20CrMnTi高速外圆磨削试验研究及参数优化

为改善20Cr Mn Ti渗碳合金钢的表面磨削效果,对20Cr Mn Ti合金钢进行了高速外圆磨削试验,分析了磨削工艺参数对表面粗糙度的影响规律。基于高速磨削试验,利用最小二乘多元线性回归方法,推导并求解出了20Cr Mn Ti合金钢的磨削粗糙度预测模型。利用最优化设计方法和MATLAB优化工具箱,以加工效率为目标函数和以粗糙度预测模型为约束条件,针对企业实际的磨削问题优选了工艺参数。优化的工艺参数在保证表面加工质量的基础上可提高加工效率,这为加工企业降低生产成本提供了重要的理论依据和案例参考。     

预测数控加工表面粗糙度的解析模型研究

解析模型是基于刀具切削刃包络面形成的原理来研究零件表面形貌的形成.在解析模型的基础上研究球头刀铣削过程的零件表面生成机理、分析影响加工表面粗糙度大小的因素以及表面粗糙度的趋势,进而预测表面粗糙度,有助于数控加工条件的最优化.本文利用计算机图形学算法进行建模,该模型能够仿真已加工表面轮廓的形成和表面形貌的可视化、预测表面粗糙度和评估加工过程参数的合理性.     

基于响应曲面法的表面粗糙度预测模型研究

分析以往建立表面粗糙度预测模型方法的不足,采用响应曲面法（RSM）建立了钢及其合金铣削加工表面粗糙度预测模型。经检验,该模型预测精度高,泛化能力强,且可简便预测铣削参数对已加工表面的表面粗糙度的影响,有助于准确认识已加工表面质量随铣削参数的变化规律,为切削参数的优选和表面质量的控制提供了依据。     

基于PSO-ELM的316L不锈钢细长管磁粒研磨内表面粗糙度预测模型

针对316L不锈钢细长管磁粒研磨加工过程中,最佳工艺参数难以选择,以及加工后对工件内表面粗糙度（Ra）的预测问题,将影响磁粒研磨316L不锈钢细长管内表面粗糙度的四个工艺参数作为输入值,内表面粗糙度作为输出值,构建粒子群（PSO）优化极限学习机（ELM）模型来预测316L不锈钢细长管内表面粗糙度,利用PSO对工艺参数进行全局寻优,获得最佳工艺参数组合,最后通过试验与预测结果进行对比。构建的PSO-ELM表面粗糙度预测模型拟合优度R2为0.984 8,绝对误差（MAE）为0.013 4,均方根误差（RMSE）为0.021 4。得到的最佳工艺参数组合为：主轴转速2 389.011r/min,进给速度3.167 mm/s,磨料粒径216.185μm,加工时间35.856 min,预测Ra为0.178μm。对工艺参数进行调整,试验得到的Ra为0.182μm,与预测值相比误差为2.24%。基于PSO-ELM方法构建316L不锈钢细长管内表面粗糙度预测模型,实现对工件内表面粗糙度的精确预测,应用粒子群方法得到最佳工艺参数组合,提高了磁粒研磨316L不锈钢细长管的加工效率。     

基于支持向量机的蠕墨铸铁表面粗糙度预测

目的准确预测蠕墨铸铁加工过程中的表面质量,指导加工参数调整,保证加工过程中加工质量的稳定,运用差分进化算法优化的SVM模型(DE-SVM)构建蠕墨铸铁表面粗糙度(Ra)预测模型和加工参数选择方法。方法采用DE-SVM提高支持向量机回归模型的预测精度,建立针对实际加工材料的表面粗糙度预测模型,基于构建的预测模型,挖掘表面粗糙度与加工参数之间的关系,从而获得较优的加工参数。结果结合蠕墨铸铁的铣削加工实验数据,对比DE-SVM与常用优化算法(粒子群优化算法(PSO)和遗传算法(GA))优化的SVM模型,DE-SVM模型获得的MAPE(0.122)和R2(0.9559)值均优于粒子群和遗传算法优化的支持向量模型获得MAPE和R2值。在给定的加工参数范围内,切削速度和进给速度对表面粗糙度的影响较大,且表面粗糙度与切削速度成正比关系,与进给速度成反比,而切削深度对表面粗糙度影响不显著。结论由实验的对比结果可知,采用DE-SVM模型建立的蠕墨铸铁表面粗糙度模型具有更高的预测精度,基于DE-SVM获得的加工参数对表面粗糙度的影响,可有效指导加工参数的选择与调整,对保持蠕墨铸铁优良的加工质量具有较好的指导意义。     

