Nontraditional energy-assisted mechanical machining of difficult-to-cut materials and components in aerospace community:a comparative analysis

The aerospace community widely uses difficult-to-cut materials,such as titanium alloys,high-temperature alloys,metal/ceramic/polymer matrix composites,hard and brittle materials,and geometrically complex components,such as thin-walled structures,microchannels,and complex surfaces.Mechanical machining is the main material removal process for the vast majority of aerospace components.However,many problems exist,including severe and rapid tool wear,low machining efficiency,and poor surface integrity.Nontraditional energy-assisted mechanical machining is a hybrid process that uses nontraditional energies(vibration,laser,electricity,etc)to improve the machinability of local materials and decrease the burden of mechanical machining.This provides a feasible and promising method to improve the material removal rate and surface quality,reduce process forces,and prolong tool life.However,systematic reviews of this technology are lacking with respect to the current research status and development direction.This paper reviews the recent progress in the nontraditional energy-assisted mechanical machining of difficult-to-cut materials and components in the aerospace community.In addition,this paper focuses on the processing principles,material responses under nontraditional energy,resultant forces and temperatures,material removal mechanisms,and applications of these processes,including vibration-,laser-,electric-,magnetic-,chemical-,advanced coolant-,and hybrid nontraditional energy-assisted mechanical machining.Finally,a comprehensive summary of the principles,advantages,and limitations of each hybrid process is provided,and future perspectives on forward design,device development,and sustainability of nontraditional energy-assisted mechanical machining processes are discussed.

Stress-assisted corrosion mechanism of 3Ni steel by using gradient boosting decision tree machining learning method

Traditional 3Ni weathering steel cannot completely meet the requirements for offshore engineering development,resulting in the design of novel 3Ni steel with the addition of microalloy elements such as Mn or Nb for strength enhancement becoming a trend.The stress-assisted corrosion behavior of a novel designed high-strength 3Ni steel was investigated in the current study using the corrosion big data method.The information on the corrosion process was recorded using the galvanic corrosion current monitoring method.The gradi-ent boosting decision tree(GBDT)machine learning method was used to mine the corrosion mechanism,and the importance of the struc-ture factor was investigated.Field exposure tests were conducted to verify the calculated results using the GBDT method.Results indic-ated that the GBDT method can be effectively used to study the influence of structural factors on the corrosion process of 3Ni steel.Dif-ferent mechanisms for the addition of Mn and Cu to the stress-assisted corrosion of 3Ni steel suggested that Mn and Cu have no obvious effect on the corrosion rate of non-stressed 3Ni steel during the early stage of corrosion.When the corrosion reached a stable state,the in-crease in Mn element content increased the corrosion rate of 3Ni steel,while Cu reduced this rate.In the presence of stress,the increase in Mn element content and Cu addition can inhibit the corrosion process.The corrosion law of outdoor-exposed 3Ni steel is consistent with the law based on corrosion big data technology,verifying the reliability of the big data evaluation method and data prediction model selection.

纳米颗粒活性剂混粉EDM特性研究

为了进一步提高EDM效率和表面质量,提出了纳米颗粒活性剂混粉EDM方法。基于Marangoni效应分析了活性剂对EDM材料去除的增强机制,并开展实验研究了纳米活性剂对钛合金EDM特性的影响。结果表明:当开路电压为100 V时,相对于混有Al_(2)O_(3)和ZrO_(2)纳米颗粒的工作液,在混有TiO_(2)纳米颗粒工作液中进行EDM可得到最高的材料去除率。采用混有纳米颗粒表面活性剂的工作液进行EDM可得到更低的工具损耗率、更高的微孔锥形度以及更低的表面粗糙度。通过在去离子水工作液中混入TiO_(2)纳米颗粒,可将EDM的表面粗糙度Ra降低50%。因此,在EDM工作液中加入活性剂纳米颗粒有助于提高加工性能。

On dry machining of AZ31B magnesium alloy using textured cutting tool inserts

Magnesium alloys have many advantages as lightweight materials for engineering applications,especially in the fields of automotive and aerospace.They undergo extensive cutting or machining while making products out of them.Dry cutting,a sustainable machining method,causes more friction and adhesion at the tool-chip interface.One of the promising solutions to this problem is cutting tool surface texturing,which can reduce tool wear and friction in dry cutting and improve machining performance.This paper aims to investigate the impact of dimple textures(made on the flank face of cutting inserts)on tool wear and chip morphology in the dry machining of AZ31B magnesium alloy.The results show that the cutting speed was the most significant factor affecting tool flank wear,followed by feed rate and cutting depth.The tool wear mechanism was examined using scanning electron microscope(SEM)images and energy dispersive X-ray spectroscopy(EDS)analysis reports,which showed that at low cutting speed,the main wear mechanism was abrasion,while at high speed,it was adhesion.The chips are discontinuous at low cutting speeds,while continuous at high cutting speeds.The dimple textured flank face cutting tools facilitate the dry machining of AZ31B magnesium alloy and contribute to ecological benefits.

