their life. It also can be found wide application in the fields of surface repairing and strengthening of the ship or aircraft.展开更多
Ultrasonic machining (USM) is considered as an effective method for machining hard and brittle materials such as glass, engineering ceramics, semiconductors, diamonds, metal composites and so on. However, the low mate...Ultrasonic machining (USM) is considered as an effective method for machining hard and brittle materials such as glass, engineering ceramics, semiconductors, diamonds, metal composites and so on. However, the low material removal rate due to using abrasive slurry limits further application of USM. Rotary ultrasonic machining (rotary USM) superimposes rotational movement on the tool head that vibrates at ultrasonic frequency (20 kHz) simultaneously. The tool is made of mild steel coated or bonded with diamond abrasive. Therefore, abrasive slurry is abandoned and coolant is used to carry debris out of working area. Compared with USM, rotary USM can obtain much higher material removal rate, deep holes, and fine precision, which leads to its further application. Combined with CNC technology, rotary USM can be used to conduct contour machining of hard and brittle materials. In this paper, the movement of abrasive particles in tool tip of rotary ultrasonic machining is analyzed. The impacting and grinding of abrasive in tool tip to machined surface are considered as main factors to material removal rate. The process of crack forming and growing in one loading and unloading cycle can be described as following stages: a) When abrasive particle acts the pressure on work-piece, the macro cracks in periphery of contact area are exerted increasing tensile stress. b) As the tensile stress increase to the critical of material tension, the one of cracks in periphery of contact area begins to propagate around contact area and develop beneath the surface to certain depth. c) Indentation area varies with increasing of load, the circle crack around contact area steadily or dynamical propagates towards inside of work-piece. d) As tensile stress in crack increases to critical of crack steady failure, circle crack suddenly becomes conic crack. e) Further increase load, the crack continues to grow while contact area is surrounded by conic cracks. f) During unloading, conic crack begins to close, some of cracks continue their extension towards the surface and forms a circle groove. The mathematical model for material removal rate shows that the factors affecting on material removal rate are static load, grid and concentration of abrasive, mechanical properties of machined materials, rotational speed of tool and feed speed of work-piece.展开更多
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.
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 the Guangdong Major Project of Basic and Applied Basic Research(Grant No.2021B0301030001)the National Key Research and Development Program of China(Grant No.2021YFB3802300)the Foundation of National Key Laboratory of Shock Wave and Detonation Physics(Grant No.JCKYS2022212004)。
文摘The graded density impactor(GDI)dynamic loading technique is crucial for acquiring the dynamic physical property parameters of materials used in weapons.The accuracy and timeliness of GDI structural design are key to achieving controllable stress-strain rate loading.In this study,we have,for the first time,combined one-dimensional fluid computational software with machine learning methods.We first elucidated the mechanisms by which GDI structures control stress and strain rates.Subsequently,we constructed a machine learning model to create a structure-property response surface.The results show that altering the loading velocity and interlayer thickness has a pronounced regulatory effect on stress and strain rates.In contrast,the impedance distribution index and target thickness have less significant effects on stress regulation,although there is a matching relationship between target thickness and interlayer thickness.Compared with traditional design methods,the machine learning approach offers a10^(4)—10^(5)times increase in efficiency and the potential to achieve a global optimum,holding promise for guiding the design of GDI.
基金supported by the Aeronautical Science Foundation of China(Grant No.20230018072011)。
文摘3-Nitro-1,2,4-triazol-5-one(NTO)is a typical high-energy,low-sensitivity explosive,and accurate concentration monitoring is critical for crystallization process control.In this study,a high-precision quantitative analytical model for NTO concentration in ethanol solutions was developed by integrating real-time ATR-FTIR spectroscopy with chemometric and machine learning techniques.Dynamic spectral data were obtained by designing multi-concentration gradient heating-cooling cycle experiments,abnormal samples were eliminated using the isolation forest algorithm,and the effects of various preprocessing methods on model performance were systematically evaluated.The results show that partial least squares regression(PLSR)exhibits superior generalization ability compared to other models.Vibrational bands corresponding to C=O and–NO_(2)were identified as key predictors for concentration estimation.This work provides an efficient and reliable solution for real-time concentration monitoring during NTO crystallization and holds significant potential for process analytical applications in energetic material manufacturing.
