Analyzing the design of vibration reduction with the rubber-layered laminates in the precision turning with a diamond cutting tool

Using a diamond cutting tool in the precision turning process, the vibration of tool-tip has an undesirable effect on the machined surface??s quality. The objective of this paper is to analyze the design of turning tool-bar combined with the rubber-layered laminates for minimizing the vibration amplitude of tool-tip in the precision turning with the diamond tool. The selected rubber materials are styrene butadiene rubber (SBR) and silicone rubber (SI). Machining parameters, including the spindle speed, feed rate, and cutting depth, were chosen as numerical factors, and the status of the rubber-layered laminates was regarded as the categorical factor. The status of the rubber-layered laminates set up three categories including the solid tool (without rubber-layered laminates), tool with SBR rubber-layered laminate, and tool with SI rubber-layered laminate. An experimental plan of a four-factor (three numerical plus one categorical) D-optimal design based on the response surface methodology was employed to carry out the experimental study. The results show that the design of the turning tool-bar combined with the rubber-layered laminates is proven to improve the damping forces of the turning tool-bar. The overall vibration on the tool-tip using the tool with the rubber-layered laminates tends to be in a more stable condition, which leads to the result of having the best machined surface. With experimental values up to a 95% confidence interval, it is fairly well for the experimental results to present the mathematical models of the surface roughness without/with the rubber-layered laminates.     

Experimental investigation of tool vibration and surface roughness in the precision end-milling process using the singular spectrum analysis

The vibrations on the cutting tool have a momentous influence for the surface quality of workpiece with respect to surface profile and roughness during the precision end-milling process. Singular spectrum analysis (SSA) is a new non-parametric technique of time series analysis and forecasting. The significant features of the cutting tool vibration signals from the sensors are extracted and transformed from the SSA-processed vibration signals. In the present study, SSA is applied to extract and transform the raw signals of the vibrations on the cutting tool for investigating the relationship between tool vibration and surface roughness in the precision end-milling process of hardened steel SCM440. In this experimental investigation, the spindle speed, feed rate, and cutting depth were chosen as the numerical factor; the cutting feed direction and holder type were regarded as the categorical factor. An experimental plan consisting of five-factor (three numerical plus two categorical) d-optimal design based on the response surface methodology was employed to carry out the experimental study. A micro-cutting test was conducted to visualize the effect of vibration of tooltip on the performance of surface roughness. With the experimental values up to 95% confidence interval, it is fairly well for the experimental results to present the mathematical models of the tool vibration and surface roughness. Results show that the effects of feed rate and cutting depth provide the reinforcement on the overall vibration to cause the unstable cutting process and exhibit the result of the worst machined surface. The amplitude of vibration signals along the cutting feed direction is generally larger than that along other direction. The spindle speed and tool holder type affect the stability of cutting tooltip during the cutting process.     

SURFACE ROUGHNESS PREDICTION MODEL FOR ULTRAPRECISION TURNING ALUMINIUM ALLOY WITH A SINGLE CRYSTAL DIAMOND TOOL

A surface roughness model utilizing regression analysis method is developed for predicting roughness of ultra-precision machined surface with a single crystal diamond tool. The effects of the main variables, such as cutting speed, feed, and depth of cut on surface roughness are also analyzed in diamond turning aluminum alloy. In order to predict and control the surface roughness before ultraprecision machining, constrained variable metric method is used to select the optimum cutting conditions during process planning. A lot of experimental results show that the model can predict the surface roughness effectively under a certain cutting conditions .     

精密车削LY12铝合金的振动特性分析

利用超精密车床精密车削LY12铝合金,研究了切削速度、进给量和切削深度对主轴和刀具切削振动的影响及变化规律。试验结果表明主轴的振动随切削速度的增加而平稳增加,在高速时成为主要振动,而刀具在低速时振动很大,随着切削速度的增加而迅速降低。因此在低速时,刀具的振动是影响加工质量的主要因素;在高速时,主轴振动对加工质量的影响愈来愈大;在小进给时,各向的振动随进给的增加而迅速减少,降到临界值后,各通道振动又随进给增加而保持平稳;主轴的振动随切深的增加变化很小,而刀具的振动随切深的增加而增加,但增加到临界值,刀具的振动也保持平稳。     

