基于航空铸造钛合金Ti-6Al-4V高速铣削参数的表面质量及切削效率优化

目的研究高速铣削参数对航空铸造钛合金Ti-6Al-4V表面质量的影响规律及交互作用,并基于高速铣削参数对表面质量和材料去除率进行优化。方法采用Box-Behnken设计和二次回归正交实验法,建立高速铣削参数与表面粗糙度的显著不失拟回归模型,获得铣削参数影响表面粗糙度的显著性差异,挖掘高速铣削参数交互作用与表面粗糙度的关系;基于表面粗糙度回归模型及材料去除率,采用遗传算法(GA),对高速铣削参数进行多目标优化。结果铣削参数影响航空铸造钛合金Ti-6Al-4V试件表面粗糙度的显著性顺序为:切削深度>每齿进给量>切削宽度>主轴转速,其中切削宽度和主轴转速、每齿进给量和主轴转速的交互作用较为明显。利用遗传算法对铣削参数优化后,Ti-6Al-4V表面粗糙度较优化前提高44%,材料去除率提高70%,遗传算法优化后的试件表面粗糙度显著降低,表面刀路行距减小,纹理平均高度降低。结论由实验验证可知,通过响应曲面建立表面粗糙度显著不失拟回归模型具有较高的预测精度,基于遗传算法优化获得的铣削参数可有效提高表面质量和切削效率,对保证航空铸造钛合金Ti-6Al-4V表面质量具有较好的指导意义。     

A genetic algorithmic approach for optimization of surface roughness prediction model

Due to the widespread use of highly automated machine tools in the industry, manufacturing requires reliable models and methods for the prediction of output performance of machining processes. The prediction of optimal machining conditions for good surface finish and dimensional accuracy plays a very important role in process planning. The present work deals with the study and development of a surface roughness prediction model for machining mild steel, using Response Surface Methodology (RSM). The experimentation was carried out with TiN-coated tungsten carbide (CNMG) cutting tools, for machining mild steel work-pieces covering a wide range of machining conditions. A second order mathematical model, in terms of machining parameters, was developed for surface roughness prediction using RSM. This model gives the factor effects of the individual process parameters. An attempt has also been made to optimize the surface roughness prediction model using Genetic Algorithms (GA) to optimize the objective function. The GA program gives minimum and maximum values of surface roughness and their respective optimal machining conditions.     

Experimental evaluation of minimum quantity lubrication in near micro-milling

This paper presents the performance of the minimum quantity lubrication (MQL) technique in near micro-milling with respect to dry cutting on the basis of tool wear, surface roughness and burr formation. The effects of tool materials, oil flow rate and air flow rate on tool performance in MQL cutting are also studied. It is found that the application of MQL will significantly improve the tool life, surface roughness and burr formation compared to those in dry cutting based on slotting tests with micro-end mills on a meso-scale machine tool. It is also observed that the values of surface roughness are close related to the tool-wear conditions in micro-cutting. Based on the experimental results, it is presumed that the maximum allowable tool flank wear of the 600-μm micro-tool is 80 μm while the surface finish quickly deteriorates after the tool flank wear reaches 80 μm and the tool breaks soon after the tool wear reaches 100 μm. The optimal lubrication conditions in this study are oil flow rate of 1.88 ml/h and air flow rate of 40 l/min. It is also found that the air flow rate has a more significant influence on tool life than the oil flow rate under MQL conditions in this study.     

