改进的球头铣刀加工表面形貌建模方法

为解决传统时间步形貌模型存在的刀刃微元尺寸和切削时间离散步长对工件网格尺寸依赖性强、计算效率低、计算精度差等问题,提出一种改进的时间步形貌模型.首先,基于5轴铣削加工中刀具与工件之间的相对运动关系,运用齐次坐标变换原理建立刀刃扫掠面方程.其次,基于刀具不同倾斜角时与工件的接触关系,建立了切入刀刃模型.然后,根据刀刃微元...     

Detection process approach of tool wear in high speed milling

The cutting tool wear degrades the quality of the product in the manufacturing process, for this reason an on-line monitoring of the cutting tool wear level is very necessary to prevent any deterioration. Unfortunately there is no direct manner to measure the cutting tool wear on-line. Consequently we must adopt an indirect method where wear will be estimated from the measurement of one or more physical parameters appearing during the machining process such as the cutting force, the vibrations, or the acoustic emission, etc. The main objective of this work is to establish a relationship between the acquired signals variation and the tool wear in high speed milling process; so an experimental setup was carried out using a horizontal high speed milling machine. Thus, the cutting forces were measured by means of a dynamometer whereas; the tool wear was measured in an off-line manner using a binocular microscope. Furthermore, we analysed cutting force signatures during milling operation throughout the tool life. This analysis was based on both temporal and frequential signal processing techniques in order to extract the relevant indicators of cutting tool state. Our results have shown that the variation of the variance and the first harmonic amplitudes were linked to the flank wear evolution. These parameters show the best behavior of the tool wear state while providing relevant information of this later.     

钛合金铣削刀具磨损对表面完整性影响研究

为了掌握钛合金TC4铣削过程中刀具磨损对表面完整性的影响规律,通过对不同刀具后刀面磨损量下铣削钛合金工件的表面完整性测试,得出了刀具磨损对表面完整性的影响规律,并对其影响机理进行了分析.结果表明,在刀具处于初期磨损和正常磨损阶段,刀具的挤光效应引起的压应力占主导地位,而在刀具剧烈磨损阶段,加工过程中的热塑性变形引起的拉应力占主导地位;随着刀具后刀面磨损量的增加,刀具正常磨损阶段粗糙度值缓慢增加,剧烈磨损阶段粗糙度值迅速增加;随着刀具后刀面磨损量的增加,已加工表面的显微硬度值和表面层的硬化深度都随之增大.     

Cutting force model considering tool edge geometry for micro end milling process

The analysis of the cutting force in micro end milling plays an important role in characterizing the cutting process, as the tool wear and surface texture depend on the cutting forces. Because the depth of cut is larger than the tool edge radius in conventional cutting, the effect of the tool edge radius can be ignored. However, in micro cutting, this radius has an influence on the cutting mechanism. In this study, an analytical cutting force model for micro end milling is proposed for predicting the cutting forces. The cutting force model, which considers the edge radius of the micro end mill, is simulated. The validity is investigated through the newly developed tool dynamometer for the micro end milling process. The predicted cutting forces were consistent with the experimental results.     

Modeling and simulation of grinding surface topography considering wheel vibration

Grinding is an important means of realizing precision and ultra-precision machining. Vibration caused by an unbalanced grinding wheel in grinding process has a significant impact on the quality of workpiece surface. However, the effect of wheel surface topography and/or the relative vibration between grinding wheel and workpiece are not considered in most researches. Taking the relative vibration between grinding wheel and workpiece into account, alongside the abrasive grain trajectory equation, a new analysis and simulation model for surface topography of the grinding process is established. The model for the topography of the grinding wheel surface is first studied, and subsequently, a new simulation model for surface topography of the grinding process is proposed. Case studies are performed at the end, and the influence of grinding wheel vibration amplitude, wheel grit number, as well as grinding parameters on the surface waviness and roughness is discussed. The simulation results could be used to optimize the actual grinding process to improve the ground surface quality or predict the surface topography by given grinding parameters.     

