数控铣削加工工艺参数优化方法综述

介绍了铣削力、铣削用量等数控铣削加工工艺参数,分析了材料去除率、表面粗糙度、能耗、铣刀颤振等工艺指标,并给出了数控铣削加工工艺参数的优化目标、优化方法、现有试验研究,以及近似模型。所做研究可以为数控铣削加工工艺参数的选择和优化提供理论参考。     

数控铣削加工工艺参数优化方法分析

介绍数控铣削加工工艺和数控铣削加工工艺参数优化方法,通过切削深度及其参数优化、高速铣削加工及其切削参数的确定、五轴铣削加工工艺优化,有效保证数控铣削加工的可靠性与合理性。因此,分析加工工艺参数优化方法对数控铣削具有重要意义。     

数控铣削内拐角加工精度优化研究

如何提高内拐角加工精度一直是数控加工中的难点。文中对影响数控铣削内拐角加工精度原因进行了分析,通过理论证明和实验验证,优化程序和调整参数,改变进给加(减)速变化,可达到提高内拐角加工精度和降低表面粗糙度的目的。     

飞机钛合金结构件侧壁精铣动力学仿真与试验

为了实现飞机结构件数控铣削精加工工艺规范化,获得稳定高效的钛合金精铣切削参数,梳理了飞机钛合金结构件侧壁特征尺寸范围,设计了7种不同精铣余量的切削力仿真与试验方案,分析了顺铣和逆铣情况下切削力随切削余量的变化规律,并进行了试验验证和表面粗糙度测量,其切削力仿真误差仅为0.78%,加工后的表面粗糙度Ra均小于0.4μm,Rz均小于2.9μm,满足飞机零件表面精度要求。试验与测量结果验证了切削力仿真结果的正确性以及工艺参数的可实用性。     

基于正交试验的铝合金行切加工表面粗糙度研究

行切是航空结构件数控加工中的常用工艺方法,本文对铝合金航空结构件数控铣削过程中应用牛鼻铣刀行切加工零件表面特征的现象展开试验。针对不同切削线速度及每齿进给量对表面粗糙度的影响进行对比分析,试验结果显示,表面粗糙度不随理论残留高度的增加而增加,而是维持在一定范围;行距的增加并不会明显降低表面粗糙度;在该试验条件下优先选择较高的切削线速度及较低的每齿进给量能保证较好的表面粗糙度。     

弯管外表面五轴数控铣削表面粗糙度仿真

根据核电主管道弯管外表面环形曲面难加工、表面质量不均匀的特殊性,考虑球头铣刀铣削加工弯管外表面质量的因素(几何因素)进行表面粗糙度数学建模,采用计算机模拟仿真的方法对模型的粗糙度进行分析。在五轴数控铣削情况下,进行路径规划,利用球头铣刀进行加工,通过设置不同工艺参数下的铣削加工条件进行实验验证。结果显示,实验结果与仿真结果具有较高的吻合度,模型输出表面粗糙度值Ra与实测值误差不超过14.6%,在误差允许范围内。该模型可信度高、可靠性强,为弯管外表面数控加工提供理论和实验依据。     

基于最小切削力波动位置的数控铣削参数优化

针对铣削加工中由于切削力波动造成刀具磨损加快、表面质量不佳的问题,提出基于最小切削力波动位置对数控铣削参数进行优化的方法。通过切削运动学仿真,分析不同切削深度下切削截面传递频率的变化过程。基于切削刃螺旋线投影视图模型,根据刀具几何特征参数,计算最小切削力波动位置,用于指导切削深度、切削宽度的匹配,优化数控铣削参数。基于最小切削力波动位置对数控铣削参数进行优化,可以减小切削力波动,提高表面加工质量。     

基于HP滤波法的数控铣削加工误差波动特征分析

数控铣削加工是现代加工技术的重要手段和方法,研究其加工误差的波动规律和特征,有利于提高数控加工的精度和质量。现以表面粗糙度为例,采用HP滤波法对数控铣削加工误差的波动特征进行分析,结果表明,表面粗糙度随进给速度、每齿进给量的增加而增加,随主轴转速的增加而减小;表面粗糙度随进给速度、每齿进给量和主轴转速的变化而产生波动性,其波动程度受主轴转速的影响较大,受每齿进给量的影响较小;在主轴转速为620 r/min时表面粗糙度波动性随进给速度的增加而呈现出稳定性。     