高强铝合金干式切削加工参数多目标优化

为了研究航空铝合金在绝对干式切削条件下的切削参数对表面质量的影响规律并据此选择合理的切削用量,采用正交试验法对7075-T6铝合金进行了车削试验并对试验数据进行处理,得到表面粗糙度、表面残余应力关于切削三要素的多元非线性回归模型。在此基础上,以表面去除率、表面残余应力和表面粗糙度为优化目标,切削三要素为优化对象,基于遗传算法进行多目标优化,并绘制出Pareto前沿。根据实际加工需求,从优化解集中选择最优工艺参数。结果表明：表面粗糙度和表面残余应力均与表面去除率成反比关系;进给量和切削速度是影响表面粗糙度和表面残余应力的主要参数;仅在进给量最小的状态下,切削深度的增加会产生较大的表面残余拉应力。     

高分子添加剂对磨料水射流切割质量的影响

通过磨料水射流和在磨料水射流中加入不同浓度高分子添加剂切割大理石的对比实验,测量了在不同工况下切缝表面不同位置测点的粗糙度。试验结果表明:在相同工况下,高分子添加剂磨料水射流较磨料水射流能减小切缝表面粗糙度,提高切缝表面质量;不同浓度高分子添加剂磨料射流对切缝表面粗糙度影响不一,存在最优浓度为3×10~(-4);磨料水射流切割中,走刀速度过慢和过快时获得切缝表面最小表面粗糙度的靶距较正常走刀速度大;高分子添加剂磨料射流切割中,不同走刀速度下获得切缝最小表面粗糙度的靶距趋向一致。     

Effect of cooling and lubrication conditions on surface topography and turning process of C45 steel

At present coolants and lubricants are increasingly recognized as harmful factors for environment and machine operators’ health. Industry and research institutions are looking for new means of reducing or eliminating the use of cutting fluids, both for economical and ecological reasons. This can be done if quality properties of machined surfaces and process parameters in dry and wet machining are comparable. This paper presents an investigation into the influence of cutting zone cooling and lubrication on surface roughness, waviness, profile bearing ratio and topography after turning C45 steel. Dry cutting and minimum quantity lubrication (MQL) results are compared with conventional emulsion cooling. Cutting forces and their components were put under examination as well. The experimental outcomes indicate that the cooling and lubrication conditions affect significantly the investigated process and surface properties. However, the impact of the cooling and lubricating technique depends to a large extent on the applied cutting parameters, namely the cutting speed and feed rate. Turning dry or with MQL with properly selected cutting parameters makes it possible to produce better surface topography characteristics than turning with conventional emulsion cooling. Apart from improving the surface properties the MQL mode of cooling and lubrication also provides environmental friendliness.     

基于灰色关联分析的GH2132线材高精度切削参数优化

目的 通过无心车床车削去除GH2132线材的表面缺陷,分析无心车床加工参数对线材表面粗糙度、尺寸误差和表面显微硬度的响应关系,并建立GH2132线材表面灰色关联度多目标优化模型,确定可行工艺参数域。方法 采用响应曲面中心复合设计,测量车削后GH2132线材的表面粗糙度、尺寸误差和表面显微硬度;利用响应曲面法(Response Surface Method,RSM)分别建立表面粗糙度、尺寸误差和表面显微硬度的单目标预测模型,确定单目标优化最优工艺参数组;基于灰色关联分析(Grey Correlation Analysis,GRA)理论,以表面粗糙度、尺寸误差和表面显微硬度为优化指标进行降维处理,构建车削工艺参数与灰色关联度的二阶回归预测模型;绘制车削工艺参数与灰色关联度值的等值线图,确定可行工艺参数域。结果 对建立的表面粗糙度、尺寸误差和表面显微硬度的单目标预测模型进行方差分析,显著度均小于0.000 1。得到了最小表面粗糙度工艺参数组,切削速度n=373.919 r/min,进给速度vf =0.475 m/min。得到了最小尺寸误差工艺参数组,n=375.636 r/min,vf =0.596 m/min。得到了最大表面显微硬度工艺参数组,n=337 r/min,vf = 0.903 m/min。对于灰色关联度多目标预测模型,误差范围为0.13%~9.4%,确定的可行工艺参数域对应的最小灰色关联度值为0.544 37。结论 基于灰色关联分析的多目标预测模型的准确度较高,主轴转速n对多目标的响应程度大于进给速度vf。通过确定可行工艺参数域,为GH2132线材去除表面缺陷提供工程参考。     