应用经济型数控车床研究不锈钢车削力和加工质量

应用经济型数控车床加工SUS304不锈钢,研究切削过程中刀具各个方向受力以及零件加工表面的粗糙度分布趋势。采用单因素试验方法设计车削力试验,并记录加工过程中的切削力以及对应参数下获得的零件表面粗糙度。研究发现,不锈钢切削时,切削力和粗糙度都随着主轴转速增加而略有增大,随着进给量和切削增加而明显增大,并且切削力和粗糙度之间存在弱负相关关系,两个指标都与材料去除率呈正比例变化。     

金刚石厚膜刀具切削性能的研究

制备了机夹式金刚石厚膜刀具 ,通过切削试验研究了金刚石厚膜刀具精密车削LY12铝合金时切削用量 (进给速度vf、切削速度v和切削深度ap)对切削表面粗糙度的影响     

基于响应曲面法的表面粗糙度预测模型研究

通过正交试验,研究了高速端铣加工中切削参数对表面粗糙度的影响。采用田口设计方法和响应曲面法构建了表面粗糙度预测模型,分析了主轴转速、进给量、切深对表面粗糙度的影响。结果显示,进给量对表面粗糙度的影响最显著,主轴转速次之,切深的影响不大。模型预测精度为99．84％,达到了较高的预测水平。     

金刚石磨头刀具磨铣加工对高体份SiCp/Al复合材料加工表面质量的影响

针对高体份SiCp／Al复合材料,采用佥刚石磨头刀具磨铣切削的加工方法,研究了高速磨铣加工中机床主轴转速、工件进给速度及背吃刀量对材料加工表面形貌损伤以及表面粗糙度的影响规律。研究表明,机床主轴转速的提高、工件进给速度的减小都能够减小材料表面形貌的损伤情况,改善加工表面粗糙度质量：背吃刀量的改变对材料表面形貌损伤以及表面粗糙度的影响不大。     

Modeling and analyzing the effects of air-cooled turning on the machinability of Ti–6Al–4V titanium alloy using the cold air gun coolant system

This study provides the mathematical models for modeling and analyzing the effects of air-cooling on the machinability of Ti–6Al–4V titanium alloy in the hard turning process. A cold air gun coolant system was used in the experiments and produced a jet of compressed cold air for cooling the cutting process. The air-cooling process seems to be a good environment friendly option for the hard turning. In this experimental investigation, the cutting speed, feed rate and cutting depth were chosen as the numerical factor; the cooling method was regarded as the categorical factor. An experimental plan of a four-factor (three numerical plus one categorical) D-optimal design based on the response surface methodology (RSM) was employed to carry out the experimental study. The mathematical models based on the RSM were proposed for modeling and analyzing the cutting temperature and surface roughness in the hard turning process under the dry cutting process and air-cooling process. Tool wear and chip formation during the cutting process were also studied. The compressed cooling air in the gas form presents better penetration of the lubricant to the cutting zone than any conventional coolants in the cutting process do. Results show that the air-cooling significantly provides lower cutting temperature, reduces the tool wear, and produces the best machined surface. The machinability performance of hard turning Ti–6Al–4V titanium alloy on the application of air-cooling is better than the application of dry cutting process. This air-cooling cutting process easily produces the wrinkled and breaking chips. Consequently, the air-cooled cutting process offers the attractive alternative of the dry cutting in the hard turning process.     

Multi response optimisation of CNC turning parameters via Taguchi method-based response surface analysis

This study presents a new method to determine multi-objective optimal cutting conditions and mathematic models for surface roughness (Ra and Rz) on a CNC turning. Firstly, cutting parameters namely, cutting speed, depth of cut, and feed rate are designed using the Taguchi method. The AISI 304 austenitic stainless workpiece is machined by a coated carbide insert under dry conditions. The influence of cutting speed, feed rate and depth of cut on the surface roughness is examined. Secondly, the model for the surface roughness, as a function of cutting parameters, is obtained using the response surface methodology (RSM). Finally, the adequacy of the developed mathematical model is proved by ANOVA. The results indicate that the feed rate is the dominant factor affecting surface roughness, which is minimized when the feed rate and depth of cut are set to the lowest level, while the cutting speed is set to the highest level. The percentages of error all fall within 1%, between the predicted values and the experimental values. This reveals that the prediction system established in this study produces satisfactory results, which are improved performance over other models in the literature. The enhanced method can be readily applied to different metal cutting processes with greater confidence.     

