复杂曲面五坐标数控铣削表面粗糙度预测的关键技术研究

针对CAD/CAM复杂曲面数控加工刀位点曲率与走刀行距关系进行分析,给出了不同刀具路径下走刀行距的计算方法,并且对影响表面粗糙度的因素进行了讨论,该方法可用于对表面粗糙度的一个范围进行预测.并提出一种基于主曲率匹配的等残留高度刀具路径规划改进算法,该方法能够提高加工效率,改善加工表面粗糙度.     

单向和往复走刀方式的Sn-Bi合金异形曲面铣削加工表面质量与参数优化

为了提高Sn-Bi合金曲面铣削加工表面质量,分别基于单向走刀方式(Zig)和往复走刀方式(Zig-Zag)进行 Sn-Bi合金异形曲面铣削试验,采用田口法设计分析不同铣削因素(主轴转速、铣削深度、进给速度、铣削行距)对表面粗糙度的影响规律.基于稳健性优化原理对实验结果进行分析,研究结果表明:Zig、Zig-Zag铣削方式对表面加工质量具有明显的影响,且Zig铣削质量更高;不同铣削方式下,均有铣削行距对表面粗糙度影响最大,铣削深度、进给速度影响次之,主轴转速影响最小的规律;得到不同铣削方式的最佳工艺参数,理论分析结果与实验结果吻合度较高.     

高速铣削曲面工件的表面粗糙度仿真

在实际生产中,空间曲面一般采用行切法加工。无论是三坐标还是两坐标联动铣削,走刀行距的选择对加工表面粗糙度都有很大影响。本文针对球头铣刀对Bezier双三次曲面工件的高速铣削,结合走刀行距经验公式,提出了一种表面粗糙度的仿真算法,并通过仿真实例进行了验证。     

自由曲面的五坐标端铣加工研究

针对自由曲面的五坐标端铣加工，建立了平底刀有效切削轮廓的数学模型，分析了刀具姿态对有效切削轮廓及加工效率的影响。沿行距方向，通过对被加工曲面法截线的偏置曲线与有效切削轮廓求交，得到一种走刀行距的计算方法。探讨了影响走刀行距的因素，在不发生干涉的前提下，小的后跟角和侧偏角有利于加工行距的提高。结合平底刀加工不发生过切的充要条件和被加工曲面沿行距方向的法曲率，给出了后跟角的自动计算方法。算例表明，所提出的走刀行距及刀具倾角的计算方法合理可行，能够有效提高计算精度和加工效率。     

多线往复摇摆线锯切割水晶玻璃的试验研究

利用多线往复摇摆线锯对不同形状的水晶玻璃进行切割试验,测量了不同线速度、不同进给速比条件下的切片表面粗糙度,研究了加工工艺参数对切片表面粗糙度的影响规律。试验结果表明,摇摆运动会有效地降低切片的整体表面粗糙度,线速度的提高有利于减小切片整体表面粗糙度,进给速比的改变对加工表面不同位置的粗糙度影响较大。单位长度材料去除量与切片表面粗糙度之间有较好的对应关系,减小切割过程中单位长度材料去除量可有效地降低切片的表面粗糙度。利用单位长度材料去除量可有效归整多线往复摇摆线锯加工运动中的多个调控参量。     

高速铣削P20模具钢复杂曲面粗糙度试验研究

应用硬质合金球头铣刀对P20（3Cr2Mo）模具钢进行了高速铣削精加工试验,研究了加工参数（包括主轴转速、每齿进给量和径向进给量）对曲面粗糙度的影响情况,分析了不同加工路径下粗糙度的形成机理。研究结果表明,高速条件下,主轴转速对加工表面粗糙度的影响不明显;每齿进给量和径向进给量对纵向和横向粗糙度的影响呈线性增加关系;为得到较小的粗糙度值,走刀路径应选择被加工曲面曲率半径变化大的方向为进给方向。     

自由曲面五坐标环形刀铣削加工刀位轨迹优化

将圆环面刀等效为变直径平底刀,推导切削行距与进给方向和刀具姿态之间的函数关系式,分析进给方向、刀具前倾角和侧偏角对切削行距大小的影响,给出一种新的检查和消除环形刀瓶颈干涉的方法.在无干涉的前提下,选取较小的前倾角,沿最小主曲率方向进给,切削区域宽度最大,加工效率最高.     

