奥氏体不锈钢车削时刀具磨损对切削因素和表面质量的影响研究

研究了奥氏体不锈钢车削加工过程中刀具后刀面磨损对切削力、切削温度、粗糙度及残余应力影响规律。试验结果表明:当刀具后刀面磨损在一定范围内,F_x与F_z随磨损量的增加而显著增大,而F_y基本保持不变;温度随刀具后刀面磨损量增加而显著增大;工件的表面粗糙度随刀具后刀面磨损量增大而增大;当车刀后刀面磨损为0.167 mm时,工件加工表面的残余应力最大。     

加工镍基高温合金切屑塑性侧流实验研究

通过聚晶立方氮化硼(PCBN)刀具直角自由切削加工镍基高温合金实验,研究了切削速度、进给量、刀具磨损状态、刀具几何参数及刀具材质对切屑塑性侧流的影响,探讨了切屑塑性侧流对刀具磨损的影响。实验结果表明:切削速度、进给量、刀具磨损及负倒棱角度对切屑塑性侧流影响较大,刀具材质及刃口钝化影响很小;切屑塑性侧流现象在低速、较大进给量、较大负倒棱前角以及刀具磨损量较大的条件下比较明显,且当速度超过某值(v=62.4 m/min)时,切屑侧流达到稳态;切屑塑性侧流产生的锯齿形毛刺是刀具前刀面两侧产生沟槽磨损的主要原因。     

切削条件对淬硬钢已加工表面白层形成的影响

高速切削淬硬钢已加工表面存在白层,对工件使用性能具有很大的影响,研究已加工表面白层对改善工件表面质量和切削加工性具有重要意义。通过使用PCBN刀具高速干硬切削GCr15钢和40Cr Ni Mo A钢实验,分析了高速干硬切削过程中已加工表面产生白层的条件,研究了切削速度、后刀面磨损量等切削参数以及材料含碳量对白层厚度的影响规律。研究表明:已加工表面白层厚度随切削速度提高呈现先增加后减小趋势,随刀具磨损量增加而增大;随着工件材料含碳量增加,白层厚度增大。     

超声纵-扭复合铣削钛合金刀具磨损特性研究

目的采用超声纵-扭复合振动加工方法,获得较长的刀具使用寿命。方法采用理论建模与不同铣削振动方式,研究刀具磨损特性。通过超景深电子显微镜和粗糙度测试仪,分别对刀具微观形貌、工件表面粗糙度进行了分析;通过不同铣削方式加工钛合金材料,对刀具磨损特性进行了系统分析。结果与普通铣削和超声纵振铣削相比,超声纵-扭铣削方式下,刀具后刀面磨损减小,工件表面粗糙度降低。经测试,当去除面积为6356mm~2时,超声纵-扭复合加工刀具后刀面磨损量VB为103μm,分别比普通和超声纵振加工时降低了38μm和36μm。当去除面积为4530 mm~2时,Ra为1.2μm,普通铣削和超声纵振铣削的Ra则分别为1.62μm和1.38μm。由于超声纵振加工仅仅是在轴向方向实现了分离,后刀面时刻冲击着已加工表面,当去除面积为6356 mm~2时,刀具后刀面磨损量反有超出普通铣削的趋势。结论超声纵-扭复合加工从旋转方向内实现了刀-屑分离,在铣削过程中,极大地减少了刀具后刀面对已加工表面的冲击,从而使得刀具寿命有所延长,为高效加工、难加工材料提供了一种加工方法。     

车削玻璃陶瓷刀具磨损及表面粗糙度实验研究

以二硅酸锂玻璃陶瓷为加工对象,使用五种不同材质的刀具进行单因素车削实验。研究了不同材质对刀具体积磨损量及加工表面粗糙度的影响,并分析了刀具磨损形貌。实验结果表明,YG6刀具体积磨损量最大,PCD刀具硬度最大因此体积磨损量最小。YG6、YW1、YT14刀具的主要磨损形式是脆性剥落。刀具磨损改变了刀尖处的应力分布,玻璃陶瓷萌生更多裂纹。刀具磨损量越大,加工表面凹坑数目越多,粗糙度越大,表面质量越低。PCD刀具最适合车削玻璃陶瓷材料。     

