基于表面粗糙度预测的数控车削加工物理仿真模型的研究

通过分析数控加工中表面粗糙度的产生原因,提出了表面粗糙度仿真系统模型的结构,建立了切削力、振动和表面粗糙度的数学模型,为通过物理仿真预测加工表面粗糙度提供了理论依据。     

Optimization of drilling parameters on surface roughness in drilling of AISI 1045 using response surface methodology and genetic algorithm

Modeling and optimization of cutting parameters are one of the most important elements in machining processes. The present study focused on the influence machining parameters on the surface roughness obtained in drilling of AISI 1045. The matrices of test conditions consisted of cutting speed, feed rate, and cutting environment. A mathematical prediction model of the surface roughness was developed using response surface methodology (RSM). The effects of drilling parameters on the surface roughness were evaluated and optimum machining conditions for minimizing the surface roughness were determined using RSM and genetic algorithm. As a result, the predicted and measured values were quite close, which indicates that the developed model can be effectively used to predict the surface roughness. The given model could be utilized to select the level of drilling parameters. A noticeable saving in machining time and product cost can be obtained by using this model.     

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 .     

Three-dimensional modeling of roughness effects on microthickness filling in injection mold cavity

Effects of cavity surface roughness on filling polymer flow in macrocavities are generally not significant. However, in a microcavity, surface roughness may become very important and ignoring it could lead to inaccurate or even misleading results. As an extension to our previous work, a three-dimensional roughness model for filling polymer flow in microinjection molding is proposed that takes into consideration the cavity surface roughness effects. A numerical procedure, which implements the finite volume and level set methods and integrates the proposed roughness model, is presented. The numerical model was validated by comparing its predicted results with experimental and/or analytical results. The numerical results obtained using the three-dimensional roughness model were in good agreement with experimental observations and were more accurate than those obtained using the two-dimensional roughness model.     

Application of Taguchi and response surface methodologies for surface roughness in machining glass fiber reinforced plastics by PCD tooling

This paper discusses the use of Taguchi and response surface methodologies for minimizing the surface roughness in machining glass fiber reinforced (GFRP) plastics with a polycrystalline diamond (PCD) tool. The experiments have been conducted using Taguchi’s experimental design technique. The cutting parameters used are cutting speed, feed and depth of cut. The effect of cutting parameters on surface roughness is evaluated and the optimum cutting condition for minimizing the surface roughness is determined. A second-order model has been established between the cutting parameters and surface roughness using response surface methodology. The experimental results reveal that the most significant machining parameter for surface roughness is feed followed by cutting speed. The predicted values and measured values are fairly close, which indicates that the developed model can be effectively used to predict the surface roughness in the machining of GFRP composites. The predicted values are confirmed by using validation experiments.     

轴向超声辅助端面磨削金属表面形貌及粗糙度预测

轴向超声辅助端面磨削被广泛应用于难加工材料加工,而磨削后的表面粗糙度对构件摩擦、疲劳等服役性能有重要影响。超声振幅的大小对轴向超声辅助端面磨削金属表面形貌和粗糙度有较大影响,但是现有模型中并未考虑实际加载对振幅的影响,因此提出了一种考虑加载状态下振幅变化的轴向超声辅助端面磨削金属表面形貌及粗糙度预测方法。根据砂轮粒度及尺寸建立了考虑磨粒随机分布的砂轮端面模型,并对轴向超声辅助端面磨削磨粒的三维磨削轨迹进行了数学描述,生成了加工后的表面三维数据矩阵并对表面粗糙数值进行了计算。在此基础上,研究了粗糙度随振幅的变化规律,提出了振幅衰减形貌映射系数这一概念,并给出了其标定方法。通过振幅衰减形貌映射系数近似计算出加载状态下的振幅并代入到所建立的轴向超声辅助端面磨削表面形貌及粗糙度预测模型中,实现了金属表面形貌模拟及粗糙度预测。最后,通过试验对所建模型的正确性进行了验证。     

OPTIMAL MACHINING CONDITIONS FOR TURNING OF PARTICULATE METAL MATRIX COMPOSITES USING TAGUCHI AND RESPONSE SURFACE METHODOLOGIES

The present investigation focuses on the influence of machining parameters on the surface finish obtained in turning of LM25 Al/SiC particulate composites. The experiments are conducted based on Taguchi's experimental design technique. In this work, the effect of machining parameters on the surface roughness is evaluated and optimum machining conditions for maximizing the metal removal rate and minimizing the surface roughness are determined using response surface methodology. A second-order response surface model for the surface roughness is developed to predict the surface roughness. The predicted values and measured values are fairly close to each other, which indicates that the developed model can be effectively used to predict the surface roughness on the machining of Al/SiC-MMC composites with 95% confidence intervals within the ranges of parameters studied.     