高速铣削工件表面粗糙度的预测

表面粗糙度是衡量工件表面质量的重要指标。采用正交试验方法,利用圆环面铣刀对模具钢NAK80进行了高速铣削试验,测量了不同工艺参数下的工件表面粗糙度。将试验结果与人工智能中的BP神经网络结合,建立了表面粗糙度预测模型,用于预测不同主轴转速、进给速度、切削深度、切削行距、刀具倾角时被加工工件的表面粗糙度,并通过MATLAB图形用户界面设计了表面粗糙度预测软件。结果表明,该预测模型及其封装后的软件可用于加工前工件表面粗糙度的预测。     

磨削工艺参数对硬质合金刀具磨制损伤的影响

硬质合金刀具被广泛应用于航空航天、汽车、冶金等高端制造领域。硬质合金刀具的生产通常采用金刚石砂轮磨削加工,因此,砂轮的磨削工艺参数对刀具的成品质量有重要影响。本试验通过改变砂轮的线速度及进给速度加工硬质合金麻花钻,加工完成后,采用超景深显微镜、白光干涉仪和扫描电镜对钻头的磨削表面以及亚表面进行检测,分析砂轮进给速度及线速度对加工损伤的影响。结果表明：砂轮进给速度和线速度越大,锯齿量越大;当线速度与进给速度较小时,钻头的主切削刃易出现微崩刃现象;砂轮进给速度越大,钻头后刀面表面粗糙度越大;砂轮线速度越大,钻头后刀面表面粗糙度越小;钻头后刀面处的亚表面最大损伤深度随线速度的增大而减小;当线速度30m/s、进给速度160mm/min时,钻头的磨削损伤最小。     

斜平面数控铣削加工的表面精度影响分析

为了指导数控铣削加工编程时刀轨步距的设置,使零件在满足表面粗糙度要求的条件下尽量以最大行距生成数控刀轨,分析了斜平面三轴数控加工中球头刀切削的残余高度和表面粗糙度的计算模型,以及表面残余高度与表面粗糙度随刀具半径和刀轨步距的变化关系,并通过切削试验,验证了刀具半径和刀轨步距对表面粗糙度的影响关系。     

Effects of process parameters on surface roughness in abrasive waterjet cutting of aluminium

Abrasive waterjet cutting is a novel machining process capable of processing wide range of hard-to-cut materials. Surface roughness of machined parts is one of the major machining characteristics that play an important role in determining the quality of engineering components. This paper shows the influence of process parameters on surface roughness (R_a) which is an important cutting performance measure in abrasive waterjet cutting of aluminium. Taguchi’s design of experiments was carried out in order to collect surface roughness values. Experiments were conducted in varying water pressure, nozzle traverse speed, abrasive mass flow rate and standoff distance for cutting aluminium using abrasive waterjet cutting process. The effects of these parameters on surface roughness have been studied based on the experimental results.     

高速干切削铁基粉末冶金零件时细晶粒硬质合金刀具的切削性能研究

用细晶粒硬质合金刀具进行了铁基粉末冶金零件的高速干切削试验。研究了切削参数与刀具耐用度以及加工表面粗糙度的关系,给出了刀具的主要磨损形态,通过能谱分析研究了刀具的磨损机理。结果表明:所选用细晶粒硬质合金刀具具有较高的刀具耐用度和较好的加工表面粗糙度,适合于铁基粉末冶金的加工;细晶粒硬质合金的主要磨损形态是前刀面的月牙洼磨损;主要磨损机理是扩散磨损、粘结磨损。     