铣削7075铝合金表面粗糙度试验研究

为进一步探究加工参数与7075铝合金表面粗糙度之间的变化关系。开展铣削7075铝合金表面粗糙度试验,基于单因素试验结果分析加工参数与表面粗糙度之间的影响规律,基于含有交互作用的正交试验结果,分析各加工因素最优参数水平,构建表面粗糙度二、三阶响应曲面预测模型。研究表明:表面粗糙度随着切削速度、进给量、切削深度的逐渐增加而增大;表面粗糙度各因素的最优参数水平为A2B1C1;对比分析F值、复相关系数,表面粗糙度三阶响应曲面预测模型优于二阶。确定的最优预测模型为深入研究加工参数与表面粗糙度之间变化关系奠定了理论基础。     

基于人工智能算法的最优加工表面粗糙度预测研究

以切削速度、进给量、切削深度、刀尖圆弧半径为设计变量,采用正交试验法进行了立方氮化硼(CBN)刀具干式车削冷作模具钢Cr12MoV的试验研究。利用神经网络的非线性拟合能力和遗传算法的全局寻优能力,建立了加工表面粗糙度预测模型并获得了使表面粗糙度达到最优的切削用量与刀尖圆弧半径组合。利用遗传算法获得的最优表面粗糙度值比田口方法和切削试验所获得的最佳表面粗糙度值分别降低了7.1%和17.2%。文中所采用的方法也为切削加工中刀具磨损、切削力和残余应力等问题的建模与参数优化提供理论参考。     

正交试验在不锈钢切削参数优化中的应用

不锈钢材料由于强度高、导热系数低、韧性大以及加工硬化严重等原因使得切削加工非常困难.本文采用正交试验研究涂层刀具切削不锈钢材料的参数优化组合,分析了不同切削参数对加工表面质量的影响规律,得出了不锈钢加工的较佳车削参数,最后通过实验验证了其正确性.     

基于QGA-SVR的工件表面粗糙度预测和分析

在自动化生产中建立难加工材料的表面质量预测模型,是实现可持续制造的基础。提出一种结合量子遗传算法和支持向量回归(Quantum genetic algorithm-Support vector regression,QGA-SVR)的已加工表面粗糙度预测模型,改进了现有寻优方法在搜索支持向量回归的模型参数易陷入局部最优解的问题。在量子门更新的过程中加入交叉和变异的操作,保证了模型全局搜索能力,为了提高支持向量回归的泛化能力,在参数优化过程结合了K-折叠交叉验证。结合干车削304不锈钢的切削试验以及现有的铣削实验数据,对比分析了基于量子遗传算法和遗传算法的支持向量回归模型。结果表明:QGA-SVR具有收敛速度快、预测精度高的优点,基于建立的QGA-SVR模型分析了切削参数对车削表面粗糙度的影响规律。     