Theoretical and experimental analysis of nano-surface generation in ultra-precision raster milling

The fabrication of high-quality freeform surfaces is based on ultra-precision raster milling, which allows direct machining of the freeform surfaces with sub-micrometric form accuracy and nanometric surface finish. Ultra-precision raster milling is an emerging manufacturing technology for the fabrication of high-precision and high-quality components with a surface roughness of less than 10 nm and a form error of less than 0.2 μm without the need for any additional post-processing. Moreover, the quality of a raster milled surface is based on a proper selection of cutting conditions and cutting strategies.Due to different cutting mechanics, the process factors affecting the surface quality are more complicated, as compared with ultra-precision diamond turning and conventional milling, such as swing distance and step distance. This paper presents a theoretical and experimental analysis of nano-surface generation in ultra-precision raster milling. Theoretical models for the prediction of surface roughness are built. An optimization system is established based on the theoretical models for the optimization of cutting conditions and cutting strategy in ultra-precision raster milling. A series of experiments have conducted and the results show that the theoretical models predict well the trend of the variation of surface roughness under different cutting conditions and cutting strategies.     

前混合磨料水射流切割参数对表面粗糙度的影响

采用前混合磨料水射流对Q235碳素结构钢进行切割实验,测量样品切口表面粗糙度;研究前混合磨料水射流的切割压力、喷嘴出口直径、切割靶距、切割速度和切割深度对样品切口表面粗糙度的影响规律;结合实验数据,建立表面粗糙度二次非线性回归预测方程。研究结果表明：前混合磨料水射流的切割压力、喷嘴出口直径与表面粗糙度呈负相关关系;切割靶距、切割速度、切割深度与表面粗糙度呈正相关关系;各因素的影响权重大小依次为：喷嘴出口直径、切割深度、切割压力、切割速度、切割靶距;影响表面粗糙度的实质因素为磨料流量和磨料能量;建立的表面粗糙度二次非线性回归预测方程的平均偏差为7.99%。     

面向绿色高表面质量制造的硬态车削工艺多目标参数优化

目的 为了进行硬态车削绿色制造与工艺性能协同优化研究,提出一种同时考虑碳排放量和表面粗糙度的多目标优化方法。方法 首先,通过分析硬态车削过程中切削参数、工件材料、刀具材料等因素对切削功率的影响建立碳排放目标函数,针对工件的表面粗糙度受到切削条件、工件材料、刀具材料等诸多因素的影响,利用正交试验和广义回归神经网络建立轴承硬态车削表面粗糙度目标函数。然后,考虑加工过程中机床特性和硬车实际工况等约束条件,建立以切削参数为优化变量,以碳排放量和表面粗糙度为优化目标的多目标优化模型,引入权重系数将其转化为单目标优化模型。最后,利用遗传算法对优化模型进行优化求解,深入分析切削参数对优化目标的影响。结果 在工厂实际轴承产品硬车试验中验证了优化模型的有效性,结果表明,切削速度为225 m/min、进给量为0.08 mm/r、背吃刀量为0.10 mm时,碳排放量和表面粗糙度的综合优化指标最低。相比优化前,虽然碳排放量上升了13.05%,但表面质量提升了34.44%。结论 研究结果对面向绿色制造的轴承硬车工艺参数优化提供理论方法有重要意义。     

A comprehensive experimental study on surface integrity by end milling Ti–6Al–4V

End milling titanium Ti–6Al–4V has wide applications in aerospace, biomedical, and chemical industries. However, milling induced surface integrity has received little attention. In this study, a series of end milling experiments were conducted to comprehensively characterize surface integrity at various milling conditions. The experimental results have shown that the milled surface shows the anisotropic nature with the range of surface roughness values from 0.6 to 1.0 μm. Surface roughness value increases with feed and radial depth-of-cut (DoC), but has much less variation in the cutting speed range. Compressive residual normal stress occurs in both cutting and feed directions, while the influences of cutting speed and feed on residual stress trend are quite different. The microstructure analysis shows that β phase becomes much smaller and severely deformed in the near surface with the cutting speed, but phase transformation was absent for the milling conditions. The milled surface microhardness is about 70–90% higher than the bulk material in the subsurface.     

