基于AdvantEdge的硬态精车过程仿真

为探讨硬态精车过程中工艺参数对工件和刀具的影响,采用专门的金属切削仿真软件Advant Edge,建立精车H13淬硬钢的三维有限元模型,对其车削力、刀片切削温度、刀片应力以及切屑进行分析。结果表明:硬态车削过程中,切深抗力在切削过程中起着重要作用,在进给量小于一定值时,切深抗力可以大于主切削力;切削速度越大,刀片温度越高,切削力越小,刀片应力越小,有利于加工表面成型;切削刃半径越小,切削力小,刀片应力小,有利于提高切削加工性能,但小的切削刃半径容易导致刀片磨损;硬态车削切屑呈锯齿状,切屑温度带状分布,切削速度越高,进给量越大,切屑温度越高。研究结论可用于硬态切削过程中的工艺参数优化和刀具及其涂层材料的选择与设计。     

基于神经网络的高速硬车削切削力预测研究

采用PCBN刀具进行高速硬车削AISI P20淬硬钢的切削试验,并通过方差分析研究切削速度、进给量、切削深度和刀尖圆弧半径对切削力的影响.基于获得的试验数据,应用人工神经网络方法建立高速硬车削P20淬硬钢时的切削力预测模型.试验与仿真分析显示,切削力随进给量、切削深度和刀尖圆弧半径的增加而增大,而不同切削速度下的切削力值几乎保持不变;同时,切削深度对切削力的影响最为显著,其次为进给量,再次为刀尖圆弧半径,而切削速度的影响则非常微弱.     

基于正交试验的高速切削淬硬模具钢的切削力研究

通过正交设计方案,对淬硬到60HRC的冷作模具钢Cr12MoV进行高速车削切削力试验,分析了切削用量和刀具变量对切削力的影响规律,并建立了切削力的经验公式。得到如下结论:切削用量中,切削速度对切削力影响最小,进给量和背吃刀量对切削力影响较大;三个方向力中,轴向力最小,径向力和主切削力相接近,与常规切削相比,径向力偏大;小刀尖圆弧半径的PCBN刀具是进行高速切削淬硬钢时的理想刀具;经验公式预测值与实测值之间存在约5%-20%的误差。     

不同材料和刀尖圆弧半径刀具加工TC4钛合金的表面质量及刀具磨损行为

采用聚晶立方氮化硼(PCBN)、TiAlN涂层硬质合金(2种刀尖圆弧半径)和Al_2O_3+TiC涂层硬质合金等3种刀具车削TC4钛合金工件,测试了刀具后刀面磨损宽度和工件表面粗糙度,观察了刀具的磨损形貌并分析了磨损机制;同时,研究了刀尖圆弧半径对工件表面粗糙度和切屑形貌的影响。结果表明:TiAlN涂层硬质合金刀具具有比其他2种刀具更长的使用寿命,且加工后工件的表面粗糙度最小、表面质量最好,其磨损形式主要为磨粒磨损和黏结磨损;PCBN刀具的失效形式主要为前刀面和后刀面崩塌,而Al_2O_3+TiC涂层硬质合金刀具的磨损形式主要为扩散磨损;刀尖圆弧半径的增大有利于提高TiAlN涂层硬质合金刀具的断屑能力以及加工工件的表面质量。     

高速铣削SM1CrNi3的切削力和表面质量试验研究

应用均匀设计法设计并进行淬硬钢SM1CrNi3的高速铣削试验,分析铣削速度、每齿进给量、侧吃刀量、背吃刀量、刀尖半径和刀具材料对切削力和表面质量的影响。结果表明:刀具材料对切削力有影响,对表面质量无影响;铣削用量和刀尖半径对切削力和表面质量都有影响,且不一致,甚至是矛盾的,因此在实际生产选择铣削参数时应以表面质量为先,兼顾切削力。     

