TB9钛合金车削系统振动特性与表面粗糙度关系的试验研究

目的 通过车削加工TB9钛合金试验,定量研究不同位置的振动特性对表面粗糙度的影响规律,并建立基于振动参数的表面粗糙度预测模型。方法 选用涂层硬质合金刀具对TB9钛合金线材进行车削加工。通过8704B25和3333A2加速度传感器对试验过程中不同位置的切削振动进行检测。运用Matlab对振动加速度信号进行处理和分析。采用TR2000高精度表面粗糙度仪测量工件表面粗糙度。结果 车削系统不同位置的振动特性均与表面粗糙度存在线性关系。车削系统中刀具振动加速度均方根值、主轴振动加速度均方根值以及后导向振动加速度均方根值与表面粗糙度的Pearson相关系数分别为0.379 93、0.331 90、0.181 95。表面粗糙度预测模型的预测平均百分比误差小于3%。结论 车削加工时刀具、主轴以及后导向的车削振动均对表面粗糙度有一定影响。车削系统不同位置的振动特性对表面粗糙度的影响次序为刀具>主轴>后导向,可见距离切削位置越近的振动对车削加工表面粗糙度的影响越大。基于振动参数的表面粗糙度预测模型的准确度较高,可作为表面粗糙度的预测模型。     

细长轴车削表面粗糙度实验与分析

为了明确加工状态及切削参数对细长轴类零件切削表面粗糙度的影响规律，通过刀具切削刃与工件表面形貌的几何映射关系，推导轴向截面的轮廓曲线方程，得出不同切削参数下的理论表面粗糙度值；对比分析不同加工状态、切削参数下细长轴切削表面粗糙度数据。结果表明：稳定切削时，细长轴工件的振动以主轴转频及其倍频为主，加工表面粗糙度受进给量影响最大，粗糙度随进给量的增大而增大，工件刚度较大时理论粗糙度与实测结果误差较小；当颤振发生时，工件振动信号中出现与其固有频率接近的高频振动成分，此时粗糙度理论预测结果与实测结果误差较大。理论模型中应充分融合工艺系统的振动信息，可进一步提高预测模型的精度与适用范围。     

超声复合磨料振动抛光方法有限元分析与实验

为研究超声复合磨料振动抛光方法对工件表面材料去除量与工件表面粗糙度的影响,分析了超声复合磨料振动抛光方法;并利用ANSYS Workbench软件分别分析了超声振动条件下和超声复合磨料振动条件下工件表面结构与应力变化情况,同时在超声复合磨料振动条件下通过实验验证超声复合磨料振动抛光技术对工件表面材料去除量与工件表面粗糙度的影响程度。结果表明:超声复合磨料振动条件下工件表面位移小于超声振动条件下的工件表面位移,超声复合磨料振动条件下工件表面应力大于超声振动条件下的工件表面应力;在超声复合磨料振动条件下,影响工件表面粗糙度最显著的因素是磨料质量分数,影响工件表面材料去除量最显著的因素是抛光时间,且磨料质量分数为30%、抛光时间为4 h时,抛光效果最佳。     

针对表面粗糙度和刀具振幅的切削用量优化研究

对进给量、切削速度和轴向切深这3个切削参数对工件表面粗糙度和刀具振动幅度的影响进行试验研究。采用BBD响应面法对6061铝工件进行端铣加工试验,并通过数学建模对试验结果进行分析。提出一种基于遗传算法的多目标优化方法来同时减小工件表面粗糙度和刀具振动幅度。建立能预报表面粗糙度和刀具振动的径向基神经网络模型,并通过试验验证其准确性。     

N6镍铣削表面粗糙度与铣削力-铣削振动的耦合特性研究

为了研究铣削工件表面质量与铣削力-铣削振动的耦合关联特性,搭建铣削力-铣削振动-表面粗糙度测试系统,设计铣削三参数全因子试验方案,对N6镍金属进行铣削试验,同步采集三向铣削振动和铣削力信号,利用粗糙度测量仪测量工件表面粗糙度。基于灰色关联分析法,计算工件表面粗糙度与铣削力、铣削振动及铣削参数等多因素的灰色关联度,得到了影响表面粗糙度最显著的因子。基于响应面法,建立铣削工件表面粗糙度关于铣削振动-铣削力的耦合模型。对相关系数值、粗糙度拟合值与实测值的对比曲线及残差散点图等的研究表明：镍金属表面质量与切削振动-铣削力的耦合效应显著,耦合模型拟合数据的优度很高,可以较好预测N6镍金属的表面粗糙度。     

