高速铣削过程中表面粗糙度变化规律的试验研究

在高速铣削试验的基础上 ,研究分析切削速度与进给量对加工表面粗糙度的影响。试验数据表明 ,切削速度的提高有利于改善加工表面粗糙度 ,当切削速度超过某一范围后 ,随切削速度的进一步提高 ,加工表面粗糙度的降低并不明显 ,有时还会使表面粗糙度增加。根据试验结果 ,对具体工件材料与刀具材料匹配选择合理的切削速度与进给量范围 ,可以获得最小加工表面粗糙度值     

凸圆弧前刀面铣刀头超精铣削时主要参数对粗糙度及残留面积的影响

通过详细推算,给出凸圆弧前刀面金刚石铣刀头超精铣削平面时的理论表面粗糙度和切削残留面积的计算公式.利用所得的公式,进一步分析了凸圆弧半径、走刀、后角及安装误差角等主要参数对它们的影响.其Ra值可达几个纳米、P-V值达亚微米级.     

高速铣削过程中表面粗糙度变化规律的试验研究

在高速铣削试验的基础上,研究切削速度与进给量对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.     

铣削加工粗糙度的智能预测方法

提出了一种基于最小二乘支持向量机的铣削加工表面粗糙度智能预测方法.首先进行了铣削工艺参数对工件表面粗糙度影响的正交实验,再通过对主轴转速、进给速率和切削深度三因素,以及各因素之间交互三水平实验的数据分析,找出了铣削工艺参数对工件表面粗糙度影响的一些规律.利用最小二乘支持向量机算法建立了铣削预测模型,通过该模型能在有限实验基础上利用工艺参数方便地得到粗糙度预测值.实际预测表明,在相同情况下,该模型构造速度比反向传播神经网络建模预测方法高2个～3个数量级,预测精度高10倍左右.     

Optimization of surface roughness in end milling Castamide

Castamide is vulnerable to humidity up to 7%; therefore, it is important to know the effect of processing parameters on Castamide with and without humidity during machining. In this study, obtained quality of surface roughness of Castamide block samples prepared in wet and dry conditions, which is processed by using the same cutting parameters, were compared. Moreover, an artificial neural network (ANN) modeling technique was developed with the results obtained from the experiments. For the training of ANN model, material type, cutting speed, cutting rate, and depth of cutting parameters were used. In this way, average surface roughness values could be estimated without performing actual application for those values. Various experimental results for different material types with cutting parameters were evaluated by different ANN training algorithms. So, it aims to define the average surface roughness with minimum error by using the best reliable ANN training algorithm. Parameters as cutting speed (V _c), feed rate (f), diameter of cutting equipment, and depth of cut (a _p) have been used as the input layers; average surface roughness has been also used as output layer. For testing data, root mean squared error, the fraction of variance (R ²), and mean absolute percentage error were found to be 0.0681%, 0.9999%, and 0.1563%, respectively. With these results, we believe that the ANN can be used for prediction of average surface roughness.     

基于声发射信号的铣削加工表面粗糙度预测技术研究

使用声发射技术对铣削过程进行监测,通过对声发射信号进行频域分析,比较不同频段的能量比来在线预测加工后的表面粗糙度.     

A dynamic surface roughness model for face milling

This paper presents a newly developed mathematical model for surface roughness prediction in a face-milling operation. The model considers the static and the dynamic components of the cutting process. The former includes of cutting conditions as well as the edge profile and the amount of runout of each insert set into a cutter body. The latter introduces the dynamic characteristics of the milling process. It is verified that such a model predicts the maximum or the arithmetic mean surface roughness value through the cutting experiments. The model can evaluate the surface texture of the precision parts machined with face milling.     

0Cr18Ni9不锈钢焊缝高速铣削表面粗糙度试验研究

采用正交试验方法进行0Cr18Ni9不锈钢焊缝高速铣削试验,分析铣削参数对表面粗糙度的影响规律,并建立表面粗糙度的经验预测模型。结果表明:随着主轴转速的增大,表面粗糙度值减小,而随着进给速度、切削深度的增大,表面粗糙度值增大,进给速度对表面粗糙度的影响较为明显;较高的主轴转速和较低的进给速度的交互作用有利于减小表面粗糙度值,提高表面质量。     

A new representation with probability distribution for nanometric surface roughness in ultra-precision machining

Ultra-precision machining (UPM) commonly produces nanometric surface roughness (NSR), which is governed by high-frequency components with tool marks sensitive to noise. Its spacing features (SF) majorly affect optical quality by diffraction and interference. However, the ISO SR standard cannot effectively represent SF. In this study, a new representation for SF was developed by evaluating surface derivative, as extra SR parameters. Probability distribution with the 95–99 rule was adopted to reduce noise effects. The results were found that the extra SR parameters well represents SF and are sensitive to spatial frequency. Probability distribution is an efficient means of reducing noise effects. Significantly, the proposed method is simple and efficient to represent SF of NSR in UPM.     