Construction of a surface roughness prediction model for high speed machining

In manufacturing environment prediction of surface roughness is very important for product quality and production time. For this purpose, the finite element method and neural network is coupled to construct a surface roughness prediction model for high-speed machining. A finite element method based code is utilized to simulate the high-speed machining in which the cutting tool is incrementally advanced forward step by step during the cutting processes under various conditions of tool geometries (rake angle, edge radius) and cutting parameters (yielding strength, cutting speed, feed rate). The influences of the above cutting conditions on surface roughness variations are thus investigated. Moreover, the abductive neural networks are applied to synthesize the data sets obtained from the numerical calculations. Consequently, a quantitative prediction model is established for the relationship between the cutting variables and surface roughness in the process of high-speed machining. The surface roughness obtained from the calculations is compared with the experimental results conducted in the laboratory and with other research studies. Their agreements are quite well and the accuracy of the developed methodology may be verified accordingly. The simulation results also show that feed rate is the most important cutting variable dominating the surface roughness state.     

An optimization method of the machining parameters in high-speed machining of stainless steel using coated carbide tool for best surface finish

High-speed machining (HSM) has emerged as a key technology in rapid tooling and manufacturing applications. Compared with traditional machining, the cutting speed, feed rate has been great progress, and the cutting mechanism is not the same. HSM with coated carbide cutting tools used in high-speed, high temperature situations and cutting more efficient and provided a lower surface roughness. However, the demand for high quality focuses extensive attention to the analysis and prediction of surface roughness and cutting force as the level of surface roughness and the cutting force partially determine the quality of the cutting process. This paper presents an optimization method of the machining parameters in high-speed machining of stainless steel using coated carbide tool to achieve minimum cutting forces and better surface roughness. Taguchi optimization method is the most effective method to optimize the machining parameters, in which a response variable can be identified. The standard orthogonal array of L₉ (3⁴) was employed in this research work and the results were analyzed for the optimization process using signal to noise (S/N) ratio response analysis and Pareto analysis of variance (ANOVA) to identify the most significant parameters affecting the cutting forces and surface roughness. For such application, several machining parameters are considered to be significantly affecting cutting forces and surface roughness. These parameters include the lubrication modes, feed rate, cutting speed, and depth of cut. Finally, conformation tests were carried out to investigate the improvement of the optimization. The result showed a reduction of 25.5% in the cutting forces and 41.3% improvement on the surface roughness performance.     

Fuzzy-nets-based in-process surface roughness adaptive control system in end-milling operations

A fuzzy-nets-based in-process adaptive surface roughness control (FN-ASRC) system was developed to be able to adapt cutting parameters in-process and in a real time fashion to improve the surface roughness of machined parts when the surface roughness quality was not meeting customer requirements in the end-milling operations. The FN-ASRC system was comprised of two sub-systems: (1) fuzzy-nets in-process surface roughness recognition (FN-IPSRR); and (2) fuzzy-nets adaptive feed rate control (FN-AFRC) sub-system. To test the system, while the machining process was taking place, the FN-IPSRR system predicted the surface roughness, which was then compared to the desired surface roughness. If the desired surface roughness was not met, then, the FN-AFRC system proposed a new feed rate for the machining process. Once the feed rate was changed, and the cutting continued, the output of the surface roughness of the new feed rate was compared with the desired surface roughness. This proposed FN-ASRC system has been demonstrated to be successful using 25 experimental tests with 100% success rate.     

Application of Taguchi and response surface methodologies for surface roughness in machining glass fiber reinforced plastics by PCD tooling

This paper discusses the use of Taguchi and response surface methodologies for minimizing the surface roughness in machining glass fiber reinforced (GFRP) plastics with a polycrystalline diamond (PCD) tool. The experiments have been conducted using Taguchi’s experimental design technique. The cutting parameters used are cutting speed, feed and depth of cut. The effect of cutting parameters on surface roughness is evaluated and the optimum cutting condition for minimizing the surface roughness is determined. A second-order model has been established between the cutting parameters and surface roughness using response surface methodology. The experimental results reveal that the most significant machining parameter for surface roughness is feed followed by cutting speed. The predicted values and measured values are fairly close, which indicates that the developed model can be effectively used to predict the surface roughness in the machining of GFRP composites. The predicted values are confirmed by using validation experiments.     