钛合金超声振动研磨表面粗糙度特性试验研究

根据超声振动研磨加工原理,采用自行研制的超声振动研磨装置对塑性难加工材料钛合金(TC4)表面粗糙度特性进行了试验研究。试验采用单因素法,分别研究了工件转速、超声振动振幅以及磨料粒度对工件表面粗糙度的影响规律。试验结果表明:超声振动的附加在一定程度上降低了工件表面粗糙度。所获得的结论对超声振动研磨中加工参数的选择具有一定的参考价值。     

超声研磨Al2O3陶瓷材料的表面粗糙度特征研究

通过对Al2O3陶瓷工件的普通及超声研磨实验，分析了超声研磨加工中各个加工参数和陶瓷工件表面粗糙度的相互关系，得出径向超声振动研磨达到最佳表面粗糙度的工件转速为120～260r／min；径向超声振动研磨要得到最佳表面的研磨压力在450N左右，过大或过小均可引起表面粗糙度恶化。在相同转速和相同压力下，超声研磨工件的表面粗糙度较普通研磨工件的小；当转速较小时，W5与W20研磨的表面粗糙度变化不大，随着转速升高，粗糙度先变小后变大，在n=250n/min左右Ra值最小。研究结论为高效研磨Al2O3陶瓷提供了依据。     

超声辅助铣削碳纤维复合材料的工艺研究

使用自行研制的超声振动发生系统,对碳纤维复合材料进行了铣削加工实验研究,通过建立瞬时铣削模型,分析超声辅助铣削与传统铣削下纤维束的断裂机理,及在不同加工参数下对切削力、工件表面粗糙度的影响。实验结果表明:随着主轴转速的升高,铣削力和工件表面粗糙度减小;随着进给速度的增加,铣削力和工件表面粗糙度增加;随着切深增加,铣削力和工件表面粗糙度增加。与传统铣削相比,在相同的加工参数下,超声辅助铣削加工铣削力和工件表面粗糙度较小。     

二维超声磨削复相陶瓷加工表面质量的试验研究

本文通过对二维超声磨削纳米复相陶瓷和普通磨削进行对比试验研究,分析了磨削深度、工件速度、砂轮粒度对工件表面质量的影响.研究结果表明,采用二维超声振动磨削能大大提高工件的表面质量;表面粗糙度随着切深的增大而增大,随着切削深度的进一步增加,超声振动在磨削加工中所起的作用减弱;二维超声振动磨削大大扩大了复相陶瓷磨削的塑性加工区域,二维超声振动磨削过程的塑性域是切削深度小于5μm,而普通磨削塑性域是磨削深度小于2μm;二维超声振动磨削时,表面粗糙度随着砂轮粒度的减小而明显减小,且比较稳定,故二维超声振动磨削有利于使用细粒度砂轮;工件速度对二维超声振动磨削表面粗糙度影响很大,其值随着工件速度的增加而增大.     