应用响应曲面法优化影响Zr-4锆合金管坯表面粗糙度的磨削工艺参数

为优化影响锆合金管坯外表面粗糙度的磨削工艺参数,提高Zr-4锆合金包壳管的耐腐蚀性能,首先采用中心组合设计方法,在多次抛磨试验结果的基础上,利用Design-Expert 8.0软件建立了与主要磨削工艺参数(砂带线速度、磨削压力、砂带进给速度及管坯旋转速度)具有映射关系的表面粗糙度分析模型;次之,对所建模型进行了优化及可靠性验证;最后通过研究工艺参数间的交互效应对管坯表面粗糙度的影响规律,得到了各工艺参数间的最优组合。试验表明,采用优化后的工艺参数组合进行磨削加工,可将锆合金管坯表面粗糙度有效控制在0.46μm以下。     

整体叶盘数控铣削优化技术探讨

针对整体叶盘数控铣削过程中存在的难题,从最优化的角度出发,结合近年来国内外文献研究,从铣削工艺方案优化和工艺参数优化两方面进行介绍,重点对刀具可达性、加工效率、颤振变形、刀具磨损以及优化模型、优化算法等相关优化技术的研究现状进行综述。通过分析现有研究中存在的一些难题,探讨了整体叶盘数控铣削优化技术的研究方法,对后期开展整体叶盘制造技术研究具有一定的参考意义。     

数字孪生驱动的数控铣削刀具磨损在线监测方法

针对数控铣床不断老化导致刀具磨损预测模型误差较大,加工过程中动态数据难以在线采集等问题,提出一种数字孪生驱动的刀具磨损在线监测方法。采用神经网络对加工过程中的多源数据进行特征提取,建立考虑机床老化的刀具磨损时变偏差量化模型,并在此基础上提出数控铣削刀具磨损的在线预测方法;开发了面向刀具磨损的数控铣削数字孪生系统,在线感知加工过程中的动态数据并实时仿真刀具磨损过程;最后,将该方法应用于实际加工中并与其他的预测方法进行了对比,结果表明该方法有效降低了机床老化带来的误差,实现了刀具磨损的精确预测。     

Roughness modeling and optimization in CNC end milling using response surface method: effect of workpiece material variation

Influence of machining parameters, viz., spindle speed, depth of cut and feed rate, on the quality of surface produced in CNC end milling is investigated. In the present study, experiments are conducted for three different workpiece materials to see the effect of workpiece material variation in this respect. Five roughness parameters, viz., centre line average roughness, root mean square roughness, skewness, kurtosis and mean line peak spacing have been considered. The second-order mathematical models, in terms of the machining parameters, have been developed for each of these five roughness parameters prediction using response surface method on the basis of experimental results. The roughness models as well as the significance of the machining parameters have been validated with analysis of variance. It is found that the response surface models for different roughness parameters are specific to workpiece materials. An attempt has also been made to obtain optimum cutting conditions with respect to each of the five roughness parameters considered in the present study with the help of response optimization technique.     

基于多源同步信号与深度学习的刀具磨损在线识别方法

为提高刀具状态监测系统的实用性、避免实际加工过程中工序变换产生的信号干扰,提出一种基于多源同步信号与深度学习的刀具磨损在线识别方法。该方法利用自动触发的方式实现了机床运行在特定工序时的刀具振动、主轴功率、数控系统参数等多源信号的同步在线采集,保证信号同步性的同时有效避免了因工序变换而产生的信号波动干扰;进一步利用高频振动特征实现了 “切削过程”与“切削间隙”采集样本的准确划分,并基于皮尔逊积矩相关系数筛选出强关联特征,保证了多源监测信号融合样本的可用性;最后基于一维卷积神经网络建立了刀具磨损在线识别模型。实验结果表明,该方法无论从识别精度还是诊断效率,均能实现实际加工过程中刀具磨损状态的在线识别。     

铣削加工粗糙度的智能预测方法

提出了一种基于最小二乘支持向量机的铣削加工表面粗糙度智能预测方法.首先进行了铣削工艺参数对工件表面粗糙度影响的正交实验,再通过对主轴转速、进给速率和切削深度三因素,以及各因素之间交互三水平实验的数据分析,找出了铣削工艺参数对工件表面粗糙度影响的一些规律.利用最小二乘支持向量机算法建立了铣削预测模型,通过该模型能在有限实验基础上利用工艺参数方便地得到粗糙度预测值.实际预测表明,在相同情况下,该模型构造速度比反向传播神经网络建模预测方法高2个～3个数量级,预测精度高10倍左右.     