基于声表面波原理的切削力测量智能刀具研究

切削力的测量对于监测加工过程以及获得高精度的零部件具有重要作用,为实现自适应加工提供切削状态参数。研究了一种基于声表面波原理的切削力测量智能刀具。能在切削加工中实现主切削力的实时测量,并具有无线、无源的测量优势,能够适应复杂的加工环境。建立了切削力与声表面波谐振器石英基片应变的关系模型,分析了声表面波谐振器谐振频率,得到了切削力与声表面波谐振频率偏移量的关系。实验结果表明,基于声表面波原理的切削力测量智能刀具能够实现切削力的实时测量。     

Precision machining of an aluminum alloy piston reinforced with a cast iron insert

Bimetallic pistons consisting of aluminum alloy reinforced with a cast iron (CI) insert are used to reduce the weight and improve the wear resistance of pistons. A major problem with machining such bimetallic pistons is producing the desired shape with minimal cutting forces and without damaging the bonding registry. The objective of this paper is to determine the optimal cutting parameters (cutting speed, feed, and depth of cut) for turning bimetallic pistons. When machining, we wish to obtain optimal values of the cutting forces and a better surface integrity while maintaining the required surface finish. Experiments were conducted following Taguchi’s parameter design approach using a cubic boron nitride tool for the machining. The results indicate that the process parameters affected the mean and variance of the cutting force at the Al-CI interface of the piston. The Al-CI interface was examined using an ultrasonic piston bond tester after machining to assure the bond quality. The surface roughness of the components was measured with a surface roughness tester. A mathematical model was developed using the Systat 12.0 software package to establish the relationship between the input quantities (speed, feed, and depth of cut) and the output data (cutting force). The output data of the mathematical model were compared with the experimental results. The results from the Taguchi robust design concept were compared with the results obtained from a nonconventional Genetic Algorithm optimization technique.     

Optimal cutting condition determination for desired surface roughness in end milling

CNC end milling is a widely used cutting operation to produce surfaces with various profiles. The manufactured parts’ quality not only depends on their geometries but also on their surface texture, such as roughness. To meet the roughness specification, the selection of values for cutting conditions, such as feed rate, spindle speed, and depth of cut, is traditionally conducted by trial and error, experience, and machining handbooks. Such empirical processing is time consuming and laborious. Therefore, a combined approach for determining optimal cutting conditions for the desired surface roughness in end milling is clearly needed. The proposed methodology consists of two parts: roughness modeling and optimal cutting parameters selection. First, a machine learning technique called support vector machines (SVMs) is proposed for the first time to capture characteristics of roughness and its factors. This is possible due to the superior properties of well generalization and global optimum of SVMs. Next, they are incorporated in an optimization problem so that a relatively new, effective, and efficient optimization algorithm, particle swarm optimization (PSO), can be applied to find optimum process parameters. The cooperation between both techniques can achieve the desired surface roughness and also maximize productivity simultaneously.     

大型铝合金薄壁回转体零件表面粗糙度与切削三要素对应关系研究

铝合金薄壁回转体零件广泛应用于核工业、石油化工、航天等众多领域。但其刚性差、硬度低,加工时易产生变形,尤其是大型薄壁件较难保证设计要求的精度和表面粗糙度,因此其切削加工一直是一个难点。针对该类零件精度稳定性差、表面粗糙度不易保证、生产效率低的现状,通过正交试验分析,应用多元线性回归方法拟合出粗糙度公式,得到了大型薄壁回转体零件表面粗糙度与切削三要素对应关系,得出粗糙度的变化规律曲线,从而为切削参数优化控制表面质量提供了理论依据。