超声振动辅助ELID磨削淬硬12Cr2Ni4A合金钢表面特性的研究

目的 提高淬硬12Cr2Ni4A钢的加工质量,消除工件表面残余应力.方法 采用普通磨削(OG)、超声振动辅助磨削(UVAG)以及超声振动辅助ELID磨削(UVAEG)3种磨削方式,分别对淬硬12Cr2Ni4A合金钢进行加工,分析3种加工方式下被加工工件的表面粗糙度以及残余应力.结果 在超声振动辅助磨削、超声振动辅助ELID磨削下,工件表面粗糙度都低于普通加工,而超声振动辅助ELID磨削后的工件表面质量最高,相对普通磨削加工,超声振动辅助ELID磨削后的表面粗糙度降低了66％,相对于超声振动辅助磨削,超声振动辅助ELID磨削后,表面粗糙度降低了约41％.对工件表面进行残余应力测定发现,普通磨削加工后工件表面为残余拉应力,而超声振动辅助磨削、超声振动辅助ELID磨削后的工件表面都产生了残余压应力,超声振动辅助ELID磨削后,工件表面的残余压应力高于超声振动辅助磨削约30％.普通磨削加工中,随磨削深度的增加,残余拉应力一直变大,而超声振动辅助磨削和超声振动辅助ELID磨削的残余压应力总体呈现减小的趋势.在磨削深度达到22.5μm后,超声振动辅助磨削加工表面的残余压应力转变为残余拉应力.在超声振动辅助磨削和超声振动辅助ELID磨削后,随超声振幅的增大,表面残余压应力增大,超声振动辅助ELID磨削表面的残余压应力随占空比的增大而增大.结论 超声振动辅助ELID磨削加工后,能得到更小的表面粗糙度及更大的表面残余压应力.     

Modeling and experimental validation of the force–surface roughness relation for smoothing burnishing with a spherical tool

The effect of a smoothing-burnishing process strongly depends on the initial roughness of a workpiece. This factor has not been considered by existing classical models of the processes. In this paper, assuming a model of burnishing with a spherical tool, in the form of wedges of surface roughness deformed with a force normal to the base line, expressions describing the relation between burnishing force and displacement of the tops of surface asperities is derived. The expression includes the effect of mechanical properties of the workpiece material, geometry of contact of the tool with the workpiece and the roughness of the burnished surface. Using the derived expressions it is possible to determine an optimum burnishing force. This has been verified experimentally. The experiment made it possible to demonstrate that the optimum burnishing force of the ground 42CrMo4 steel samples was 11–15 daN and that the burnishing effect depends a lot not only on the mechanical properties of the machined workpiece and the geometry of the contact area between the tool and the workpiece but also on the initial surface roughness. The applied optimum burnishing force, calculated on the basis of the theoretical, assumed model-derived dependences, is 12–13 daN. The above proves the validity of the adopted assumptions and the formulas worked out.     

In-process surface roughness prediction using displacement signals from spindle motion

A method to predict surface roughness in real time was proposed and its effectiveness was proved through experiment in this paper. To implement the proposed method in machining process, a sensor system to measure relative displacement caused by the cutting operation was developed. In this research, roughness of machined surface was assumed to be generated by the relative motion between tool and workpiece and the geometric factors of a tool. The relative motion caused by the machining process could be measured in process using a cylindrical capacitive displacement sensor (CCDS). The CCDS was installed at the quill of a spindle and the sensing was not disturbed by the cutting. The workpiece was NAK80 and TiAlN coated carbide end mills were used in the test. Model to predict surface roughness was developed. A simple linear regression model was developed to predict surface roughness using the measured signals of relative motion. Close relation between machined surface roughness and roughness predicted using the measured signals was verified with similarity of about 95%.     

Built-up edge effect on tool wear when turning steels at low cutting speed

In any machining process, it is very important to control the cutting variables used during the process because these will affect, for example, tool life and workpiece surface roughness. Since the built-up edge (BUE) increases the wear of the tool and affects the surface roughness of the workpiece, the study of this phenomenon is very important in predicting and minimizing the wear of a cutting tool. This research studies the influence of the BUE formation for coated carbide tools when turning medium- and high-strength steels. Different mathematical expressions were obtained to quantify this effect. Mathematical expressions for uncoated carbide tools were not possible to obtain, due to the fact that for these tools an increase in the wear and their premature fracture was observed.     

A review of cryogenic cooling in machining processes

The cooling applications in machining operations play a very important role and many operations cannot be carried out efficiently without cooling. Application of a coolant in a cutting process can increase tool life and dimensional accuracy, decrease cutting temperatures, surface roughness and the amount of power consumed in a metal cutting process and thus improve the productivity. In this review, liquid nitrogen, as a cryogenic coolant, was investigated in detail in terms of application methods in material removal operations and its effects on cutting tool and workpiece material properties, cutting temperature, tool wear/life, surface roughness and dimensional deviation, friction and cutting forces. As a result, cryogenic cooling has been determined as one of the most favourable method for material cutting operations due to being capable of considerable improvement in tool life and surface finish through reduction in tool wear through control of machining temperature desirably at the cutting zone.     