单晶硅超精密切削的刀具磨损试验研究

针对单晶硅超精密切削过程中金刚石刀具磨损问题,对单晶硅进行超精密车削试验。通过观察金刚石刀具磨损演变过程,分析刀具的磨损过程对表面加工质量的影响,得到刀具磨损机理。结果表明,在超精密切削单晶硅过程中,随着切削距离的增加,刀具磨损面积逐渐增加,加工过程中产生的碳化硅及类似金刚石碳颗粒与刀具后刀面发生划擦造成磨粒磨损;同时,由于交变载荷作用导致的应力疲劳现象,进而伴有解理断裂产生。当切削路程小于4km时,加工表面的粗糙度Ra值在200nm以内,切削路程大于8km时,表面粗糙度Ra值在350nm~400nm之间。     

Ti6Al4V车削加工时硬质合金刀具磨损的数值模拟

为研究硬质合金刀具车削加工钛合金过程中的刀具磨损,刀具选择刚性模型,工件选择塑性模型,利用有限元软件对切削过程的刀具磨损进行了模拟。运用正交试验分析了在不同刀具参数组合下刀具磨损量的变化情况。结果表明:在固定的工艺参数下,刀具的前角和后角的交互作用对刀具的磨损量影响较大;单一参数因素的变化对刀具磨损量的变化影响较小。分析结果对降低硬质合金车削加工钛合金时的刀具磨损提供了参考。     

PCBN刀具干湿切削加工淬硬钢时的磨损对比

本文研究了在一定切削参数下干、湿式切削加工淬硬钢时四种PCBN刀具的刀具寿命、磨损形式和磨损机理。通过扫描电子显微镜观察不同切削行程下刀尖形貌和刀具后刀面磨损量,并对刀具前后刀面进行能谱分析。结果表明湿式切削时的后刀面磨损量小于干式切削,说明刀具湿切比干切时具有较好的性能;PCBN刀具的磨损形式有前刀面磨损、后刀面磨损,其中前刀面磨损的表现形式为月牙洼磨损,磨损机理为机械磨损、氧化磨损和黏结剂磨损,而后刀面磨损机理有机械磨损、氧化磨损、黏结剂磨损和扩散磨损等;同时还发现CBN含量下降,刀具的后刀面磨损量也有下降趋势,即刀具的切削寿命有延长趋势。     

PcBN加工淬硬钢刀具材料的研究

介绍了立方氮化硼刀具材料( PcBN)的制备过程,并制备了六种不同配方的样品加工淬硬钢.通过切削实验和性能检测,发现PcBN刀片在加工淬硬钢时cBN浓度起着关键作用,切削同样的路程,低浓度PcBN的后刀面磨损量小.经扫描电镜观察,CoAl合金粉能够提高PcBN烧结刀具材料的致密度.测量耐磨性时,证明用于金刚石复合片PC...     

精密切削纯铁材料硬质合金刀具刃口磨损特征演化

为研究纯铁材料精密切削时刀具刃口磨损特征演化规律,以涂层硬质合金刀具为研究对象进行纯铁材料精密切削试验。结果表明：涂层硬质合金刀具精密切削纯铁材料的磨损特征有后刀面均匀磨损带、主沟槽磨损、副沟槽磨损、刀尖磨损;主、副沟槽磨损长度都随着切削时间增加而增大且大于后刀面磨损量,沟槽磨损深度与沟槽磨损长度大致呈线性正相关;刀尖退化与后刀面磨损变化规律相互对应,切削初期磨损率大,随后磨损缓慢。     

Effects of process parameters on material side flow during hard turning

The phenomenon of material side flow represents an important aspect of machined surface quality during hard turning. In this paper, an experimental study was performed to investigate the main features of this phenomenon. The effects of process parameters including edge preparation, nose radius, feed and tool wear on material side flow were examined. Two possible mechanisms for material side flow were investigated. In the first one, the material is squeezed between the tool flank face and the machined surface when chip thickness is less than a minimum value. In the second mechanism, the plastified material in the cutting zone flows through the worn trailing edge to the side of the tool. Both of these mechanisms can exist simultaneously. The results obtained from surface examination showed a strong correlation between edge preparation and material side flow. An increase in the tool nose radius resulted in a remarkable increase of material side flow. Feed had an indirect effect on material side flow. In addition, tool wear significantly affected the existence of material side flow on the machined surface. An increase in tool wear promoted the occurrence of material side flow.     