二维超声加工淬硬钢的建模与参数优化

利用正交设计方法研究了硬质合金刀具二维超声加工(UEVC)淬硬钢Cr12Mo V时切削用量的三个因素对加工表面粗糙度和切削力的影响,并利用信噪比、方差及贡献率等方法对各因素间的相互作用进行了分析。以切削参数为独立变量,以切削力和表面粗糙度为响应,利用回归分析建立数学模型。实验结果表明:进给量是对表面粗糙度(Ra、Rz)影响最大的因数,贡献率分别为91.8%和88.8%;其次是切削深度,贡献率分别为3.72%和9.77%;对切削力(Fz)影响最大的二个因素是进给量和切削深度,贡献率分别为56.69%和38.46%;切削速度对表面粗糙度、切削力的贡献率均最小。此外,建立的回归方程对Ra、Rz和Fz均有很高的可决系数,分别为91.8%、94.3%和88.2%,说明所建线性回归模型的准确性。     

Predictive modeling of surface roughness and tool wear in hard turning using regression and neural networks

In machining of parts, surface quality is one of the most specified customer requirements. Major indication of surface quality on machined parts is surface roughness. Finish hard turning using Cubic Boron Nitride (CBN) tools allows manufacturers to simplify their processes and still achieve the desired surface roughness. There are various machining parameters have an effect on the surface roughness, but those effects have not been adequately quantified. In order for manufacturers to maximize their gains from utilizing finish hard turning, accurate predictive models for surface roughness and tool wear must be constructed. This paper utilizes neural network modeling to predict surface roughness and tool flank wear over the machining time for variety of cutting conditions in finish hard turning. Regression models are also developed in order to capture process specific parameters. A set of sparse experimental data for finish turning of hardened AISI 52100 steel obtained from literature and the experimental data obtained from performed experiments in finish turning of hardened AISI H-13 steel have been utilized. The data sets from measured surface roughness and tool flank wear were employed to train the neural network models. Trained neural network models were used in predicting surface roughness and tool flank wear for other cutting conditions. A comparison of neural network models with regression models is also carried out. Predictive neural network models are found to be capable of better predictions for surface roughness and tool flank wear within the range that they had been trained.Predictive neural network modeling is also extended to predict tool wear and surface roughness patterns seen in finish hard turning processes. Decrease in the feed rate resulted in better surface roughness but slightly faster tool wear development, and increasing cutting speed resulted in significant increase in tool wear development but resulted in better surface roughness. Increase in the workpiece hardness resulted in better surface roughness but higher tool wear. Overall, CBN inserts with honed edge geometry performed better both in terms of surface roughness and tool wear development.     

基于响应曲面法的钛合金干式车削工艺参数优化

为了提高钛合金干式车削加工质量,采用响应曲面法对主要车削工艺参数进行了优化,以工件表面粗糙度Ra和刀具磨损量VC作为评价指标,设计了切削速度、背吃刀量和进给量三因素的Box-Behnken实验模型。利用方差和拟合残差概率分布分析三因素的显著性及交互作用,并结合实验检验所建表面粗糙度和刀具磨损二阶响应预测模型的有效性。响应曲面法优化后的最佳工艺参数为:切削速度20 m/min、背吃刀量0.1788 mm、进给量0.1 mm/r,此时得到的表面粗糙度和刀具磨损量为1.031μm和155.6μm,与预测值的误差分别为:9.93%和1.58%。结果表明:基于响应曲面法的钛合金干式车削表面粗糙度和刀具磨损量预测模型准确有效。     