Field-assisted machining of difficult-to-machine materials

Difficult-to-machine materials (DMMs) are extensively applied in critical fields such as aviation,semiconductor,biomedicine,and other key fields due to their excellent material properties.However,traditional machining technologies often struggle to achieve ultra-precision with DMMs resulting from poor surface quality and low processing efficiency.In recent years,field-assisted machining (FAM) technology has emerged as a new generation of machining technology based on innovative principles such as laser heating,tool vibration,magnetic magnetization,and plasma modification,providing a new solution for improving the machinability of DMMs.This technology not only addresses these limitations of traditional machining methods,but also has become a hot topic of research in the domain of ultra-precision machining of DMMs.Many new methods and principles have been introduced and investigated one after another,yet few studies have presented a comprehensive analysis and summarization.To fill this gap and understand the development trend of FAM,this study provides an important overview of FAM,covering different assisted machining methods,application effects,mechanism analysis,and equipment design.The current deficiencies and future challenges of FAM are summarized to lay the foundation for the further development of multi-field hybrid assisted and intelligent FAM technologies.

FPGA高速数据采集的WEDM在线脉冲识别

为了提高电火花加工放电间隙的实时识别准确率,文中提出了一种基于FPGA高速数据采集的神经网络的放电间隙在线识别方案。利用FPGA和AD模块对加工间隙电压数据进行高速采集,通过FPGA内部的FIFO和串口协同实现周期性数据传输。在Python的Pytorch库构建了CNN GRU融合神经网络模型,对所采集的数据进行在线识别,验证了该采集系统的可行性。实验结果表明:文中方案对加工电压脉冲的离线识别准确率达到了97.35%,优于CNN和GRU方法,满足了电火花加工对间隙电压识别的高精度要求,实现了神经网络的实时在线识别放电间隙。

Application Research on Powder Mixed EDM in Rough Machining

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.

Comparative experimental study of machining characteristics of air-mixed EDM and conventional EDM

A new method of electrical discharge machining （EDM） in air-mixed medium--air-mixed EDM was presented, and comparative experiments were carried out to compare the machining characteristics of air-mixed EDM with conventional EDM. The experimental results showed that better machining performances were obtained with tool electrode as the cathode and workpieee as the anode. It was found that the material removal rate （MRR） was higher, the tool electrode relative wear ratio （TWR） was lower and the surface roughness （SR） quality was better in the case of air-mixed EDM than that of conventional EDM, and the reasons bringing out the results were analyzed.

Effect of tool geometry on ultraprecision machining of soft-brittle materials:a comprehensive review

Brittle materials are widely used for producing important components in the industry of optics,optoelectronics,and semiconductors.Ultraprecision machining of brittle materials with high surface quality and surface integrity helps improve the functional performance and lifespan of the components.According to their hardness,brittle materials can be roughly divided into hard-brittle and soft-brittle.Although there have been some literature reviews for ultraprecision machining of hard-brittle materials,up to date,very few review papers are available that focus on the processing of soft-brittle materials.Due to the‘soft’and‘brittle’properties,this group of materials has unique machining characteristics.This paper presents a comprehensive overview of recent advances in ultraprecision machining of soft-brittle materials.Critical aspects of machining mechanisms,such as chip formation,surface topography,and subsurface damage for different machining methods,including diamond turning,micro end milling,ultraprecision grinding,and micro/nano burnishing,are compared in terms of tool-workpiece interaction.The effects of tool geometries on the machining characteristics of soft-brittle materials are systematically analyzed,and dominating factors are sorted out.Problems and challenges in the engineering applications are identified,and solutions/guidelines for future R&D are provided.