An investigation of surface roughness of burnished AISI 1042 steel

The aim of this study is to analyse the evolution of surface roughness finished by burnishing. Burnishing is done on a surface that was initially turned or turned and then ground.It has been noted that burnishing an AISI 1042 steel offers the best surface quality when using a small feed value. This finishing process improves roughness and introduces compressive residual stresses in the machined surface. So, it can replace grinding in the machining range of the piece.Also, an analytical model has been defined to determine the R_t factor in relation to the feed. Good correlations have been found between the experimental and analytical results.     

A study of surface roughness variation in ultrasonic vibration-assisted milling

The objective of this paper is to investigate the effects of assisted ultrasonic vibration on the surface roughness of machined surfaces in micro-end-milling. Series of slot-milling experiments were conducted with aluminum alloy as workpiece material. The surface roughness of slot bottom surface and vertical side wall surface of slot was studied, respectively. It is found that surface roughness of the machined slot bottom surface could increase to varying degrees because of ultrasonic vibration in most of the studied cases, and this deterioration becomes more apparent when large feed per tooth and low-spindle speed were adopted. As for the vertical side wall surface of the slot, there is an obvious improvement of surface roughness when ultrasonic vibration is applied. Based on analysis of variance analysis, further study indicates that the surface roughness of vertical side wall surface of the slot is determined by several key parameters including spindle speed, feed per tooth and amplitude in ultrasonic vibration-assisted milling. An optimal combination of these parameters is of great benefit to achieving small surface roughness.     

基于遗传神经网络的表面粗糙度预测模型

建立超精密切削表面粗糙度预测模型是分析各切削参数对表面粗糙度影响和提高切削效率的关键,针对最小二乘法和传统反向传播神经网络等参数辨识方法的不足,提出将遗传算法优化的反向传播神经网络应用于超精密切削表面粗糙度预测模型的参数辨识中,得出采用金刚石刀具超精密切削铝合金的表面粗糙度预测模型,并与传统的参数辨识方法比较。实验结果表明该方法能更有效的辨识表面粗糙度预测模型,可为超精密车削加工表面质量的控制提供帮助。     

An investigation into performance of electric discharge milling using slotted tools

Journal of Mechanical Science and Technology - The flushing of by-products from the interelectrode gap (EG) of a few microns is the major concern in the electrical discharge machining (EDM). The...     

Optimal cutting condition determination for desired surface roughness in end milling

CNC end milling is a widely used cutting operation to produce surfaces with various profiles. The manufactured parts’ quality not only depends on their geometries but also on their surface texture, such as roughness. To meet the roughness specification, the selection of values for cutting conditions, such as feed rate, spindle speed, and depth of cut, is traditionally conducted by trial and error, experience, and machining handbooks. Such empirical processing is time consuming and laborious. Therefore, a combined approach for determining optimal cutting conditions for the desired surface roughness in end milling is clearly needed. The proposed methodology consists of two parts: roughness modeling and optimal cutting parameters selection. First, a machine learning technique called support vector machines (SVMs) is proposed for the first time to capture characteristics of roughness and its factors. This is possible due to the superior properties of well generalization and global optimum of SVMs. Next, they are incorporated in an optimization problem so that a relatively new, effective, and efficient optimization algorithm, particle swarm optimization (PSO), can be applied to find optimum process parameters. The cooperation between both techniques can achieve the desired surface roughness and also maximize productivity simultaneously.     

微细精密铣削表面粗糙度DRSM研究

DRSM方法具有序贯性、可旋转性、模型的稳健性以及试验次数少等优点,近年来逐渐运用在微细精密车铣加工运用中,笔者着重对微细精密铣削表面粗糙度进行DRSM分析,得出了微细精密铣削条件下工艺参数对表面粗糙度的影响规律,并进行了表面粗糙度的预测,有较强的理论实践和现实意义。     

An improved surface roughness prediction model using Box-Cox transformation with RSM in end milling of EN 353

Journal of Mechanical Science and Technology - In the present work, an attempt has been made to use Box-Cox transformation with response surface methodology to develop improve surface roughness...     

An investigation into the abrasive waterjet milling circular pocket on titanium alloy

This paper presents an experimental study on abrasive waterjet (AWJ) milling circular pockets of the titanium alloy Ti6Al4V workpieces. A material removal     

Use of NC kernel data for surface roughness monitoring in milling operations

This work focuses on developing an application based on the information contained in the numerical control (NC) kernel for surface roughness monitoring of the part in process. A human?Cmachine interface (HMI) was developed in order to facilitate the interaction between the operator and the NC kernel with a graphical user interface working in the computer numerically controlled (CNC) screen. Experimentation was carried out in order to obtain the data to be modeled with artificial neural networks for surface roughness average parameter (Ra) predictions. Finally, a compact solution was implemented through global user data (GUD). Data from the HMI and from the kernel are collected in the GUD and analyzed with the artificial neural network. The application provides the surface roughness average parameter of the part in process and gives optimized parameters to the operator. Verification tests were carried out, showing accurate results. The use of the application developed in this research ensures the surface roughness Ra requirement, improves cutting parameters, reduces manual finishing operations and unacceptable parts at the end of the manufacturing process, and provides a solution implemented in the machine tool CNC screen without the need of any other external sensors.