White layers and thermal modeling of hard turned surfaces

White layers in hard turned surfaces are identified, characterized and measured as a function of tool flank wear and cutting speed. White layer depth progressively increases with flank wear. It also increases with speed, but approaches an asymptote. A thermal model based on Jaeger's moving heat source problems (J.C. Jaeger, Moving source of heat and the temperature at sliding contacts, in: Proceedings of the Royal Society, NSW, vol. 56, pp. 203–224) is applied to simulate the temperature field in machined surfaces and to estimate white layer depth in terms of the penetration depth for a given critical temperature. The analysis shows good agreement with the trend in experimental results. White layer formation seems to be dominantly a thermal process involving phase transformation of the steel, possibly plastic strain activated; flank wear land rubbing may be a primary heat source for white layer formation. A strong material dependence of surface alteration is also observed.     

A worn tool force model for three-dimensional cutting operations

Previous studies have shown that there is a region on the flank of a worn cutting tool where plastic flow of the workpiece material occurs. This paper presents experimental data which shows that in three-dimensional cutting operations in which the nose of the tool is engaged, the region of plastic flow grows linearly with increases in total wearland width. A piecewise linear model is developed for modeling the growth of the plastic flow region, and the model is shown to be independent of cutting conditions. A worn tool force model for three-dimensional cutting operations that uses this concept is presented. The model requires a minimal number of sharp tool tests and only one worn tool test. An integral part of the worn tool force model is a contact model that is used to obtain the magnitude of the stresses on the flank of the tool. The force model is validated through comparison to data obtained from wear tests conducted over a range of cutting conditions and workpiece materials. It is also shown that for a given tool and workpiece material combination, the incremental increases in the cutting forces due to tool flank wear are solely a function of the amount and nature of the wear and are independent of the cutting condition in which the tool wear was produced.     

The relation between chip morphology and tool wear in ultra-precision raster milling

In the field of ultra-precision machining, the study of the relation between chip morphology and tool wear is significant, since tool wear characteristics can be reflected by morphologies of cutting chips. In this research, the relation between chip morphology and tool flank wear is first investigated in UPRM. A cutting experiment was performed to explore chip morphologies under different widths of flank wear land. A geometric model was developed to identify the width of flank wear land based on chip morphology. Theoretical and experimental results reveal that the occurrence of tool flank wear can make the cutting chips truncated at both their cut-in and cut-out sides, and reduce the length of cutting chips in the feed direction. The width of truncation positions of the cutting chip can be measured and used to calculate the width of flank wear land with the help of the mathematical model. The present research is potentially used to detect tool wear and evaluate machined surface quality in intermittent cutting process.     

On tool wear and its effect on machined surface integrity

This paper presents the results of an investigation of induced residual stress, induced strain, and induced subsurface energy in machined surfaces due to the machining process. The influence of tool wear on residual stress, strain, and energy is also reported. The exact elasticity solution for a split ring was extended and used to calculate the residual stress in the machined surface by using ring dimension changes caused by the electrochemical removal of a thin layer of residually stressed surface. The strain distribution beneath the machined surface was determined by using the grid technique. The subsurface energy stored in the machined surface was then obtained from the data of residual stress and strain. For the materials studied, this investigation showed that such energy could not be neglected when establishing the total energy needed for machining a unit volume of material. Tool coatings having different surface roughness and tools having various magnitudes of flank wear were investigated. The experimental results show that tool wear is a dominant factor affecting the values of induced residual stress, strain, subsurface energy, and the quality of the machined surface. The increase of tool wear caused an increase of residual stress and strain beneath the machined surface. It was also found that the overall energy stored in the machined subsurface increases as the tool wear increases and as the tool surface gets rougher. When the cutting tool is severely worn, the machined surface not only becomes very rough, but also contains many partially fractured laps or cracks. This makes tool wear a key factor in controlling the quality of the machined surface.     

Predictive modeling of surface roughness and tool wear in hard turning using regression and neural networks

In machining of parts, surface quality is one of the most specified customer requirements. Major indication of surface quality on machined parts is surface roughness. Finish hard turning using Cubic Boron Nitride (CBN) tools allows manufacturers to simplify their processes and still achieve the desired surface roughness. There are various machining parameters have an effect on the surface roughness, but those effects have not been adequately quantified. In order for manufacturers to maximize their gains from utilizing finish hard turning, accurate predictive models for surface roughness and tool wear must be constructed. This paper utilizes neural network modeling to predict surface roughness and tool flank wear over the machining time for variety of cutting conditions in finish hard turning. Regression models are also developed in order to capture process specific parameters. A set of sparse experimental data for finish turning of hardened AISI 52100 steel obtained from literature and the experimental data obtained from performed experiments in finish turning of hardened AISI H-13 steel have been utilized. The data sets from measured surface roughness and tool flank wear were employed to train the neural network models. Trained neural network models were used in predicting surface roughness and tool flank wear for other cutting conditions. A comparison of neural network models with regression models is also carried out. Predictive neural network models are found to be capable of better predictions for surface roughness and tool flank wear within the range that they had been trained.Predictive neural network modeling is also extended to predict tool wear and surface roughness patterns seen in finish hard turning processes. Decrease in the feed rate resulted in better surface roughness but slightly faster tool wear development, and increasing cutting speed resulted in significant increase in tool wear development but resulted in better surface roughness. Increase in the workpiece hardness resulted in better surface roughness but higher tool wear. Overall, CBN inserts with honed edge geometry performed better both in terms of surface roughness and tool wear development.     