基金supported by Agricultural Science and Technology Innovation Program of Chinese Academy of Agricultural Science。
文摘Background The geo-traceability of cotton is crucial for ensuring the quality and integrity of cotton brands. However, effective methods for achieving this traceability are currently lacking. This study investigates the potential of explainable machine learning for the geo-traceability of raw cotton.Results The findings indicate that principal component analysis(PCA) exhibits limited effectiveness in tracing cotton origins. In contrast, partial least squares discriminant analysis(PLS-DA) demonstrates superior classification performance, identifying seven discriminating variables: Na, Mn, Ba, Rb, Al, As, and Pb. The use of decision tree(DT), support vector machine(SVM), and random forest(RF) models for origin discrimination yielded accuracies of 90%, 87%, and 97%, respectively. Notably, the light gradient boosting machine(Light GBM) model achieved perfect performance metrics, with accuracy, precision, and recall rate all reaching 100% on the test set. The output of the Light GBM model was further evaluated using the SHapley Additive ex Planation(SHAP) technique, which highlighted differences in the elemental composition of raw cotton from various countries. Specifically, the elements Pb, Ni, Na, Al, As, Ba, and Rb significantly influenced the model's predictions.Conclusion These findings suggest that explainable machine learning techniques can provide insights into the complex relationships between geographic information and raw cotton. Consequently, these methodologies enhances the precision and reliability of geographic traceability for raw cotton.
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the Deanship of Scientific Research at Northern Border University,Arar,KSA for funding this research work through the project number"NBUFFMRA-2025-2461-09"。
文摘Blast-induced ground vibration,quantified by peak particle velocity(PPV),is a crucial factor in mitigating environmental and structural risks in mining and geotechnical engineering.Accurate PPV prediction facilitates safer and more sustainable blasting operations by minimizing adverse impacts and ensuring regulatory compliance.This study presents an advanced predictive framework integrating Cat Boost(CB)with nature-inspired optimization algorithms,including the Bat Algorithm(BAT),Sparrow Search Algorithm(SSA),Butterfly Optimization Algorithm(BOA),and Grasshopper Optimization Algorithm(GOA).A comprehensive dataset from the Sarcheshmeh Copper Mine in Iran was utilized to develop and evaluate these models using key performance metrics such as the Index of Agreement(IoA),Nash-Sutcliffe Efficiency(NSE),and the coefficient of determination(R^(2)).The hybrid CB-BOA model outperformed other approaches,achieving the highest accuracy(R^(2)=0.989)and the lowest prediction errors.SHAP analysis identified Distance(Di)as the most influential variable affecting PPV,while uncertainty analysis confirmed CB-BOA as the most reliable model,featuring the narrowest prediction interval.These findings highlight the effectiveness of hybrid machine learning models in refining PPV predictions,contributing to improved blast design strategies,enhanced structural safety,and reduced environmental impacts in mining and geotechnical engineering.
基金Project(2024JJ2074) supported by the Natural Science Foundation of Hunan Province,ChinaProject(22376221) supported by the National Natural Science Foundation of ChinaProject(2023QNRC001) supported by the Young Elite Scientists Sponsorship Program by CAST,China。
文摘Driven by rapid technological advancements and economic growth,mineral extraction and metal refining have increased dramatically,generating huge volumes of tailings and mine waste(TMWs).Investigating the morphological fractions of heavy metals and metalloids(HMMs)in TMWs is key to evaluating their leaching potential into the environment;however,traditional experiments are time-consuming and labor-intensive.In this study,10 machine learning(ML)algorithms were used and compared for rapidly predicting the morphological fractions of HMMs in TMWs.A dataset comprising 2376 data points was used,with mineral composition,elemental properties,and total concentration used as inputs and concentration of morphological fraction used as output.After grid search optimization,the extra tree model performed the best,achieving coefficient of determination(R2)of 0.946 and 0.942 on the validation and test sets,respectively.Electronegativity was found to have the greatest impact on the morphological fraction.The models’performance was enhanced by applying an ensemble method to the top three optimal ML models,including gradient boosting decision tree,extra trees and categorical boosting.Overall,the proposed framework can accurately predict the concentrations of different morphological fractions of HMMs in TMWs.This approach can minimize detection time,aid in the safe management and recovery of TMWs.
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.
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.