Accurate modeling and prediction of surface roughness by computer vision in turning operations using an adaptive neuro-fuzzy inference system

Modeling and prediction of surface roughness of a workpiece by computer vision in turning operations play an important role in the manufacturing industry. This paper proposes a method using an adaptive neuro-fuzzy inference system (ANFIS) to accurately establish the relationship between the features of surface image and the actual surface roughness, and consequently can effectively predict surface roughness using cutting parameters (cutting speed, feed rate, and depth of cut) and gray level of the surface image. Experimental results show that the proposed ANFIS-based method outperforms the existing polynomial network-based method in terms of modeling and prediction accuracy.     

Generalized dynamic model of metal cutting operations

This paper presents a unified mathematical model which allows the prediction of chatter stability for multiple machining operations with defined cutting edges. The normal and friction forces on the rake face are transformed to edge coordinates of the tool. The dynamic forces that contain vibrations between the tool and workpiece are transformed to machine tool coordinates with parameters that are set differently for each cutting operation and tool geometry. It is shown that the chatter stability can be predicted simultaneously for multiple cutting operations. The application of the model to single-point turning and multi-point milling is demonstrated with experimental results.     

A study of back cutting surface finish from tool errors and machine tool deviations during face milling

The surface finish of mechanical components produced by face milling is given by factors such as cutting conditions, workpiece material, cutting geometry, tool errors and machine tool deviations. The contribution of the different tool teeth to imperfections in the machined surface is strongly influenced by tool errors such as radial and axial runouts. The surface profile of milled parts is not only affected by chip removal due to front cutting, but also by back cutting, which must be taken into account when predicting surface roughness. In the present work, the influence of back cutting on the surface finish obtained by face milling operations is studied. Final part surface roughness is modelled from tool runouts and height deviations that affect the surface marks provoked by back cutting. Round insert cutting tools and surface positions defined by cutter axis trajectory are considered, and milling experiments are developed for a spindle speed of 750 rpm, depth of cut of 0.5 mm and feeds from 0.4 to 1.0 mm/rev. Experimental observations are compared with the theoretical predictions provided by the surface roughness model, and good agreement is found between both results. Surface imperfections caused by front and back cutting are analysed, and discrepancies between experiments and numerical predictions are explained by undeformed chip thickness variations along the tool tooth cutting edge, the tearing of the workpiece material, and fluctuations in the feedrate and height deviation during machine tool axis displacement.     

Surface roughness inspection by computer vision in turning operations

The use of computer vision techniques to inspect surface roughness of a workpiece under a variation of turning operations has been reported in this paper. The surface image of the workpiece is first acquired using a digital camera and then the feature of the surface image is extracted. A polynomial network using a self-organizing adaptive modeling method is applied to constructing the relationships between the feature of the surface image and the actual surface roughness under a variation of turning operations. As a result, the surface roughness of the turned part can be predicted with reasonable accuracy if the image of the turned surface and turning conditions are given.     

Effect of the lubrication-cooling technique, insert technology and machine bed material on the workpart surface finish and tool wear in finish turning of AISI 420B

The paper is focussed on the effects produced by cutting operations on workpiece surface finish and tool wear. To this end, finish turning of AISI 420B stainless-steel was carried out under wet, minimum quantity of lubricant and dry cutting conditions, using both conventional and wiper technology inserts, on turning centres equipped with beds made in polymer concrete and cast iron. The workpiece surface finish and tool wear versus cutting volume were measured, and the results analysed and discussed in detail. The most significant results were: (i) the lubrication-cooling technique does not significantly affect the tool wear, whilst wet cutting produces the worst surface finish, (ii) the wiper inserts allow obtaining of the best surface finish, and (iii) the use of polymer concrete bed leads to an improved behaviour in terms of tool wear and surface roughness.