磨料射流铣削工艺参数优化

目的对表面粗糙度和材料去除率作为输出参数的磨料水射流铣削45#钢过程进行研究,旨在寻找最优加工参数。方法对射流去除材料机理进行了分析,设计并进行了以磨料粒度、射流压力、横向进给距离、靶距为加工工艺参数的田氏正交实验。采用Minitab对不同实验参数组合下磨料水射流加工45#钢的表面粗糙度、材料去除效率进行了数据分析,并从材料去除机理方面,对4种加工工艺参数对于铣削表面质量和材料去除效率的影响程度和影响趋势,以及各因素之间的交互作用进行了分析。结果对射流铣削面表面粗糙度影响较显著的因素是横向进给距离,射流压力次之;对于材料去除效率,磨料粒径的影响最显著,横向进给距离次之。结论综合材料去除效率和表面粗糙度值,选出最优加工参数:磨料粒径2000目,射流压力120~160 MPa,喷嘴横移距离1.0~1.5 mm,靶距约30 mm。     

基于神经网络遗传算法的磁粒研磨TC4材料工艺参数优化

目的利用磁粒研磨光整加工技术提高TC4材料的表面质量,使用BP神经网络建立加工工艺参数和表面粗糙度之间的关系,使用遗传算法寻找最优工艺参数组合。方法使用双级雾化快凝法制备的金刚石磁性磨料对TC4材料工件进行L9(34)正交试验,借助Matlab软件建立结构为4-12-1的BP神经网络,根据正交试验结果训练BP神经网络,探究工艺参数主轴转速n、加工间隙δ、进给速率v、磨料粒径D和表面粗糙度Ra之间的关系。使用决定系数R2评判BP神经网络训练结果,基于训练好的BP神经网络使用遗传算法对工艺参数进行全局寻优。使用计算得到的优化工艺参数进行试验,并测量工件表面粗糙度,与计算得到的表面粗糙度做对比。结果BP神经网络的预测误差在1.5%以下,通过决定系数R2优化的模型可在训练样本较少的情况下进行有效可靠的预测。遗传算法优化的结果,在主轴转速为1021.26 r/min、加工间隙为1.52 mm、进给速率为1.04 mm/min、磨料粒径为197.91μm下,获得最佳表面粗糙度,为0.0951μm。使用调整后的工艺参数,在主轴转速为1020 r/min、加工间隙为1.50 mm、进给速率为1.0 mm/min、磨料粒径为196μm下,试验得到的表面粗糙度为0.093μm,与计算得到的最佳表面粗糙度误差为2.21%。结论采用磁粒研磨光整加工技术与寻优参数结合,可以有效提高TC4材料加工后的表面质量。     

PVA基粘弹性磁性磨具的实验研究及加工参数优化

目的检验新研制的PVA基粘弹性磁性磨具的表面光整加工性能,掌握配比参数、加工条件等因素对加工效果的影响规律,并对加工参数进行优化以达到最佳加工效果。方法以6061铝合金管外圆表面为光整加工实验对象,通过先导实验首先确定出影响加工效果的主要因素及其参数范围,而后基于响应曲面法实验,对主轴转速、两相质量比、磨粒尺寸及加工时长等因素与工件表面粗糙度下降率(%?Ra)之间的关系进行了探究分析。结果最后通过对实验结果进行方差分析,建立了PVA基粘弹性磁性磨具加工铝合金管外表面的%?Ra预测模型,并对影响参数进行了优化设计,得到在最佳实验条件下(加工时间46 min、两相质量比1.45、主轴转速635r/min、磨粒尺寸65目),工件表面粗糙度下降率为92.5%,最低表面粗糙度为59 nm,显著改善了加工效果。结论作为一种新型光整加工介质PVA基粘弹性磁性磨具,其具有良好的自适应性及流动性,能达到较好的光整加工效果。影响%?Ra的单因素显著性从强到弱依次为:加工时长、主轴转速、磨粒尺寸、两相质量比。交互作用显著的因子为两相质量比+主轴转速、加工时长+主轴转速、两相质量比+磨粒尺寸。在主轴转速、加工时长取高水平,两相质量比取中等水平,磨粒尺寸取低或高水平时,能得到较好的表面加工效果。     

Surface roughness prediction of flow-formed AA6061 alloy by design of experiments