Inconel 718合金超声振动切削质量与刀具寿命控制研究

超声振动切削UVC是解决难加工材料精细加工问题的理想方法。在对Inconel718合金同时进行UVC和传统车削法CT加工中,研究了切削参数（切削速度、进给量、切削时间）对刀具切削性能的影响。通过对刀具磨损、切屑形态和工件表面粗糙度的研究,发现在进行低速硬态切削时,UVC在切削表面质量和刀具寿命方面均优于传统车削加工。同时随着刀具－工件表面接触率TWCR的降低,刀具磨损和切削力随之降低,而工件表面质量和刀具寿命得到提高。     

硬质合金刀具铣削Ti6Al4V材料性能研究

Ti6Al4V材料具有良好的性能,广泛应用于航空、航天、医学等领域。这一材料属于典型难加工金属材料,对刀具的切削性能要求较高。对YG6硬质合金刀具干式铣削Ti6Al4V材料时的刀具磨损形态、切削力、切屑形态进行了研究,结果表明,在其它切削参数相同的条件下,低速切削时刀具磨损主要表现为粘结磨损、磨粒磨损、边界磨损。随着切削速度的加快,刀具磨损加剧,主切削刃出现微崩刃。在较高的切削速度和较大的进给量下,刀具磨损进一步加剧,刀尖处磨损最为严重,前刀面出现剥落现象。在进给量和切削深度保持不变的情况下,切削速度加快,切削力出现减小的趋势。YG6刀具切削Ti6Al4V材料容易形成节状切屑,这会对加工表面质量产生不利影响。     

精车45淬硬钢切削力的研究

45淬硬钢的主要性能是硬度高、强度高、脆性大、导热性差、切削加工性差,切削加工困难大。它在切削加工中有以下特点:切削力大、切削温度高,刀具易磨损;淬硬钢径向切削力较大;切削淬硬钢时切屑与前刀面接触长度短,因此切削力和切削温度集中在切削刃附近,易使刀具磨损和崩刃;易获得较高的表面质量。为了获得更高的加工精度和表面粗糙度,论文对45淬硬钢切削力进行了研究。     

硬态干式车削淬硬钢SKD11表面粗糙度试验研究

应用单因素法研究了PCBN 刀具硬态干式切削淬硬钢SKD11过程中,进给量、切削速度、背吃刀量、刀尖圆弧半径和倒棱宽度等参数对表面粗糙度的影响规律.     

GH4169和K4169切削加工性能对比研究

GH4169与K4169分属变形镍基高温合金和铸造镍基高温合金，具有不同的成型工艺和热处理状态。开展了非涂层硬质合金刀具干切削GH4169和K4169的对比试验，从切削力、刀具磨损方面揭示了两种材料加工性能的差异，从力学性能及显微组织两方面解释了差异产生的原因。结果表明，试验各组参数下，两种材料切削力无显著差别；随着切削速度和进给量的增加，切削GH4169时刀具磨损形式由刀尖磨损过渡到后刀面均匀磨损最后转变成沟槽磨损，切削K4169时刀具失效形式主要为后刀面均匀磨损和沟槽磨损，并未出现严重的刀尖磨损，K4169组织中的C化物等硬质点是导致其切削力波动较大及刀具产生沟槽磨损的主要原因。     

精密干式硬态车削淬硬工具钢加工表面温度参数优化

使用PCBN刀具对不同淬硬状态工具钢Cr12MoV进行精密干式硬态车削试验,运用极差法分析切削速度、走刀量、切削深度、试件硬度、刀尖圆弧半径五个因素对工件表面温度影响的显著性,并得到了最优车削参数。试验表明：影响工件表面温度最显著的因素是工件淬火硬度,切削深度与走刀量的影响相当,刀尖圆弧半径的影响最小。     

Effects of cutting edge geometry, workpiece hardness, feed rate and cutting speed on surface roughness and forces in finish turning of hardened AISI H13 steel