文摘For electro-discharge machining, only in the optimum state could the highest material removal rate be realized. In practical machining process, the timely elevation of the tool electrode is needed to eliminate chipping, which ordinarily occupies quite a lot of time. Therefore, besides the control of the machining parameters, the control of the optimum discharge gap and the conversion of different machining states is also needed. In this paper, the adaptive fuzzy control system of servomechanism for EDM combined with ultrasonic vibration is studied, the servomechanism of which is composed of the stepping motor comprising variable steps and the inductive synchronizer. The fuzzy control technology is used to realize the control of the frequency and the step of the servomechanism. The adaptive fuzzy controller has three inputs and two outputs, which can well meet the actual control requirements. The constitution of the fuzzy control regulation for the step frequency is the key to the design of the whole fuzzy control system of the servomechanism. The step frequency is mainly determined by the position error and the change rate of the position error. When the value of the position error is high or medium, the controlled parameters are selected to eliminate the error; when the position error is lower, the controlled parameters are selected to avoid the over-orientation and thus keep the stability of the system. According to these, a fuzzy control table is established in advanced, which is used to express the relations between the fuzzy input parameters and the fuzzy output parameters. The input parameters and the output parameters are all expressed by the level-values in fuzzy field. Therefore, the output parameters used for control can be obtained for the fuzzy control table according to the detected actual input parameters, by which the EDM combined with ultrasonic vibration is improved and the machining efficiency is increased. In addition, a stimulation program is designed by means of Microsoft Visual Basic
基金Project(50635040) supported by the National Natural Science Foundation of ChinaProject(2009AA044205) supported by the National High Technology Research and Development ProgramProject(BK2008043) supported by the Jiangsu Provincial Natural Science Foundation,China
文摘The influences of the mask wall angle on the current density distribution,shape of the evolving cavity and machining accuracy were investigated in electrochemical machining(ECM) by mask.A mathematical model was developed to predict the shape evolution during the ECM by mask.The current density distribution is sensitive to mask wall angle.The evolution of cavity is determined by the current density distribution of evolving workpiece surface.The maximum depth is away from the center of holes machined,which leads to the island appearing at the center of cavity for mask wall angles greater than or equal to 90°(β≥90°).The experimental system was established and the simulation results were experimentally verified.The results indicate that the simulation results of cavity shape are consistent with the actual ones.The experiments also show that the repetition accuracy of matrix-hole for β≥90° is higher than that for β<90°.A hole taper is diminished,and the machining accuracy is improved with the mask wall angle increasing.
基金Projects(51575010,51575009)supported by the National Natural Science Foundations of ChinaProject(Z1511000003150138)supported by Beijing Nova Program,China
文摘In heavy duty machine tools, hydrostatic turntable is often used as a means for providing rotational motion and supporting workpiece, so the accuracy of turntable is crucial for part machining. In order to analyze the influence of load-indcued errors on machining accuracy, an identification model of load-induced errors based on the deformation caused by applied load of hydrostatic turntable of computerized numerical control(CNC) gantry milling heavy machine is proposed. Based on multi-body system theory and screw theory, the space machining accuracy model of heavy duty machine tool is established with consideration of identified load-induced errors. And then, the influence of load-induced errors on space machining accuracy and the roundness error of a milled hole is analyzed. The analysis results show that load-induced errors have a big influence on the roundness error of machined hole, especially when the center of the milled hole is far from that of hydrostatic turntable.
文摘In this study, a newly developed titanium superalloy, i.e., the Ti-5553 alloy has used for hot machining. This material replaced Ti-grade-5 alloy in the application of aerospace, automobile, and biomedical sector. However, similar to Ti-grade-5 alloy, the Ti-5553 alloy has a low thermal conductivity which makes it difficult-to-cut material categories hence, high tool wear, cutting force and bad surface finish. Hot machining of Ti-5553 has been studied at different machining condition (room and hot) using Deform-2D finite element analysis. The result from the simulation test was compared with the experimental value and reduction of cutting and thrust forces was observed. The experiment was carried out with the same input parameters as simulation, and good coherence between them observed. Additionally, cutting zone temperature, effective stress, etc. for both room and elevated the temperature are also discussed.