单点金刚石切削加工表面粗糙度的影响因素分析

本文研究了单点金刚石切削加工表面微观形貌形成机理,建立了圆弧刃金刚石刀具超精密加工表面微观形貌的理论模型,重点分析了主轴转速、进给量、刀尖圆弧半径和振动等因素对超精密加工表面粗糙度的影响。     

A multi-spectrum analysis of surface roughness formation in ultra-precision machining

The formation of surface roughness in ultra-precision diamond turning is investigated using a multi-spectrum analysis method. The features on a diamond turned surface are extracted and analyzed by the spectrum analysis of its surface roughness profiles measured at a finite number of radial sections of the turned surface. It is found that the tool feed rate, the spindle rotational speed, the tool geometry, the material properties, as well as the relative tool-work vibration are not the only dominant components contributing to the generation of surface roughness. The material induced vibration caused by the variation of material crystallography is another major factor. The vibration causes a significant variation of the frequency of the surface modulation of the machined surface. With the use of the multi-spectrum analysis method, it is possible to conjecture the patterns of this vibration as well as to evaluate the properties of the workpiece materials.     

On the prediction of surface roughness in the hard turning based on cutting parameters and tool vibrations

This research work concerns the elaboration of a surface roughness model in the case of hard turning by exploiting the response surface methodology (RSM). The main input parameters of this model are the cutting parameters such as cutting speed, feed rate, depth of cut and tool vibration in radial and in main cutting force directions. The machined material tested is the 42CrMo4 hardened steel by Al₂O₃/TiC mixed ceramic cutting tool under different conditions. The model is able to predict surface roughness of Ra and Rt using an experimental data when machining steels. The combined effects of cutting parameters and tool vibration on surface roughness were investigated while employing the analysis of variance (ANOVA). The quadratic model of RSM associated with response optimization technique and composite desirability was used to find optimum values of cutting parameters and tool vibration with respect to announced objectives which are the prediction of surface roughness. The adequacy of the model was verified when plotting the residuals values. The results indicate that the feed rate is the dominant factor affecting the surface roughness, whereas vibrations on both pre-cited directions have a low effect on it. Moreover, a good agreement was observed between the predicted and the experimental surface roughness. Optimal cutting condition and tool vibrations leading to the minimum surface roughness were highlighted.     

Cutting tool condition monitoring by analyzing surface roughness,work piece vibration and volume of metal removed for AISI 1040 steel in boring

The vibration is one of the intensive problems in boring process. Machining and tool wear are affected more by vibration of tool due to length of boring bar. The present work is to estimate the effect of cutting parameters on work piece vibration, roughness on machined surface and volume of metal removed in boring of steel (AISI1040). A laser Doppler vibrometer (LDV) was used for online data acquisition and a high-speed FFT analyzer used to process the AOE signals for work piece vibration. A design of experiments was prepared with eight experiments with two levels of cutting parameters such as spindle rotational speed, feed rate and tool nose radius. Taguchi method has been used to optimize the cutting parameters and a multiple regression analysis is done to obtain the empirical relation of Tool life with roughness of machined surface, volume of metal removed and amplitude of work piece vibrations.     

Prediction of surface roughness in end face milling based on Gaussian process regression and cause analysis considering tool vibration

Surface roughness is a technical requirement for machined products and one of the main product quality specifications. In order to avoid the costly trial-and-error process in machining parameters determination, the Gaussian process regression (GPR) was proposed for modeling and predicting the surface roughness in end face milling. Cutting experiments on C45E4 steel were conducted and the results were used for training and verifying the GPR model. Three parameters, spindle speed, feed rate, and depth of cut were considered; the experiment results showed that depth of cut is the main factor affecting the surface roughness and regression results showed that the GPR model has a good precision in predicting the surface roughness in different cutting conditions. The prediction accuracy was nearly about 84.3 %. Based on the GPR prediction model, 3D-maps of surface roughness under various cutting parameters could be obtained. It is very concise and useful to select the appropriate cutting parameters according to the maps. As experimental results did not conform to the empirical knowledge, frequency spectrums of the tool were analyzed according to the 3D-maps, it was found that tool vibration is also a crucial factor affecting the machined surface quality.     

