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.     

微铣削刀具磨损研究现状

在微制造领域,微铣削因具有加工材料的多样性和能实现三维曲面加工的独特优势而受到越来越多学者的关注,但是微铣刀的快速磨损严重影响了微铣削技术的应用.研究表明微铣刀的磨损主要发生在刀尖部位,刀具磨损呈现显著的尺度效应.分析了微铣刀的磨损机理、刀具磨损的影响因素和改善措施以及刀具磨损状态的监控,并指出了今后研究值得注意的发展方向.     

基于小波分析和模糊控制理论的铣刀磨损监控方法

研制了一种铣刀磨损的监控方法.在该系统中信号采集采用声发射传感器,信号的特征提取采用小波分析的方法,将变换后的尺度系数和各个频段的小波系数作为特征,采用自行设计的Sugeno模糊控制系统进行状态识别,模糊控制系统的输出是刀具磨损的具体值.     

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。     

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.     

Job scheduling and management of wearing tools with stochastic tool lifetimes

The problem of scheduling jobs using wearing tools is studied. Tool wearing is assumed to be stochastic and the jobs are processed in one machining centre provided with a limited capacity tool magazine. The aim is to minimize the expected average completion time of the jobs by choosing their processing order and tool management decisions wisely. All jobs are available at the beginning of the planning period. This kind of situation is met in production planning of CNC-machines. Previous studies concerning this problem have either assumed deterministic wearing for the tools or omitted the wearing completely. In our formulation of the problem, tool wearing is stochastic and the problem becomes very hard to solve analytically. A heuristic based on genetic algorithms is therefore given for the joint problem of job scheduling and tool management. The algorithm searches the most beneficial job sequence when the tool management decisions are made by a removal rule taking into account the future planned usage of the tools. The cost of each job sequence is evaluated by simulating the job processing. Empirical tests with heuristics indicate that by taking the stochastic information into account, one can reduce the average job processing time considerably.     

Instantaneous uncut chip thickness modeling for micro-end milling process

The prediction model of instantaneous uncut chip thickness is critical for micro-end milling process, which can directly affect the cutting forces, surface accuracy, and process stability of the micro-end milling process. This paper presents an instantaneous uncut chip thickness model systematically based on the actual trochoidal trajectory of tooth and the tool run-out in micro-end milling process. The variable entry and exit angles of tool, which are affected by the tool run-out, are concerned in the model. The related instantaneous uncut chip thickness is evaluated by considering the theoretical instantaneous uncut chip thickness and the minimum uncut chip thickness, which is formulated by two types of material removal mechanisms, in the elastic-plastic deformation region and the complete chip formation region, respectively. In comparison with the instantaneous chip thickness obtained from the conventional model, the feasibility of the proposed model can be proved by the related simulation results with variable process parameters including feed per tooth, radial depth of cut, and tool run-out. In addition, the predicted and measured cutting forces are compared with validate the accuracy of the proposed instantaneous uncut chip thickness model for the micro-end milling process.     

Novel tool wear monitoring method in ultra-precision raster milling using cutting chips

Tool wear monitoring is a popular research topic in the field of ultra-precision machining. However, there appears to have been no research on the monitoring of tool wear in ultra-precision raster milling (UPRM) by using cutting chips. In the present research, monitoring tool wear was firstly conducted in UPRM by using cutting chips. During the cutting process, the fracture wear of the diamond tool is directly imprinted on the cutting chip surface as a group of ‘ridges’. Through inspection of the locations, cross-sectional shape of these ridges by a 3D scanning electron microscope, the virtual cutting edge of the diamond tool under fracture wear is built up. A mathematical model was established to predict the virtual cutting edge with two geometric elements: semi-circle and isosceles triangle used to approximate the cross-sectional shape of ridges. Since the theoretical prediction of cutting edge profile concurs with the inspected one, the proposed tool wear monitoring method is found to be effective.     