铣刀磨损量监测和剩余寿命预测方法研究

研究在线诊断端面铣刀磨损量和预测铣刀剩余寿命的方法。首先通过实验采集Y向铣削力作为监测信号,分析铣削力与刀具磨损量VB和刀具磨损时间之间的关系。然后,通过铣削力信号分析提取出有效监测特征向量,此特征向量作为BP神经网络的输入,用于刀具磨损量的监测和剩余寿命的预计。最后,通过实验证明,该神经网络模型误差很小,利用该方法能够正确地进行在线监测和预测。     

Surface generation modeling of micro milling process with stochastic tool wear

Micro milling is widely used to manufacture miniature parts and features at high quality with low set-up cost. To achieve a higher quality of existing micro products and improve the milling performance, a reliable analytical model of surface generation is the prerequisite as it offers the foundation for surface topography and surface roughness optimization. In the micro milling process, the stochastic tool wear is inevitable, but the deep influence of tool wear hasn't been considered in the micro milling process operation and modeling. Therefore, an improved analytical surface generation model with stochastic tool wear is presented for the micro milling process. A probabilistic approach based on the particle filter algorithm is used to predict the stochastic tool wear progression, linking online measurement data of cutting forces and tool vibrations with the state of tool wear. Meanwhile, the influence of tool run-out is also considered since the uncut chip thickness can be comparable to feed per tooth compared with that in conventional milling. Based on the process kinematics, tool run-out and stochastic tool wear, the cutting edge trajectory for micro milling can be determined by a theoretical and empirical coupled method. At last, the analytical surface generation model is employed to predict the surface topography and surface roughness, along with the concept of the minimum chip thickness and elastic recovery. The micro milling experiment results validate the effectiveness of the presented analytical surface generation model under different machining conditions. The model can be a significant supplement for predicting machined surface prior to the costly micro milling operations, and provide a basis for machining parameters optimization.     

Simulation of textured surface topography during a low wear process

Wear experiments were conducted on a block-on ring tester. The stationary block made from cast iron of 50 HRC hardness was ground. The rotated ground ring was made from 42CrMo4 steel of 32 HRC hardness. The rings were modified by a burnishing technique in order to obtain surfaces with oil pockets. Oil pockets of spherical and of drop shape were tested. The correlation and regression analysis of parameters of textured surface topography was carried out. Two sets of surfaces were analysed: after machining and after “zero-wear”. As the result of analysis, minimum number of parameters describing this surface kind was obtained. A simple truncation model of the ring surface change was used. Worn surface topographies, after a low wear, were also modeled in a different way. An idea of the proposed method of surface topography modeling is imposition of random surface of Gaussian ordinate distribution on the base surface (after burnishing). The modeled surfaces were correctly matched to the measured surfaces in 90% of all analysed cases. Basing on the simulation, the local wear values during a low wear process were calculated and compared with experimental ones.     

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.     

Cutting force in face milling with tool wear

The proposed mathematical model includes not only the cutting forces due to processes in the shear zone but also the component of the force due to processes at the tool’s real surface. The model takes account of the different configuration of the replaceable multifaceted plates and the forces at radial and linear sections of the mill tooth.     

Adaptive modelling of the milling process and application of a neural network for tool wear monitoring

An adaptive signal processing scheme that uses a low-order autoregressive time series model is introduced to model the cutting force data for tool wear monitoring during face milling. The modelling scheme is implemented using an RLS (recursive least square) method to update the model parameters adaptively at each sampling instant. Experiments indicate that AR model parameters are good features for monitoring tool wear, thus tool wear can be detected by monitoring the evolution of the AR parameters during the milling process. The capability of tool wear monitoring is demonstrated with the application of a neural network. As a result, the neural network classifier combined with the suggested adaptive signal processing scheme is shown to be quite suitable for in-process tool wear monitoring     