In this study, the effects of cutting edge geometry, workpiece hardness, feed rate and cutting speed on surface roughness and resultant forces in the finish hard turning of AISI H13 steel were experimentally investigated. Cubic boron nitrite inserts with two distinct edge preparations and through-hardened AISI H13 steel bars were used. Four-factor (hardness, edge geometry, feed rate and cutting speed) two-level fractional experiments were conducted and statistical analysis of variance was performed. During hard turning experiments, three components of tool forces and roughness of the machined surface were measured. This study shows that the effects of workpiece hardness, cutting edge geometry, feed rate and cutting speed on surface roughness are statistically significant. The effects of two-factor interactions of the edge geometry and the workpiece hardness, the edge geometry and the feed rate, and the cutting speed and feed rate also appeared to be important. Especially honed edge geometry and lower workpiece surface hardness resulted in better surface roughness. Cutting-edge geometry, workpiece hardness and cutting speed are found to be affecting force components. The lower workpiece surface hardness and honed edge geometry resulted in lower tangential and radial forces.     

Experimental investigation of the three-component forces in finish dry hard turning of hardened tool steel at different hardness levels

In this paper, the effects of cutting speed, depth of cut, feed, workpiece hardness (51, 55, 58, 62, and 65?±?1 HRC), tool flank wear, and nose radius on three-component forces in finish dry hard turning (FDHT) of the hardened tool steel AISI D2 were experimentally investigated by utilizing the PCBN inserts. Experimental results showed that the feed force is the lowest in three-component forces and influence of cutting parameters on it is less than two others in the FDHT of AISI D2. Values of the radial force are higher than those of the cutting force when cutting speed, depth of cut, and feed range from 75 to 301 m/min, and 0.10 to 0.40 and 0.05 to 0.20 mm, respectively, but lower in the range between 0.8- and 1.6-mm nose radius. Values of the cutting force are higher than those of the radial force as the workpiece hardness varies from 51 to 58?±?1 HRC while lower in the range between 62 and 65?±?1 HRC. Besides, there are relations between the changing laws of three-component forces and the softening effect of chip, cohesion effect in the tool–chip junction zone, and intenerating effect of metal in the workpiece surface. The high flank wear formation increases the contact with workpiece surface and hence induces tearing–drawing and welding effect duo to instantaneous high temperature.     

Analysis of surface roughness and cutting force components in hard turning with CBN tool: Prediction model and cutting conditions optimization

In this study, the effects of cutting speed, feed rate, workpiece hardness and depth of cut on surface roughness and cutting force components in the hard turning were experimentally investigated. AISI H11 steel was hardened to (40; 45 and 50) HRC, machined using cubic boron nitride (CBN 7020 from Sandvik Company) which is essentially made of 57% CBN and 35% TiCN. Four-factor (cutting speed, feed rate, hardness and depth of cut) and three-level fractional experiment designs completed with a statistical analysis of variance (ANOVA) were performed. Mathematical models for surface roughness and cutting force components were developed using the response surface methodology (RSM). Results show that the cutting force components are influenced principally by the depth of cut and workpiece hardness; on the other hand, both feed rate and workpiece hardness have statistical significance on surface roughness. Finally, the ranges for best cutting conditions are proposed for serial industrial production.     

Use of Ceramic Tools in Hard Turning of Hardened AISI M2 Steel

Tool wear and machining performance of hardened AISI M2 steel in hard turning has been studied. Ceramic tools were used in the cutting tests without coolants, and the workpiece was heat treated to increase its hardness up to Re 60. Cutting forces, temperature, and tool wear were measured in the experiments and the effects of cutting conditions on these were investigated. Important aspects from the research are summarized as follows: 1. Flank wear was the dominant wear mode on the ceramic tool insert in hard turning. In contrast, crater wear was very small due to the ceramics high resistance against chemical reactions at high temperature. A notch was unlikely to be formed in the tool.