Design of experiments has been used to study the effects of the main flow-forming parameters such as the speed of the mandrel, the longitudinal feed, and the amount of coolant used on the surface roughness of flow-formed AA6061 tube. A mathematical prediction model of the surface roughness has been developed in terms of the above parameters. The effect of these parameters on the surface roughness has been investigated using response surface methodology (RSM). Response surface contours were constructed for determining the optimum forming conditions for a required surface roughness. The developed prediction equation shows that the longitudinal feed rate is the most important factor that influences the surface roughness. The surface roughness was found to increase with increase in the longitudinal feed and it decreased with decrease in the amount of the coolant used. The verification experiment carried out to check the validity of the developed model predicted surface roughness within 6% error.     

A novel approach to quantifying tool wear and tool life measurements for optimal tool management

It is a common practice in batch production to continually use the same tool to machine different parts, using disparate machining parameters. In such an environment, the optimal points at which tools have to be changed, while achieving minimum production cost and maximum production rate within the surface roughness specifications, have not been adequately studied. The tool wear index (TWI) and the tool life model developed in this study use a novel approach, analyzing wear surface areas and material loss from the tool using micro-optics and image processing/analysis algorithms. With relation to surface roughness, the TWI measures the wear conditions more accurately and comprehensively, and the tool life model enables maximum use of a worn tool and minimum risk for in-process tool failure. The TWI and a surface roughness control model are integrated into an optimal control strategy that shows potential for productivity improvement and reduction of manufacturing cost.     

Statistical analysis of surface roughness and cutting forces using response surface methodology in hard turning of AISI 52100 bearing steel with CBN tool

The present work concerns an experimental study of hard turning with CBN tool of AISI 52100 bearing steel, hardened at 64 HRC. The main objectives are firstly focused on delimiting the hard turning domain and investigating tool wear and forces behaviour evolution versus variations of workpiece hardness and cutting speed. Secondly, the relationship between cutting parameters (cutting speed, feed rate and depth of cut) and machining output variables (surface roughness, cutting forces) through the response surface methodology (RSM) are analysed and modeled. The combined effects of the cutting parameters on machining output variables are investigated while employing the analysis of variance (ANOVA). The quadratic model of RSM associated with response optimization technique and composite desirability was used to find optimum values of machining parameters with respect to objectives (surface roughness and cutting force values). Results show how much surface roughness is mainly influenced by feed rate and cutting speed. Also, it is underlined that the thrust force is the highest of cutting force components, and it is highly sensitive to workpiece hardness, negative rake angle and tool wear evolution. Finally, the depth of cut exhibits maximum influence on cutting forces as compared to the feed rate and cutting speed.     

Surface finish generated in hard turning of quenched alloy steel parts using conventional and wiper ceramic inserts

Significant progress has already been achieved in green manufacturing including dry and hard, often high-speed, machining technologies. For instance, the demand for higher productivity has resulted in the wider application of ceramic and PCBN tools with special multi-radii (wiper) geometry. This paper reports some important characteristics of the surface roughness produced in the turning of a hardened low-chromium alloy steel using mixed alumina–titanium carbon (TiC) ceramic cutting tools equipped with both conventional and wiper inserts. The characteristic geometrical features of surfaces obtained in both these turning operations have been assessed by means of representative two-dimensional (2D) surface roughness parameters, and some 3D visualizations, which allowed more complete characterization of the surface topography and prediction of its service properties. Results show that keeping equivalent feed rates, i.e. 0.1 mm/rev for conventional and 0.2 mm/rev for wiper tools, the surfaces obtained have similar 3D height roughness parameters, and comparable values of skew and kurtosis. At defined cutting parameters, surfaces produced by wiper tools contain blunt peaks with distinctly smaller slopes resulting in better bearing properties. Only marginal changes of Ra parameter were recorded during 15 min machining trials.     