异形螺杆数控铣削刀具磨损智能建模研究

介绍了一种在线估算螺杆数控铣削中刀具磨损量的新方法。该方法基于螺杆铣削过程变切削参数的工况,提取了振动信号和功率信号的刀具磨损特征值,基于自适应神经—模糊推理系统建立了刀具磨损数学模型。实验证明,由此建立的刀具磨损模型能够排除切削参数变化的干扰,可以较好的反映加工中刀具磨损状态,同时也为具有时变切削参数特性的加工过程刀具磨损状态监控提供了新的研究方法。     

Built-up edge effects on process outputs of titanium alloy micro milling

Built-up edge (BUE) is generally known to cause surface finish problems in the micro milling process. The loose particles from the BUE may be deposited on the machined surface, causing surface roughness to increase. On the other hand, a stable BUE formation may protect the tool from rapid tool wear, which hinders the productivity of the micro milling process. Despite its common presence in practice, the influence of BUE on the process outputs of micro milling has not been studied in detail. This paper investigates the relationship between BUE formation and process outputs in micro milling of titanium alloy Ti6Al4V using an experimental approach. Micro end mills used in this study are fabricated to have a single straight edge using wire electrical discharge machining. An initial experimental effort was conducted to study the relationship between micro cutting tool geometry, surface roughness, and micro milling process forces and hence conditions to form stable BUE on the tool tip have been identified. The influence of micro milling process conditions on BUE size, and their combined effect on forces, surface roughness, and burr formation is investigated. Long-term micro milling experiment was performed to observe the protective effect of BUE on tool life. The results show that tailored micro cutting tools having stable BUE can be designed to machine titanium alloys with long tool life with acceptable surface quality.     

Gouging elimination through tool lifting in tool path generation for five-axis milling based on faceted models

Five-axis milling may be performed with a constant tool-orientation or varying optimal tool-orientation. When applying a constant tool orientation, the inclination angle, the angle between the tool axis and the normal vector of a contact point (cc-point), is kept constant along the tool path. On the other hand, when applying a varying optimal tool orientation, the tool inclination angle is dynamically optimized along the tool path in order to maintain the tool to be as close as possible to the surface without gouging. In both tool orientation methods, tool lifting is one of the crucial components and involved in tool path generation, especially when it is used for gouging elimination. For the constant tool orientation, the tool is lifted immediately when the specified inclination angle causes gouging with the part surface. In the case of varying optimal tool orientation, the minimum rotation angle (inclination angle) has to be found first to avoid gouging. If the gouging still occurs (e.g. due to limited rotational axes of the milling machine), then the tool is lifted. In this paper, gouging elimination through tool lifting for five-axis milling based on a faceted model is presented. The tool is lifted based on the types of gouging. These types of gouging are described and the tool lifting procedure has been developed and implemented for gouging elimination in both tool orientation methods.     

新型微细锥形铣刀几何刃形设计与试验研究

基于切削原理及刀具结构理论,针对微细铣削加工特点,设计了新型微细锥形铣刀.通过试验研究,验证了新型微细锥形铣刀几何刃形的合理性.从而解决了锥形铣刀加工时刀具对工件挤压严重造成塑陛隆起、毛刺突出的问题.     

Influence of minimum quantity lubrication parameters on tool wear and surface roughness in milling of forged steel

The minimum quantity of lubrication (MQL) technique is becoming increasingly more popular due to the safety of environment.Moreover,MQL technique not only leads to economical benefits by way of saving ...     

在线支持向量机在数控铣床刀具磨损预测建模中的应用

刀具磨损的自动监测是现代制造技术的关键技术之一,是保证自动化加工顺利进行的前提之一.在实际生产当中,对刀具磨损的检测,不能停机检测而只能采取在线的间接监测方法.提出一种基于在线支持向量机的数控铣床刀具磨损的预测方法.结果表明,所提方法具有参数调整时间快、泛化能力强的优点,可以比较准确地监控刀具磨损.     

An artificial-neural-networks-based in-process tool wear prediction system in milling operations

An artificial-neural-networks-based in-process tool wear prediction (ANN-ITWP) system has been proposed and evaluated in this study. A total of 100 experimental data have been received for training through a back-propagation ANN model. The input variables for the proposed ANN-ITWP system were feed rate, depth of cut from the cutting parameters, and the average peak force in the y-direction collected online using a dynamometer. After the proposed ANN-ITWP system had been established, nine experimental testing cuts were conducted to evaluate the performance of the system. From the test results, it was evident that the system could predict the tool wear online with an average error of ±0.037 mm. Experiments have shown that the ANN-ITWP system is able to detect tool wear in 3-insert milling operations online, approaching a real-time basis .     