Modelling of Material Side Flow in Hard Turning

In this paper an attempt has been made to model the material side flow generated during hard turning operation. A three dimensional thermo elasto-viscoplastic finite element model is presented. The model incorporates cutting tools with different nose radii and cutting conditions. The model was also used to investigate the effect of different process parameters on the extent of material side flow. The predicted results revealed that more side flow is generated when higher nose radius is used. A similar observation is noted when lower feed is used. The simulated results agreed well with the experimentally examined machined surface, viewed with a scanning electron microscope.     

On the use of fractal methods for the tool flank wear characterization

Fractal theory is widely used to analyze the topography of machined surfaces, but the relationship between fractal dimensions and tool flank wear has hardly been reported. In this paper, the fractal dimensions of tool flank wear are described based on the surface roughness R_a rather than the conventional worn width VB to evaluate tool wear, thus providing better fractal identification in evaluating tool performance.     

Thermal and mechanical modeling analysis of laser-assisted micro-milling of difficult-to-machine alloys

This study is focused on numerical modeling analysis of laser-assisted micro-milling (LAMM) of difficult-to-machine alloys, such as Ti6Al4V, Inconel 718, and stainless steel AISI 422. Multiple LAMM tests are performed on these materials in side cutting of bulk and fin workpiece configurations with 100-300 μm diameter micro endmills. A 3D transient finite volume prismatic thermal model is used to quantitatively analyse the material temperature increase in the machined chamfer due to laser-assist during the LAMM process. Novel 2D finite element (FE) models are developed in ABAQUS to simulate the continuous chip formation with varying chip thickness with the strain gradient constitutive material models developed for the size effect in micro-milling. The steady-state workpiece and tool cutting temperatures after multiple milling cycles are analysed with a heat transfer model based on the chip formation analysis and the prismatic thermal model predictions. An empirical tool wear model is implemented in the finite element analysis to predict tool wear in the LAMM side cutting process. The FE model results are discussed in chip formation, flow stresses, temperatures and velocity fields to great details, which relate to the surface integrity analysis and built-up edge (BUE) formation in micro-milling.     

Thermal modeling for white layer predictions in finish hard turning

Part thermal damage is a process limitation in finish hard turning and understanding process parameter effects, especially, tool wear, on cutting temperatures is fundamental for process modeling and optimization. This study develops an analytical model for cutting temperature predictions, in particular, at the machined-surfaces, in finish hard turning by either a new or worn tool.A mechanistic model is employed to estimate the chip formation forces. Wear-land forces are modeled using an approach that assumes linear growth of plastic zone on the wear-land and quadratic decay of stresses in elastic contact. Machining forces and geometric characteristics, i.e. shear plane, chip–tool contact, and flank wear-land, approximate the heat intensity and dimensions of the shear plane, rake face, as well as wear-land heat sources. The three heat sources are further discretized into small segments, each treated as an individual rectangular heat source and subsequently used to calculate temperatures using modified moving or stationary heat-source approaches. Temperature rises due to all heat-source segments are superimposed, with proper coordinate transformation, to obtain the final temperature distributions due to the overall heat sources. All heat sources are simultaneously considered to determine heat partition coefficients, both at the rake face and wear-land, and evaluate the final temperature rises due to the combined heat-source effects.Simulation results show that, in new tool cutting, maximum machined-surface temperatures are adversely affected by increasing feed rate and cutting speed, but favorably by increasing depth of cut. In worn tool cutting, flank wear has decisive effects on machined-surface temperatures; the maximum temperature increases 2–3 times from 0 to 0.2 mm wear-land width. White layers (phase-transformed structures) formed at the machined-surfaces have been used to experimentally validate the analytical model by investigating tool nose radius effects on the white layer depth. The experimental results show good agreement with the model predictions.The established model forms a framework for analytical predictions of machined-surface temperatures in finish hard turning that are critical to part surface integrity and can be used to specify a tool life criterion.