OPTIMAL MACHINING CONDITIONS FOR TURNING OF PARTICULATE METAL MATRIX COMPOSITES USING TAGUCHI AND RESPONSE SURFACE METHODOLOGIES

The present investigation focuses on the influence of machining parameters on the surface finish obtained in turning of LM25 Al/SiC particulate composites. The experiments are conducted based on Taguchi's experimental design technique. In this work, the effect of machining parameters on the surface roughness is evaluated and optimum machining conditions for maximizing the metal removal rate and minimizing the surface roughness are determined using response surface methodology. A second-order response surface model for the surface roughness is developed to predict the surface roughness. The predicted values and measured values are fairly close to each other, which indicates that the developed model can be effectively used to predict the surface roughness on the machining of Al/SiC-MMC composites with 95% confidence intervals within the ranges of parameters studied.     

基于遗传算法与BP神经网络的微晶玻璃点磨削工艺参数优化

通过低膨胀微晶玻璃点磨削实验,测试了加工表面粗糙度、表面硬度,分析了实验数据变化趋势。通过最小二乘拟合,建立了关于粗糙度、表面硬度的一元数值模型,并将模型预测值与实验值进行了比较,以验证模型的精确性,结果表明模型具有较高的精度。根据正交实验结果,基于BP神经网络算法和遗传算法,建立了粗糙度、表面硬度的多元数值模型并以此作为目标函数,以表面硬度最大和表面粗糙度最小作为优化目标,基于遗传算法进行了工艺参数的双目标优化,获得了一组点磨削工艺参数的最优解范围,实验验证结果表明优化结果是合理的。     

Optimisation of machining parameters for turning operations based on response surface methodology

Design of experiments has been used to study the effect of the main turning parameters such as feed rate, tool nose radius, cutting speed and depth of cut on the surface roughness of AISI 410 steel. A mathematical prediction model of the surface roughness has been developed in terms of above parameters. The effect of these parameters on the surface roughness has been investigated by using Response Surface Methodology (RSM). Response surface contours were constructed for determining the optimum conditions for a required surface roughness. The developed prediction equation shows that the feed rate is the main factor followed by tool nose radius influences the surface roughness. The surface roughness was found to increase with the increase in the feed and it decreased with increase in the tool nose radius. The verification experiment is carried out to check the validity of the developed model that predicted surface roughness within 6% error.     

基于遗传算法工具箱的精密车削表面粗糙度预测

通过优化切削参数(切削速度、进给量和切削深度)可以获得精密车削马氏体3J33最佳表面粗糙度。切削试验采用二次旋转组合设计的方法,使用谢菲尔大学研究的遗传算法工具箱所获得的在特定约束条件下表面粗糙度预测模型,应用遗传算法对切削用量对表面粗糙度的优化计算,并将其结果和非线性优化计算结果进行比较,两种计算结果基本吻合。本文的目的在于在特定切削参数条件下预测表面粗糙度。     

Construction of a surface roughness prediction model for high speed machining

In manufacturing environment prediction of surface roughness is very important for product quality and production time. For this purpose, the finite element method and neural network is coupled to construct a surface roughness prediction model for high-speed machining. A finite element method based code is utilized to simulate the high-speed machining in which the cutting tool is incrementally advanced forward step by step during the cutting processes under various conditions of tool geometries (rake angle, edge radius) and cutting parameters (yielding strength, cutting speed, feed rate). The influences of the above cutting conditions on surface roughness variations are thus investigated. Moreover, the abductive neural networks are applied to synthesize the data sets obtained from the numerical calculations. Consequently, a quantitative prediction model is established for the relationship between the cutting variables and surface roughness in the process of high-speed machining. The surface roughness obtained from the calculations is compared with the experimental results conducted in the laboratory and with other research studies. Their agreements are quite well and the accuracy of the developed methodology may be verified accordingly. The simulation results also show that feed rate is the most important cutting variable dominating the surface roughness state.     

多尺度粗糙度对波导传输性能的影响

文中针对波导内壁存在的粗糙度对波导传输性能的影响问题进行了建模、仿真研究。首先,基于改进的二维分形函数,建立了波导内壁粗糙度的多尺度数学模型。基于此模型,以数值积分和有限元法为数学工具,分别推导出了考虑粗糙度影响的波导内壁局部和整体的功率损耗公式。作为对理论结果的验证,进行了一个针对矩形波导的仿真实验。分别将二维分形函数、多尺度粗糙度模型和三角波作为粗糙度的模拟函数,将得到的功率损耗结果与用经典文献中提供的计算方法得到的结果进行对比,结果表明本文所建立的多尺度粗糙度模型的结果在高频时更接近经典文献数据。随后,又对多尺度粗糙度模型的两个主要参数对功率损耗的影响程度进行了对比,结果显示粗糙度表面密度对功率损耗的影响较小,而分维数的影响较大。随着表面粗糙度均方根值的增大,功率损耗的比值也逐渐增加,最大值可达光滑表面功率损耗的1.8倍。     

Modeling and evaluating of surface roughness prediction in micro-grinding on soda-lime glass considering tool characterization