Laser machining fundamentals:micro,nano,atomic and close-to-atomic scales

With the rapid development in advanced industries,such as microelectronics and optics sectors,the functional feature size of devises/components has been decreasing from micro to nanometric,and even ACS for higher performance,smaller volume and lower energy consumption.By this time,a great many quantum structures are proposed,with not only an extreme scale of several or even single atom,but also a nearly ideal lattice structure with no material defect.It is almost no doubt that such structures play critical role in the next generation products,which shows an urgent demand for the ACSM.Laser machining is one of the most important approaches widely used in engineering and scientific research.It is high-efficient and applicable for most kinds of materials.Moreover,the processing scale covers a huge range from millimeters to nanometers,and has already touched the atomic level.Laser–material interaction mechanism,as the foundation of laser machining,determines the machining accuracy and surface quality.It becomes much more sophisticated and dominant with a decrease in processing scale,which is systematically reviewed in this article.In general,the mechanisms of laser-induced material removal are classified into ablation,CE and atomic desorption,with a decrease in the scale from above microns to angstroms.The effects of processing parameters on both fundamental material response and machined surface quality are discussed,as well as theoretical methods to simulate and understand the underlying mechanisms.Examples at nanometric to atomic scale are provided,which demonstrate the capability of laser machining in achieving the ultimate precision and becoming a promising approach to ACSM.

Failure mode change and material damage with varied machining speeds:a review

High-speed machining(HSM) has been studied for several decades and has potential application in various industries, including the automobile and aerospace industries. However,the underlying mechanisms of HSM have not been formally reviewed thus far. This article focuses on the solid mechanics framework of adiabatic shear band(ASB) onset and material metallurgical microstructural evolutions in HSM. The ASB onset is described using partial differential systems. Several factors in HSM were considered in the systems, and the ASB onset conditions were obtained by solving these systems or applying the perturbation method to the systems. With increasing machining speed, an ASB can be depressed and further eliminated by shock pressure. The damage observed in HSM exhibits common features. Equiaxed fine grains produced by dynamic recrystallization widely cause damage to ductile materials, and amorphization is the common microstructural evolution in brittle materials. Based on previous studies, potential mechanisms for the phenomena in HSM are proposed. These include the thickness variation of the white layer of ductile materials. These proposed mechanisms would be beneficial to deeply understanding the various phenomena in HSM.

基于FOCAS的模具EDM零件加工优先级设定研究

针对模具加工自动线生产流程中所遇到的多个模具零件同时到达EDM加工环节的问题,提出计算电极的程序完成进度率实时监测模具电极数控加工进度的方法。采用FOCAS技术利用C#语言调用FOCAS相关函数对FANUC系统0i系列数控机床进行NC程序下载和已经运行完成的程序行数等数据采集;基于采集所得数据,建立SQL Server数据库对数据进行及时存储;结合自动线流程中的约束情况,提出模具零件加工优先级计算方案,连接MES系统数据库和SQL Server数据库获取相关数据,通过比较EDM机床数量、电极齐套率、电极程序完成进度率等,对相关数据进行冒泡排序进而确定模具零件的优先级数,从而设定模具零件加工的先后顺序。在某模具企业验证了该方案的可行性,并取得了良好的应用效果。

Assessment of Lubrication Property and Machining Performance of Nanofluid Composite Electrostatic Spraying(NCES)Using Different Types of Vegetable Oils as Base Fluids of External Fluid

The current study of minimum quantity lubrication(MQL)concentrates on its performance improvement.By contrast with nanofluid MQL and electrostatic atomization(EA),the proposed nanofluid composite electrostatic spraying(NCES)can enhance the performance of MQL more comprehensively.However,it is largely influenced by the base fluid of external fluid.In this paper,the lubrication property and machining performance of NCES with different types of vegetable oils(castor,palm,soybean,rapeseed,and LB2000 oil)as the base fluids of external fluid were compared and evaluated by friction and milling tests under different flow ratios of external and internal fluids.The spraying current and electrowetting angle were tested to analyze the influence of vegetable oil type as the base fluid of external fluid on NCES performances.The friction test results show that relative to NCES with other vegetable oils as the base fluids of external fluid,NCES with LB2000 as the base fluid of external fluid reduced the friction coefficient and wear loss by 9.4%-27.7%and 7.6%-26.5%,respectively.The milling test results display that the milling force and milling temperature for NCES with LB2000 as the base fluid of external fluid were 1.4%-13.2%and 3.6%-11.2%lower than those for NCES with other vegetable oils as the base fluids of external fluid,respectively.When LB2000/multi-walled carbon nanotube(MWCNT)water-based nanofluid was used as the external/internal fluid and the flow ratio of external and internal fluids was 2:1,NCES showed the best milling performance.This study provides theoretical and technical support for the selection of the base fluid of NCES external fluid.