Influence of tool wear on surface roughness in hard turning using differently shaped ceramic tools

Hard turning has been applied in many cases in producing bearings, gears, cams, shafts, axels, and other mechanical components since the early 1980s. Mixed ceramics (aluminum oxide plus TiC or TiCN) is one of the two cutting tool materials (apart from PCBN) widely used for finish machining of hardened steel (HRC 50–65) parts, especially under dry machining conditions and moderate cutting speed ranging from 90 to 120 m/min. This paper reports an extensive characterization of the surface roughness generated during hard turning (HT) operations performed with conventional and wiper ceramic tools at variable feed rate and its changes originated from tool wear. Moreover, it compares some predominant tool wear patterns produced on the two types of ceramic inserts and their influence on the alteration of surface profiles. After the hard turning tests, the relevant changes of surface profiles and surface roughness parameters were successively registered and measured by a stylus profilometer. In this investigation, a set of 2D surface roughness parameters, as well as profile and surface characteristics, such as the amplitude distribution functions, bearing area curves and symmetrical curves of geometrical contact obtained for the machined surface, were determined and analyzed. A novel aspect of this research is that the notch wear progress at the secondary cutting (trailing) edges was found to produce the substantial modifications of the individual irregularities, and constitute the altered surface profiles. Moreover, this research contributes to practical aspects of HT technology due to exploring the relations between the tool state at different times within the tool life and the relevant surface roughness characterization.     

Surface texture generation using high-feed milling with spindle speed modulation

This paper presents a new surface texturing technique using ball-end milling with high feed speed and spindle speed modulation. The ratio between feed-rate and cutting tool radius is in the range of 0.2–0.4, which is much larger than the ratio in conventional milling. Sinusoidal modulation signal is added, so the spindle speed becomes time-varying in order to generate different texture profiles. The cutting tool kinematics are modeled considering the tool-tip run-out and deflection due to cutting forces. The effects of amplitude and frequency of the modulation signal on tool-tip trajectories and surface textures are simulated and analyzed. The relationship between the micro features of the surface texture and the process parameters are investigated. Surface texturing experiments are conducted based on the proposed technique, and tribology tests are performed on the textured surface. It is shown that the textured surfaces present frictional anisotropy, which depends on the process conditions and modulation parameters. The proposed technique is able to achieve fast generation of various surface textures without additional instrumentation, and the final texture geometry is controllable based on the presented kinematics model.     

Identification of tool wear using acoustic emission signal and machine learning methods

The work concerns the monitoring of the edge condition based on acoustic emission (AE) signals. The tool edge condition was determined by the wear width on the flank face. The processed material was an aluminum-ceramic composite containing 10% SiC. A carbide milling cutter with a diamond coating was used as the tool. Based on the AE signals, appropriate measures were developed that were correlated with the edge condition. Machine learning methods were used to assess the milling cutter's degree of wear based on AE signals. The applied approach using a decision tree allowed the prediction error of the tool condition class with a value below 6%. The method was also compared with other machine learning methods such as neural networks and the k-nearest neighbor algorithm.     

Precision surface characterization for finish cylindrical milling with dynamic tool displacements model

In this work a new approach to surface roughness parameters estimation during finish cylindrical end milling is presented. The proposed model includes the influence of cutting parameters, the tool’s static run out and dynamic phenomena related to instantaneous tool deflections. The modeling procedure consists of two parts. In the first stage, tool working part instantaneous displacements are estimated using an analytical model which considers tool dynamic deflections and static errors of the machine – tool-holder – tool system. The obtained height of the tool’s displacement envelope is then applied in the second stage to the calculation of surface roughness parameters. These calculations assume that in the cylindrical milling process, two different mechanisms of surface profile formation exist. Which mechanism is present is dependent on the feed per tooth and the maximum height of the tool’s displacement envelope. The developed model is validated during cylindrical milling of hardened hot-work tool steel 55NiCrMoV6 using a stylus profiler and scanning laser vibrometer over a range of cutting parameters. The surface roughness values predicted by the developed model are in good agreement with measured values. It is found that the employment of a model which includes only the effect of static displacements gives an inferior estimation of surface roughness compared to the model incorporating dynamic tool deflections.