The current research of micro-grinding mainly focuses on the optimal processing technology for different materials. However, the material removal mechanism in micro-grinding is the base of achieving high quality processing surface. Therefore, a novel method for predicting surface roughness in micro-grinding of hard brittle materials considering micro-grinding tool grains protrusion topography is proposed in this paper. The differences of material removal mechanism between convention grinding process and micro-grinding process are analyzed. Topography characterization has been done on micro-grinding tools which are fabricated by electroplating. Models of grain density generation and grain interval are built, and new predicting model of micro-grinding surface roughness is developed. In order to verify the precision and application effect of the surface roughness prediction model proposed, a micro-grinding orthogonally experiment on soda-lime glass is designed and conducted. A series of micro-machining surfaces which are 78 nm to 0.98 ~tm roughness of brittle material is achieved. It is found that experimental roughness results and the predicting roughness data have an evident coincidence, and the component variable of describing the size effects in predicting model is calculated to be 1.5x 107 by reverse method based on the experimental results. The proposed model builds a set of distribution to consider grains distribution densities in different protrusion heights. Finally, the characterization of micro-grinding tools which are used in the experiment has been done based on the distribution set. It is concluded that there is a significant coincidence between surface prediction data from the proposed model and measurements from experiment results. Therefore, the effectiveness of the model is demonstrated. This paper proposes a novel method for predicting surface roughness in micro-grinding of hard brittle materials considering micro-grinding tool grains protrusion topography, which would provide significant research theory and experimental reference of material removal mechanism in micro-grinding of soda-lime glass.     

Preliminary investigation of the effect of roughness in Dynarat simulation

Surface roughness has a significant effect on how loads are transmitted at the contact interface between solid bodies. It causes high local pressures in the contacting roughness peaks, i.e., asperities. Even for a low friction coefficient the surface roughness will still play an important role in the early surface wear. A dynamic ratcheting model (Dynarat) for studying the wear rate of ductile materials in rolling/sliding contact is presented. The material is divided into equal-sized rectangular elements (or ‘bricks’). Each material brick accumulates plastic shear strain when the orthogonal shear stress exceeds the brick's shear yield stress. When the accumulated plastic strain exceeds a critical value, the ductility is exhausted and the material is deemed to have failed. Inclusion of surface roughness and refinement of the near-surface brick size cause earlier failure of bricks very close to the surface. In order to model surface roughness, brick size needs to be reduced to, at most, a few microns. The purpose of this investigation is to study the effect of surface roughness in the Dynarat model by comparing the model prediction with the results from two different rolling sliding wear testing machines. Further development of the model is needed, such as more inclusive representations of microstructural behaviour. In addition to that, the ratcheting equation, which drives the Dynarat model, needs to be improved to cover other rail materials and more loading configurations.     

基于ANFIS的铝合金铣削加工表面粗糙度预测模型研究

分析以往建立表面粗糙度预测模型方法的不足，采用自适应神经模糊推理系统(ANFIS)建立了铝合金铣削加工表面粗糙度预测模型。经检验，该模型预测精度高，泛化能力强，且可简便预测铣削参数对已加工表面的表面粗糙度的影响，有助于准确认识已加工表面质量随铣削参数的变化规律，为切削参数的优选和表面质量的控制提供了依据。     

A Two-Wavelength Model of Surface Flattening in Cold Metal Rolling with Mixed Lubrication

A new model of surface flattening is developed for cold metal rolling in the mixed regime. Longitudinal surface roughness is modeled by two separate wavelengths. The new model follows the asperity crushing analysis of Sutcliffe (1999) for unlubricated rolling but additionally includes a hydrodynamic model to account for the effect of the lubricant. The effect of various parameters including speed, reduction in strip thickness, roughness wavelength and lubricant properties is examined. The results show similar behavior to previous models of mixed lubrication, with a speed parameter A_s having the most influence, and confirm the results for unlubricated rolling that the short wavelength components of the surface roughness persist more than the long wavelength components. The predicted changes in roughness are in good agreement with experiments.     

A numerical model of an oscillating squeeze film between a rubber surface and a rigid surface

A numerical model for the hydrodynamic behavior of an oscillating squeeze film between a rubber surface and a rigid surface is presented. The effects of roughness of the rubber surface on the hydrodynamic force and the leakage flow rate in the squeeze film are analyzed. A modified Reynolds equation, Laplace equation and a three-parameter viscoelastic constitutive equation are solved simultaneously to obtain the pressure distribution in the squeeze film and the deformation of the rubber surface. Equations are discretized into finite difference equations and solved by Gauss-Siedel iteration method. It is found that increasing roughness of the surface profile significantly increases the hydrodynamic force accompanied by a small decrease in the leakage flow rate. Spatial distribution of the roughness of the rubber surface has no significant effect on the leakage flow rate or hydrodynamic force. The results obtained from the presented simple model are compared with the experimental results available in the literature and a very good agreement is found.     

Formation of iron aluminide coatings on plain carbon steel by TIG process

Micro end-milling is widely used in many industries to produce micro products with complex 3D shapes. The accurate modeling and prediction of surface roughness are important for evaluating the productivity of the machine tools and the surface quality of the machined parts. This paper presents an accurate surface roughness model based on the kinematics of cutting process and tool geometry by considering the effects of tool run-out and minimum chip thickness. The proposed surface roughness model is validated by micro end-milling experiments with the miniaturized machine tool. The results show that the proposed surface roughness model can accurately predict both the trends and magnitude of the surface roughness in micro end-milling.