Micro Electrical Discharge Machining(μEDM):Holes Drilling and Characterization of the Process Parameters

μEDM(micro-electrical discharge machining)is a process for machining conductive materials without mechanical contact;it is particularly suitable for machining hard materials.The principle consists in creating electrical discharges between a micro-tool and a workpiece,both of which are immersed in a dielectric.It is a complementary process to mechanical,laser,micro-machining techniques,and even to techniques derived from silicon microtechnology(RIE,DRIE,LIGA).However,the resolution ofμEDM is limited;it depends on several electrical and physical parameters.The goal of this paper is to characterize the holes obtained by drilling usingμEDM with different micro-tool diameters(Φ=250μm;Φ=80μm;Φ=40μm;Φ=20μm)for an experimental time of t=2 h.The results obtained let us conclude that a large diameter micro-tool(Φ=250μm)leads to removing a larger amount of material(43×10^(5)μm^(3))than small diameters:Φ=80μm;Φ=40μm;Φ=20μm where the removed volume is equal to 2.6×10^(5)μm^(3);105μm^(3);0.4×10^(5)μm^(3),respectively.The electrode-tool diameter influences the maximum depth of the holes;a diameter ofΦ=250μm generates a hole where the maximum depth is 170μm while small diameters:Φ=80μm;Φ=40μm;Φ=20μm provide holes with a depth of 82μm;51μm;50μm respectively.Through these experiments,we can also conclude that the lateral gap of the holes is almost constant.It is about 40μm whatever the diameter.

EGCN-CeDML:一种面向车辆驾驶行为预测的分布式机器学习框架

在大规模动态变化的交通场景下,快速准确地预测车辆驾驶行为是智能交通领域极具挑战的问题之一。车辆驾驶行为的预测不仅要考虑通信的有效性,而且要考虑车辆历史行驶轨迹以及车辆之间的相互影响。文中综合考虑了上述因素,提出了一种新的基于边-增强图卷神经网络的通信有效的分布式机器学习框架EGCN-CeDML(Edge-enhanced Graph Convolutional Neural Network-Communication-efficient Distributed Machine Learning)。相比面向单一设备的集中式预测框架,EGCN-CeDML是通信有效的分布式机器学习框架,该框架无需将所有原始数据发送到云服务器,而是直接将用户数据在本地边缘设备存储、处理和计算。这种在多个边缘设备训练神经网络的方式缓解了集中训练神经网络的压力,降低了传输数据量和通信延迟,提升了数据处理效率,在一定程度上也保护了用户隐私。各个边缘设备部署的复合图卷积网络(EGCN-LSTM)利用边-增强注意力机制和图卷积神经网络的特征传递机制,当周围车辆数量增长至十几辆时仍能快速提取和传递车辆间的交互信息,保证了较准确的预测性能和较低的时间复杂度。不限于车辆驾驶行为预测,各边缘设备可以根据自身的计算能能力和存储能力,在保证神经网络性能的前提下灵活控制神经网络的类型和规模以适用于不同的应用场景。EGCN-CeDML在公开数据集NGSIM上的实验结果表明:无论交通复杂程度如何,EGCN-CeDML的计算时间和预测性能都优于以往模型,精准率可达0.9391,召回率可达0.9557,F1分数可达0.9473;预测时长为1 s时,预测准确率达到了91.21%;即使车辆数目增加,算法也能保持较低的时间复杂度,且稳定在0.1 s以内。

Prediction of Wearing of Cutting Tools Using Real Time Machining Parameters and Temperature Using Rayleigh-Ham Method

Wear of cutting tools is a big concern for industrial manufacturers, because of their acquisition cost as well as the impact on the production lines when they are unavailable. Law of wear is very important in determining cutting tools lifespan, but most of the existing models don’t take into account the cutting temperature. In this work, the theoretical and experimental results of a dynamic study of metal machining against cutting temperature of a treated steel of grade S235JR with a high-speed steel tool are provided. This study is based on the analysis of two complementary approaches, an experimental approach with the measurement of the temperature and on the other hand, an approach using modeling. Based on unifactorial and multifactorial tests (speed of cut, feed, and depth of cut), this study allowed the highlighting of the influence of the cutting temperature on the machining time. To achieve this objective, two specific approaches have been selected. The first was to measure the temperature of the cutting tool and the second was to determine the wear law using Rayleigh-Ham dimensional analysis method. This study permitted the determination of a law that integrates the cutting temperature in the calculations of the lifespan of the tools during machining.