Surface roughness modeling in Laser-assisted End Milling of Inconel 718

Inconel 718 is a difficult-to-machine material while products of this material require good surface finish. Therefore, it is essential for the evaluation and prediction of surface roughness of machined Inconel 718 workpiece to be developed. An analytical model for the prediction of surface roughness under laser-assisted end milling of Inconel 718 is proposed based on kinematics of tool movement and elastic response of workpiece. The actual tool trajectory is first predicted with the consideration of overall tool movement, elastic deformation of tool, and the tool tip profile. The tool movements include the translation in feed direction and the rotation along its axis. The elastic deformation is calculated based on the previously established milling force prediction model. The tool tip profile is predicted based on the tool tip radius and angle. The machined surface profile is simulated based on the tool trajectory with elastic recovery, which is considered through the comparison between the minimum thickness and actual cutting thickness. Experiments are conducted in both conventional and laser-assisted milling under seven different sets of cutting parameters. Through the comparison between the analytical predictions and experimental measurements, the proposed model has high accuracy with the maximum error less than 27%, which is more accurate for lower feed rate with error less than 3%. The proposed analytical model is valuable for providing a fast, credible, and physics-based method for the prediction of surface roughness in milling process.     

模具钢铣削中刀具磨损的试验研究

以面铣刀刀片磨损为研究对象,结合类神经网络系统建构高速数控铣削加工的预测模型。以加工参数为模型输入条件,刀腹磨耗为输出条件。采用多因素试验方法,选择切削速度、进给速度、切削深度三个试验参数,利用直交表式的试验计划法设计试验点。依照试验点铣削工件后再测量刀具加工后的刀腹磨耗量,进而求得倒传递网络所需的36组训练范例与11组验证数据。刀腹磨耗预测模式是利用类神经网络中的倒传递网络原理,以田口法求得倒传递网络参数的最优值。试验结果显示,刀腹磨耗随着切削速度、进给速度、切削深度增加而上升。铣削模具钢后,刀具磨耗预测值的平均误差为4.72%,最大误差为11.43%,最小误差为0.31%。整体而言,类神经网络对于铣削加工可进行有效预测。     

基于多误差源耦合的五轴数控铣床加工误差综合预测及评判

在综合考虑机床动静态多种误差源的基础上,建立了各运动轴伺服运动模型和多体联动模型,给出了刀具的实际运动位置和姿态,基于包络理论求解了曲面加工实际成形面,对比理想数学模型,对加工误差进行了综合预测和评判。以复杂非可展曲面--S试件为例,给出了S试件的铣削精度构建方法,分析了机床动态因素(位置环、速度环等)对零件铣削精度的影响,并通过切削实验后的数据回归分析予以验证。建立了基于神经网络的机床铣削误差辨识模型,用于评估机床加工后的状态。该平台的搭建为实现大型、关键零件的加工精度预测和保障提供了技术支撑。      

An investigation into material-induced surface roughness in ultra-precision milling

This paper presents a theoretical and experimental investigation into the effect of the workpiece material on surface roughness in the ultra-precision milling process. The influences of material swelling and tool-tip vibration on surface generation in ultra-precision raster milling are studied. A new method is proposed to characterize material-induced surface roughness on the raster-milled surface. A new parameter is defined to characterize the extent of surface roughness profile distortion induced by the materials being cut. An experiment is conducted to compare the proposed method with surface roughness parameters and power spectrum density analysis method by machining three different workpiece materials. The results show that the presence of elastic recovery improves the surface finish in ultra-precision raster milling and that, among the three materials being cut in the experiment, aluminum bronze has the greatest influence on surface finish due to its highest elastic recovery rate and hardness. The results also show that, in the case of faster feed rates, the proposed method more efficiently characterizes material-induced surface roughness.     

A dynamic surface roughness model for face milling

This paper presents a newly developed mathematical model for surface roughness prediction in a face-milling operation. The model considers the static and the dynamic components of the cutting process. The former includes of cutting conditions as well as the edge profile and the amount of runout of each insert set into a cutter body. The latter introduces the dynamic characteristics of the milling process. It is verified that such a model predicts the maximum or the arithmetic mean surface roughness value through the cutting experiments. The model can evaluate the surface texture of the precision parts machined with face milling.     

Investigation of the effect of machining parameters on the surface quality of machined brass (60/40) in CNC end milling—ANFIS modeling

Brass and brass alloys are widely employed industrial materials because of their excellent characteristics such as high corrosion resistance, non-magnetism, and good machinability. Surface quality plays a very important role in the performance of milled products, as good surface quality can significantly improve fatigue strength, corrosion resistance, or creep life. Surface roughness (Ra) is one of the most important factors for evaluating surface quality during the finishing process. The quality of surface affects the functional characteristics of the workpiece, including fatigue, corrosion, fracture resistance, and surface friction. Furthermore, surface roughness is among the most critical constraints in cutting parameter selection in manufacturing process planning. In this paper, the adaptive neuro-fuzzy inference system (ANFIS) was used to predict the surface roughness in computer numerical control (CNC) end milling. Spindle speed, feed rate, and depth of cut were the predictor variables. Experimental validation runs were conducted to validate the ANFIS model. The predicted surface roughness was compared with measured data, and the maximum prediction error for surface roughness was 6.25 %, while the average prediction error was 2.75 %.