2. The initial flank wear rate mainly depends on the feed rate. High feed rates cause a high initial flank wear rate.

3. Depth of cut was the most important cutting parameter to affect cutting force variation, and the cutting force increased due to tool wear.

     

Experimental estimation and optimization of process parameters under minimum quantity lubrication and dry turning of AISI-4340 with different carbide inserts

An experimental study has been performed on AISI 4340 steel in this paper. The influence of approach angle, feed rate, cutting speed and depth of cut has been on cutting forces and tool tip temperature has been experimentally investigated. Before conducting experiments on the AISI 4340 steel work-piece, the chemical composition test, microstructure test were performed and hardness of the work-piece was improved by heat treatment. A total of 64 experiments each by two different coated carbide inserts (PVD and CVD-coated) were conducted on AISI-4340 steel under different environmental conditions (dry and MQL machining). During the experiments, approach angle, cutting speed, feed rate are varied to four levels and the depth of cut is kept constant to investigate the effect of the same on the three cutting forces component and the temperature variations on the tool-tip. It is observed that the main cutting force was largest among the three cutting force components in case of AISI 4340 steel turning and MQL machining show beneficial effects compared to dry machining.     

Artificial neural network based tool condition monitoring in micro mechanical peck drilling using thrust force signals

Micro scale machining process monitoring is one of the key issues in highly precision manufacturing. Monitoring of machining operation not only reduces the need of expert operators but also reduces the chances of unexpected tool breakage which may damage the work piece. In the present study, the tool wear of the micro drill and thrust force have been studied during the peck drilling operation of AISI P20 tool steel workpiece. Variations of tool wear with drilled hole number at different cutting conditions were investigated. Similarly, the variations of thrust force during different steps of peck drilling were investigated with the increasing number of holes at different feed and cutting speed values. Artificial neural network (ANN) model was developed to fuse thrust force, cutting speed, spindle speed and feed parameters to predict the drilled hole number. It has been shown that the error of hole number prediction using a neural network model is less than that using a regression model. The prediction of drilled hole number for new test data using ANN model is also in good agreement to experimentally obtained drilled hole number.     

MACHINING RESIDUAL STRESSES IN AISI 316L STEEL AND THEIR CORRELATION WITH THE CUTTING PARAMETERS

It is well known that machining results in residual stresses in the workpiece. These stresses correlate very closely with the cutting tool geometrical parameters as well as with the machining regime. This paper studies the residual stress induced in turning of AISI 316L steel. Particular attention is paid to the influence of the cutting parameters, such as the cutting speed, feed and depth of cut. In the experiments, the residual stresses have been measured using the X-ray diffraction technique (at the surface of the workpiece and in depth). The effects of cutting conditions on residual stresses are analyzed in association with the experimentally determined cutting forces. The orthogonal components of the cutting force were measured using a piezoelectric dynamometer.     

Performance comparison of conventional and wiper ceramic inserts in hard turning through artificial neural network modeling

Hard turning with ceramic tools provides an alternative to grinding operation in machining high precision and hardened components. But, the main concerns are the cost of expensive tool materials and the effect of the process on machinability. The poor selection of cutting conditions may lead to excessive tool wear and increased surface roughness of workpiece. Hence, there is a need to investigate the effects of process parameters on machinability characteristics in hard turning. In this work, the influence of cutting speed, feed rate, and machining time on machinability aspects such as specific cutting force, surface roughness, and tool wear in AISI D2 cold work tool steel hard turning with three different ceramic inserts, namely, CC650, CC650WG, and GC6050WH has been studied. A multilayer feed-forward artificial neural network (ANN), trained using error back-propagation training algorithm has been employed for predicting the machinability. The input?Coutput patterns required for ANN training and testing are obtained from the turning experiments planned through full factorial design. The simulation results demonstrate the effectiveness of ANN models to analyze the effects of cutting conditions as well as to study the performance of conventional and wiper ceramic inserts on machinability.