A new tool wear estimation method based on shape mapping in the milling process

In the milling process, tool wear has a great influence on product machining quality, especially for a difficult-to-cut material. In this paper, a new approach based on shape mapping is proposed to acquire tool wear in order to establish an off-line tool wear predicting model for assessing the degree of wear and remaining useful life. The new approach maps tool wear shape into a metal material by milling holes mode after finishing each of the machining experiments. The metal material has low influence on tool wear compared to the experimental material. Thus, a series of mapped holes, which can represent the worn tool information, are formed on the metal material when finishing all milling experiments. These mapped holes on the metal material are analyzed according to all types of milling cutters in order to establish the relationship between the characteristic parameters of these mapped holes and tool wear. According to the established relationship, the characteristic parameters of these mapped holes are measured on the coordinate measure machine. The tool wear of each machining experiment can be obtained from the measured characteristic parameters of these mapped holes. The new tool wear estimation method does not require the stoppage of the machine tool and the removal of the cutter to measure tool wear in the process of conducting tool wear experiments. The new method can increase the machine tool efficiency of tool wear machining experiments and provide an efficient way to acquire tool wear in the process of establishing an off-line tool wear predicting model. In order to verify the new tool wear estimation method, a series of machining experiments were conducted on the five-axis machining center for cemented carbide cutting tool milling stainless steel. Experiments show that the shape mapping strategy of tool wear can allow for an effective assessment of tool wear and indicate good correlation with the expected wear characteristics and easily conduct tool wear experiments.     

Evaluation of static cutting forces and tool wear in HSM process applied to pocket milling

One of the main operations in the manufacturing of molds and dies is the opening of the initial pocket, from which more complex geometries are machined, in order to obtain the negative shape of the final product. Since this is a rough operation, high cutting forces and significant tool wear are observed. Aiming to reduce such parameters, several strategies of tool entry and internal cut of the pocket have been proposed. In this context, this work aims to evaluate the cutting forces in the different parts of a pocket milling, using three different strategies, which are composed by the tool, cutting conditions, tool entry, and tool trajectory (I: ramp entry with spiral internal cut, II: helical entry with zigzag internal cut, and III: plunge entry). The results obtained for strategies I and II showed an increase in the forces in the direction perpendicular to the face in which flank wear occurred and a sharp increase in force at points where the tool changes trajectory. In strategy III, the occurrence of tool fracture due to chip recut led to very high force values.     

The dependence of tool overhang on surface quality and tool wear in the turning process

In the turning process, the importance of machining parameter choice is increased, as it controls the surface quality required. Tool overhang is a cutting tool parameter that has not been investigated in as much detail as some of the better known ones. It is appropriate to keep the tool overhang as short as possible; however, a longer tool overhang may be required depending on the geometry of the workpiece and when using the hole-turning process in particular. In this study, we investigate the effects of changes in the tool overhang in the external turning process on both the surface quality of the workpiece and tool wear. For this purpose, we used workpieces of AISI 1050 material with diameters of 20, 30, and 40 mm; and the surface roughness of the workpiece and tool wear were determined through experiments using constant cutting speed and feed rates with different depth of cuts (DOCs) and tool overhangs. We observed that the effect of the DOC on the surface roughness is negligible, but tool overhang is more important. The deflection of the cutting tool increases with tool overhang. Two different analytical methods were compared to determine the dependence of tool deflection on the tool overhang. Also, the real tool deflection values were determined using a comparator. We observed that the tool deflection values were quite compatible with the tool deflection results obtained using the second analytical method.     

基于切削稳定性与表面质量约束的铣削工艺参数优化研究

铣削工艺系统的动态特性随着刀具夹持长度改变而变化,影响关联的铣削稳定性与加工表面质量,进而导致铣削加工的工艺规划具有不确定性.针对此问题,本文综合刀具悬伸量与传统铣削用量作为输入,分别建立极限切削深度与表面粗糙度的反向传播神经网络模型(BPNN),并进一步以其表达铣削稳定性与加工质量约束,构建以刀具悬伸量和粗/精加工阶...     

Towards the effective tool wear control in micro-EDM milling

The electrode wear in micro-electrical discharge milling (micro-EDM milling) is one of the main problems to be solved in order to improve machining accuracy. This paper presents an investigation on wear and material removal in micro-EDM milling for selected process parameter combinations typical of rough and finish machining of micro-features in steel. The experiments were performed on state-of-the-art micro-EDM equipment. Based on discharge counting and volume measurements, electrode wear per discharge and material removal per discharge were measured for several energy levels. The influence of the accuracy of volume measurements on the electrode wear per discharge and on the material removal per discharge are discussed, and the issues limiting the applicability of real time wear sensing in micro-EDM milling are presented.