Hard turning of an alloy steel on a machine tool with a polymer concrete bed

Finish turning of 39NiCrMo3 alloy steel in the hardened state has been widely investigated under dry, minimum quantity of lubricant and wet cutting conditions, using inserts in ceramic and PCBN materials, on turning centers equipped with polymer concrete and cast iron beds. The progress of workpart surface roughness and tool wear with cutting time has been measured and the results analysed and discussed in detail. It has been observed that dry cutting leads to the lowest values of tool wear and surface roughness, whilst the minimal quantity of lubricant technique does not provide advantages regarding to dry turning. Furthermore, the PCBN inserts are characterised by a longer tool life than the one exhibited by the ceramic inserts. Finally, the outstanding damping and high rigidity of the polymer concrete bed has a beneficial effect on both tool wear and workpiece surface finish.     

Investigation of surface integrity in high speed end milling of a low alloyed steel

Understanding the effects of cutting speed, feed rate and cutting depth on surface integrity is very important for the control of workpiece quality. This paper presents a global experimental study of surface integrity in the case of high speed end milling. In the global term, we include measurements of residual stresses, surface roughness and cutting forces. Our observations and conclusions are mainly concentrated on the effect of depth of cut with a set of constant parameters, such as cutting speed, feed rate, and tool/material couple. This set of constants has been determined using the theory of stability lobes. All experiments have been performed with an electro-spindle equipped with magnetic bearings. The results lead to a good understanding of the influence of cutting conditions on surface integrity in high speed milling of a low alloyed steel. The discussion examines a specific point where the residual stress and residual stress gradient are lowest and also the origin of the residual stress value.     

基于田口法和方差分析的整体叶轮高速铣削参数优化研究

针对整体叶轮高速铣削加工,开展了以切削速度、每齿进给和切削深度为试验因素,以叶片的表面粗糙度和加工时间为试验指标的正交切削试验,应用田口法对试验结果进行分析,初步确定铣削要素对试验指标的影响程度。采用方差分析方法对正交试验结果进行进一步处理,得到各铣削要素对试验指标的贡献率,确定了叶片铣削的最佳参数组合。经过整体叶轮加工验证,采用优化后的切削参数保证了叶片表面粗糙度Ra0.8μm,并且加工效率提高3倍,实现了整体叶轮的高效加工。     

Optimising the AWJ cutting process of ductile materials using nozzle oscillation technique

Striations and roughness on workpiece surfaces produced by abrasive waterjet (AWJ) have been the most persistent problems that stand in the way of wider applications of the technology in industry. This paper presents the experimental investigation on the impact of using nozzle oscillation cutting technique in minimising or reducing these AWJ cut surface irregularities. The technique was used for cutting ductile materials, i.e. mild steel and aluminium, at various traverse speeds, oscillation angles and frequencies of oscillation. The results show that by oscillating the nozzle during cutting, the improvement in surface finish as measured by centre-line average R_a can be obtained by as much as 30%.     

基于正交试验的PCD刀具加工铝合金时切削性能的研究

通过PCD刀具切削铝合金的正交试验,分析刃磨刀具用砂轮的结合剂和切削用量对切削力与工件表面质量的影响。结果表明：进给量与砂轮结合剂对切削力和工件表面粗糙度影响较大,而切削速度与切削深度的影响相对较小,优方案为陶瓷结合剂金刚石砂轮刃磨的PCD刀具在切削用量v=88．45m／min,切削深度αp=0．1mm,进给量f=0．032mm／r时,工件表面粗糙度最小,同时切削力也较小。     

TC11钛合金铣削的表面粗糙度建模及有限元分析

对铣削钛合金TC11的表面粗糙度进行研究。建立表面粗糙度的3种预测模型,分析模型与表面粗糙度测量值的拟合情况,并进行信噪比S/N分析和ANVOA分析,得到了各切削参数对表面粗糙度的影响程度的大小以及最佳切削参数组合。提出了采用有限元仿真铣削工件表面的位移大小,把表面的轮廓算数平均偏差作为表面粗糙度评定参数的方法。仿真结果与试验结果基本一致,表明了该方法的可行性及有效性。