A New Dynamics Analysis Model for Five-Axis Machining of Curved Surface Based on Dimension Reduction and Mapping

The equipment used in various fields contains an increasing number of parts with curved surfaces of increasing size.Five-axis computer numerical control(CNC)milling is the main parts machining method,while dynamics analysis has always been a research hotspot.The cutting conditions determined by the cutter axis,tool path,and workpiece geometry are complex and changeable,which has made dynamics research a major challenge.For this reason,this paper introduces the innovative idea of applying dimension reduction and mapping to the five-axis machining of curved surfaces,and proposes an efficient dynamics analysis model.To simplify the research object,the cutter position points along the tool path were discretized into inclined plane five-axis machining.The cutter dip angle and feed deflection angle were used to define the spatial position relationship in five-axis machining.These were then taken as the new base variables to construct an abstract two-dimensional space and establish the mapping relationship between the cutter position point and space point sets to further simplify the dimensions of the research object.Based on the in-cut cutting edge solved by the space limitation method,the dynamics of the inclined plane five-axis machining unit were studied,and the results were uniformly stored in the abstract space to produce a database.Finally,the prediction of the milling force and vibration state along the tool path became a data extraction process that significantly improved efficiency.Two experiments were also conducted which proved the accuracy and efficiency of the proposed dynamics analysis model.This study has great potential for the online synchronization of intelligent machining of large surfaces.

Optimization of CNC Turning Machining Parameters Based on Bp-DWMOPSO Algorithm

Cutting parameters have a significant impact on the machining effect.In order to reduce the machining time and improve the machining quality,this paper proposes an optimization algorithm based on Bp neural networkImproved Multi-Objective Particle Swarm(Bp-DWMOPSO).Firstly,this paper analyzes the existing problems in the traditional multi-objective particle swarm algorithm.Secondly,the Bp neural network model and the dynamic weight multi-objective particle swarm algorithm model are established.Finally,the Bp-DWMOPSO algorithm is designed based on the established models.In order to verify the effectiveness of the algorithm,this paper obtains the required data through equal probability orthogonal experiments on a typical Computer Numerical Control(CNC)turning machining case and uses the Bp-DWMOPSO algorithm for optimization.The experimental results show that the Cutting speed is 69.4 mm/min,the Feed speed is 0.05 mm/r,and the Depth of cut is 0.5 mm.The results show that the Bp-DWMOPSO algorithm can find the cutting parameters with a higher material removal rate and lower spindle load while ensuring the machining quality.This method provides a new idea for the optimization of turning machining parameters.

Powder mixed electrochemical discharge process for micro machining of C103 niobium alloy

This work demonstrates the viability of the powder-mixed micro-electrochemical discharge machining(PMECDM) process to fabricate micro-holes on C103 niobium-based alloy for high temperature applications.Three processes are involved simultaneously i.e.spark erosion,chemical etching,and abrasive grinding for removal of material while the classical electrochemical discharge machining process involves double actions i.e.spark erosion,and chemical etching.The powder-mixed electrolyte process resulted in rapid material removal along with a better surface finish as compared to the classical microelectrochemical discharge machining(MECDM).Further,the results are optimized through a multiobjective optimization approach and study of the surface topography of the hole wall surface obtained at optimized parameters.In the selected range of experimental parameters,PMECDM shows a higher material removal rate(MRR) and lower surface roughness(R_(a))(MRR:2.8 mg/min and R_(a) of 0.61 μm) as compared to the MECDM process(MRR:2.01 mg/min and corresponding Raof 1.11 μm).A detailed analysis of the results is presented in this paper.

Influence of Process Factors on Electrode Wear Rate when EDM 90CrSi Steel using Graphite Electrodes

The findings of a study on the optimization of Electric Discharge Machining(EDM)procedure for processing cylindrically shaped components with graphite electrodes are presented in this paper.Tool steel 90CrSi was the material of choice for the study’s workpiece.Additionally,the Electrode Wear Rate(EWR)was chosen as the optimization problem’s objective function.An experiment using the Taguchi design was conducted to address this issue.Additionally,experiments were created and their outcomes were analyzed using the Minitab R19 program.The impact of the input process factors on EWR was investigated,including the powder concentration,the pulse on and off times,the servo current,and the servo voltage.Additionally,the best process variables for a minimal EWR were provided.