Optimization of cutting conditions for minimum residual stress,cutting force and surface roughness in end milling of S50C medium carbon steel

Residual stresses are usually imposed on a machined component due to thermal and mechanical loading. Tensile residual stresses are detrimental as it could shorten the fatigue life of the component; meanwhile, compressive residual stresses are beneficial as it could prolong the fatigue life. Thermal and mechanical loading significantly affect the behavior of residual stress. Therefore, this research focused on the effects of lubricant and milling mode during end milling of S50C medium carbon steel. Numerical factors, namely, spindle speed, feed rate and depth of cut and categorical factors, namely, lubrication and milling mode is optimized using D-optimal experimentation. Mathematical model is developed for the prediction of residual stress, cutting force and surface roughness based on response surface methodology (RSM). Results show that minimum residual stress and cutting force can be achieved during up milling, by adopting the MQL-SiO₂ nanolubrication system. Meanwhile, during down milling minimum residual stress and cutting force can be achieved with flood cutting. Moreover, minimum surface roughness can be attained during flood cutting in both up and down milling. The response surface plots indicate that the effect of spindle speed and feed rate is less significant at low depth of cut but this effect significantly increases the residual stress, cutting force and surface roughness as the depth of cut increases.     

Analysis of surface roughness and cutting force components in hard turning with CBN tool: Prediction model and cutting conditions optimization

In this study, the effects of cutting speed, feed rate, workpiece hardness and depth of cut on surface roughness and cutting force components in the hard turning were experimentally investigated. AISI H11 steel was hardened to (40; 45 and 50) HRC, machined using cubic boron nitride (CBN 7020 from Sandvik Company) which is essentially made of 57% CBN and 35% TiCN. Four-factor (cutting speed, feed rate, hardness and depth of cut) and three-level fractional experiment designs completed with a statistical analysis of variance (ANOVA) were performed. Mathematical models for surface roughness and cutting force components were developed using the response surface methodology (RSM). Results show that the cutting force components are influenced principally by the depth of cut and workpiece hardness; on the other hand, both feed rate and workpiece hardness have statistical significance on surface roughness. Finally, the ranges for best cutting conditions are proposed for serial industrial production.     

微铣金属表面微沟槽结构的粗糙度及形貌分析

为了提高和改善微沟槽表面质量,设计了高速微铣削实验,研究了微沟槽底面表面粗糙度和侧壁残留毛刺的变化规律。从理论角度引入了已加工表面的形成机理,建立了微观表面粗糙度理论模型,提出了刀具跳动对侧壁形貌变化影响的规律。利用三轴联动精密微细铣削机床加工微细直沟槽,并选取主轴转速、轴向切深、进给速度、刀具跳动量和材料组织结构为研究因素。采用多因素正交实验和极差分析法,对表面粗糙度值进行数值分析。铝合金,钢和钛合金三类微沟槽底面对应的最佳表面粗糙度值变化范围分别为1.073~1.481 μm,0.485~0.883 μm,0.235~0.267 μm;无刀具跳动钛合金微沟槽壁毛刺的最大高度为7.637 μm,而当刀具存在0.3 μm的径向综合跳动量时对应的微槽壁毛刺的最大高度为21.79 μm。铣削参数对表面粗糙度值的影响按从大到小依次为进给速度、主轴转速、轴向切深,且随着进给速度和轴向切深的增大,表面粗糙度值增大;随着主轴转速的增大,表面粗糙度值先减小后增大;在相同加工条件下,若微圆弧刀刃无磨损,微刀具的跳动量对微直沟槽侧壁表面质量有较大影响。同时,不同金属材料特性也是影响微沟槽表面质量的潜在因素。     

微细铣削硬铝时切削用量对表面粗糙度的影响

利用自研的三轴微小型立式数控铣床和微径立铣刀(直径小于1mm),通过双因素及中心复合实验设计的方法,分析微细铣削硬铝LY12过程中的铣削参数(包括每齿进给量、轴向切深、主轴转速、刀具直径以及刀具悬伸量)对工件表面粗糙度的影响,重点探讨了每齿进给量和轴向切深对表面粗糙度的交互影响,并建立了数学模型,为微细铣削工艺参数的选择和表面质量的控制提供了基本依据。     

In-process surface roughness prediction system using cutting vibrations in turning

Machining is a complex process in which many variables can affect the desired results. Among them, surface roughness is a widely used index of a machined product quality and, in most cases, is a technical requirement for mechanical products since, together with dimensional precision, it affects the functional behavior of the parts during their useful life, especially when they have to be in contact with other materials. In-process surface roughness prediction is, thus, extremely important. In this work, an in-process surface roughness estimation procedure, based on least-squares support vector machines, is proposed for turning processes. The cutting conditions (feed rate, cutting speed, and depth of cut), parameters of tool geometry (nose radius and nose angle), and features extracted from the vibration signals constitute the input information to the system. Experimental results show that the proposed system can predict surface roughness with high accuracy in a fast and reliable way.