电火花线切割5083Al合金的实验研究和参数优化(英文)

通过实验分析,优化电火花线切割5083 Al合金的工艺参数。基于Taguchi优化法,将脉宽、脉间、峰电流和线张力作为输入参数进行了系列实验。将表面粗糙度和切割速度作为响应,基于信噪比,测定了输入参数对响应的影响。通过Taguchi优化方法,得到了最大切割速度和最小表面粗糙度的最佳加工参数。再采用附加的模型预测可能的加工组合。最后,通过使用Pareto优化法,得到一个简明的工艺参数表。     

超声滚挤压轴承套圈的表层性能预测模型建立及工艺参数优化

采用超声滚挤压技术对轴承套圈进行表面强化,为了提高其表层性能,实现对工艺参数的优化控制,以轴承套圈材料42CrMo钢为研究对象,通过超声滚挤压正交试验,建立了轴承套圈表层性能与加工参数(主轴转速、进给速度、振幅和静压力)之间的径向基(RBF)神经网络预测模型,并采用方差分析法和田口算法分析了工艺参数对表层性能(表面粗糙...     

Application of an optimized SA-ANN hybrid model for parametric modeling and optimization of LASOX cutting of mild steel

Laser assisted oxygen cutting (LASOX) process is an efficient method for cutting thick mild steel plates compared to conventional laser cutting process. However, scanty information is available as to modeling of the process. The paper presents an optimized SA-ANN model of artificial neural network (ANN) and simulated annealing (SA) to predict and optimize cutting quality of LASOX cutting process of mild steel plates. Optimization of SA-ANN parameters is carried out first where the ANN architecture and initial temperature for SA are optimized. The optimized ANN architecture is further trained using single hidden layer back propagation neural network (BPNN) with Bayesian regularization (BR). The trained ANN is then used to evaluate the objective function during optimization with SA. Experimental dataset employed for the purpose consists of input cutting parameters comprising laser power, cutting speed, gas pressure and stand-off distance while the resulting cutting quality is represented by heat affected zone (HAZ) width, kerf width and surface roughness. Results indicate that the SA-ANN model can predict the optimized output with reasonably good accuracy (around 3%). The proposed approach can be extended for prediction and optimization of operational parameters with reasonable accuracy for any experimental dataset.     

基于灰色关联分析的GH2132线材高精度切削参数优化

目的 通过无心车床车削去除GH2132线材的表面缺陷,分析无心车床加工参数对线材表面粗糙度、尺寸误差和表面显微硬度的响应关系,并建立GH2132线材表面灰色关联度多目标优化模型,确定可行工艺参数域。方法 采用响应曲面中心复合设计,测量车削后GH2132线材的表面粗糙度、尺寸误差和表面显微硬度;利用响应曲面法(Response Surface Method,RSM)分别建立表面粗糙度、尺寸误差和表面显微硬度的单目标预测模型,确定单目标优化最优工艺参数组;基于灰色关联分析(Grey Correlation Analysis,GRA)理论,以表面粗糙度、尺寸误差和表面显微硬度为优化指标进行降维处理,构建车削工艺参数与灰色关联度的二阶回归预测模型;绘制车削工艺参数与灰色关联度值的等值线图,确定可行工艺参数域。结果 对建立的表面粗糙度、尺寸误差和表面显微硬度的单目标预测模型进行方差分析,显著度均小于0.000 1。得到了最小表面粗糙度工艺参数组,切削速度n=373.919 r/min,进给速度vf =0.475 m/min。得到了最小尺寸误差工艺参数组,n=375.636 r/min,vf =0.596 m/min。得到了最大表面显微硬度工艺参数组,n=337 r/min,vf = 0.903 m/min。对于灰色关联度多目标预测模型,误差范围为0.13%~9.4%,确定的可行工艺参数域对应的最小灰色关联度值为0.544 37。结论 基于灰色关联分析的多目标预测模型的准确度较高,主轴转速n对多目标的响应程度大于进给速度vf。通过确定可行工艺参数域,为GH2132线材去除表面缺陷提供工程参考。