文摘Powder Mixed Electric Discharge Machining (PMEDM) has different mechanism from conventional EDM, which can improve the surface roughness and surface quality distinctly and to obtain nearly mirror surface effects. It is a useful finish machining method and is researched and applied by many countries. However there are little research on rough machining of PMEDM. Experiments show that PMEDM machining makes discharge breakdown easier, enlarges the discharge gaps and widens discharge passage, and at last forms even distributed and "large and shadow" shaped etched cavities. Because of much loss of discharge energy in the discharge gaps and reduction of ejecting force on the melted material, the machining efficiency gets lower and the surface roughness gets small in PMEDM machining in comparison with conventional EDM machining. This paper performs experimental research on the machining efficiency and surface roughness of PMEDM in rough machining. The machining efficiency of PMEDM can be highly increased by selecting proper discharge parameters (increasing peak current, reducing pulse width) with approximate surface roughness in comparison with conventional EDM machining. Although PMEDM can improve machining efficiency in rough efficiency, but a series of problems like electrode wear, efficiently separation of machined scraps from the powder mixed working fluid, should be solved before PMEDM machining is really applied in rough machining. Experiments result shows that powder mixed EDM machining can obviously improve machining efficiency at the same surface roughness by selecting proper discharging parameters, and can provide reference accordingly for the application of PMEDM machining technology in rough machining.
文摘This paper describes a new method of surface modification by Electrical Discharge Machining (EDM). By using ordinary EDM machine tool and kerosene fluid, a hard ceramic layer can be created on the workpiece surface with Ti or other compressed powder electrode in a certain condition. This new revolutionary method is called Electrical Discharge Coating (EDC). The process of EDC begins with electrode wear during EDM,then a kind of hard carbide is created through the thermal and chemical reaction between the worn electrode material and the carbon particle decomposed from kerosene fluid under high temperature. The carbide is piled up on a workpiece quickly and becomes a hard layer of ceramic about 20 μm in several minutes. This paper studies the principle and process of EDC systemically by using Ti powder green compact electrode. In order to obtain a layer of compact ceramic film, it is very important to select proper electric pulse parameters, such as pulse width, pulse interval, peak current. Meantime, the electrode materials and its forming mode will effect the machining surface quality greatly. This paper presents a series of experiment results to study the EDC process by adopt different technology parameters. Experiments and analyses show that a compact TiC ceramic layer can be created on the surface of metal workpiece. The hardness of ceramic layer is more 3 times higher than the base body, and the hardness changes gradiently from surface to base body. The method will have a great future because many materials can be easily added to the electrode and then be coated on the workpiece surface. Gearing the parameters ceramic can be created with different thickness. The switch between deposition and removal process is carried out easily by changing the polarity, thus the gear to the thickness and shape of the composite ceramic layer is carried out easily. This kind of composite ceramic layer will be used to deal with the surface of the cutting tools or molds possibly, in order to lengthen their life. It also can be found wide application in the fields of surface repairing and strengthening of the ship or aircraft.
文摘Ultrasonic machining (USM) is considered as an effective method for machining hard and brittle materials such as glass, engineering ceramics, semiconductors, diamonds, metal composites and so on. However, the low material removal rate due to using abrasive slurry limits further application of USM. Rotary ultrasonic machining (rotary USM) superimposes rotational movement on the tool head that vibrates at ultrasonic frequency (20 kHz) simultaneously. The tool is made of mild steel coated or bonded with diamond abrasive. Therefore, abrasive slurry is abandoned and coolant is used to carry debris out of working area. Compared with USM, rotary USM can obtain much higher material removal rate, deep holes, and fine precision, which leads to its further application. Combined with CNC technology, rotary USM can be used to conduct contour machining of hard and brittle materials. In this paper, the movement of abrasive particles in tool tip of rotary ultrasonic machining is analyzed. The impacting and grinding of abrasive in tool tip to machined surface are considered as main factors to material removal rate. The process of crack forming and growing in one loading and unloading cycle can be described as following stages: a) When abrasive particle acts the pressure on work-piece, the macro cracks in periphery of contact area are exerted increasing tensile stress. b) As the tensile stress increase to the critical of material tension, the one of cracks in periphery of contact area begins to propagate around contact area and develop beneath the surface to certain depth. c) Indentation area varies with increasing of load, the circle crack around contact area steadily or dynamical propagates towards inside of work-piece. d) As tensile stress in crack increases to critical of crack steady failure, circle crack suddenly becomes conic crack. e) Further increase load, the crack continues to grow while contact area is surrounded by conic cracks. f) During unloading, conic crack begins to close, some of cracks continue their extension towards the surface and forms a circle groove. The mathematical model for material removal rate shows that the factors affecting on material removal rate are static load, grid and concentration of abrasive, mechanical properties of machined materials, rotational speed of tool and feed speed of work-piece.
