基于进化神经网络的磁粒研磨表面粗糙度预测方法研究

目的 为实现磁粒研磨光整加工的表面粗糙度精准预测,提出一种遗传算法优化表面粗糙度BP神经网络的预测模型.方法 将表面粗糙度作为预测的目标,影响磁粒研磨5052铝合金管内表面粗糙度的五个主要工艺参数作为神经网络的输入.合理设计正交试验,得到不同工艺参数配置下的表面粗糙度值,将其作为神经网络的输出.通过建立非线性预测模型,对比遗传算法优化后和传统BP神经网络的均方差与仿真时间,分析优化前后表面粗糙度的预测效果.结果 通过试验数据建立了结构为5-11-1的神经网络,进化BP神经网络预测模型均方差为0.044,建模仿真时间为0.187 s,其平均相对误差率为13.2％.传统的BP神经网络预测模型均方差为0.231,建模仿真时间为1.840 s.结论 通过遗传算法优化后的BP神经网络均方差更小,建模仿真时间更短,进化BP神经网络可以实现更为精准的预测,同时能够极大地避免传统BP神经网络易陷入局部极小值的弊端.     

基于WOA–LSSVM的磁粒研磨表面粗糙度预测及工艺参数优化

目的 实现磁粒研磨过程中表面粗糙度值的准确预测,同时获得提高材料表面质量的最优工艺参数组合。方法 通过自由降落气固两相流双级雾化快凝法制备CBN/Fe基磁性磨料,用于磁粒研磨试验。将316L不锈钢作为实验材料,以磁极转速n、加工间隙δ、进给速度v和磁性磨料粒径d为输入值,以表面粗糙度Ra为输出值,设计L25(5⁴)正交试验。同时借助Matlab软件引入鲸鱼优化算法(WOA)与最小二乘支持向量机(LSSVM),基于正交试验结果构建WOA–LSSVM的磁粒研磨表面粗糙度预测模型,并将输出值表面粗糙度 Ra 作为适应度,再次调用WOA对工艺参数进行全局寻优,获得最优工艺参数组合。使用优化得到的工艺参数组合进行试验,并与模型预测结果进行对比。结果 根据正交试验构建的WOA–LSSVM表面粗糙度预测模型的均方根误差(RMSE)为0.003 373,平均绝对百分比误差(MAPE)为2.814%。通过WOA寻优得到了最佳工艺参数组合,n、δ、v、d分别为1 526.690 7 r/min、1.527 414 mm、1.076 732 7 mm/min、114.260 52 μm,此时获得的最佳表面粗糙度为0.063 512 μm。对寻优所得的工艺参数组合微调后进行试验,得到的表面粗糙度Ra为0.062 μm,与模型预测值的相对误差约为2.44%。结论 基于WOA–LSSVM的表面粗糙度预测模型拟合性能优良,可实现磁粒研磨的可控加工。使用磁粒研磨技术结合WOA的寻优结果可获得更优的表面质量。     

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。     

磁粒研磨加工技术的研究进展

磁粒研磨加工是一种应用广泛且高效的表面加工技术,具有加工质量高、适用范围广、柔性加工、自锐性好、易于实现自动化等优点,能够有效去除工件表面的划痕、积碳、毛刺和卷边等缺陷.首先,综述了磁粒研磨加工技术的发展与研究,包括磁粒研磨加工技术的提出与发展、数学模型分析和加工参数产生的影响,其中着重论述了加工过程中单颗磨粒的力学模...     

基于振动传感器的车削加工表面粗糙度预测

文章通过深入研究车床精车外圆时刀具和工件存在相对振动的情况下,加工工件表面轮廓的形成机理,探索出一种建立表面粗糙度值预测模型的新方法。并结合传感器技术,搭建一个能用于测量振动信号的实验平台,通过比较表面粗糙度的预测值和实测值,证明预测模型有一定的准确度。     

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。     

曲面磁性研磨加工的表面粗糙度特性研究

针对大型模具曲面精整加工问题,探讨采用磁性研磨加工模具曲面的工艺.根据磁性研磨加工原理,基于数控铣床研制了磁性研磨实验装置,对曲面的磁性研磨加工进行了实验研究.采用工具旋转的磁性研磨加工方式,磁性磨料受到磁场约束力和离心力的作用,成为影响加工过程正反两方面的因素.经过对磁性研磨加工过程中加工区域的磁感应强度、加工间隙、磁极工具转速及加工次数等参数对曲面工件表面粗糙度影响规律的研究,得到了曲面磁性研磨加工过程优化参数.     

SLM成型零件型腔内表面电解辅助磁粒研磨加工研究

为了去除选区激光熔化技术成型的零件表面缺陷和降低表面粗糙度,并寻求最佳的加工参数。从理论上解析电解辅助磁粒研磨的加工机理,利用仿真软件模拟加工区域的磁感应强度分布,设计Box-Behnken试验方案,先对材料为Ti6Al4V的钛合金工件表面进行电解钝化,后进行机械磁粒研磨,根据试验结果建立表面粗糙度的二次响应回归方程并对建立的数学模型进行方差分析,最后用响应面分析法分析主轴转速、磨料粒径、电解温度和电解电压对表面粗糙度的影响规律,得到最佳的加工参数,在最佳工艺参数下对磁粒研磨和电解辅助磁粒研磨的加工效果进行比较和分析。建立的回归方程调整后的拟合优度为92.14%,经过优化后的电解辅助磁粒研磨最佳加工参数如下:电解液为浓度16%的硝酸钠溶液,电解温度28℃,电解电压12 V,磨料粒径180μm,主轴转速1 100 r/min,使用磁粒研磨加工60 min后,工件表面粗糙度由原始的Ra 10.7μm降为Ra 0.52μm,使用电解辅助磁粒研磨加工60 min后,工件表面粗糙度由原始的Ra10.7μm降为Ra 0.354μm。使用电解辅助磁粒研磨可以有效去除选区激光熔化技术成型零件型腔内表面的缺陷,并降低零件的表面粗糙度,通过响应面分析法可以有效优化加工参数,使用电解辅助磁粒研磨加工比单一磁粒研磨加工的加工效果好,加工效率高。     

基于神经网络的表面粗糙度智能预测系统

表面粗糙度趋势分析及预测技术是计算机集成制造系统故障诊断技术发展的迫切需要。本文在讨论神经网络非线性、多因素预测原理及其拓扑结构的基础上,基于神经网络方法设计了智能型的工件表面粗糙度监测预测系统,将非线性预测和多因素预测引入表面粗糙度预测模型中,即在进行工件表面租糙度预测时兼顾了刀具磨损,从而使本系统拥有可靠和高精度的预测效果。     

基于减小表面粗糙度的铝合金薄壁件切削参数优化

以某铝合金薄壁件为对象,研究通过优化切削用量三要素(切削深度、进给量、切削速度)提高表面粗糙度的方法。针对薄壁件的半精加工和精加工阶段,根据经验切削参数分别设计了16组和25组车削试验,并测量了与之对应的薄壁件表面粗糙度。根据试验结果,建立了表面粗糙度与切削用量三要素之间的关系,并求解出使表面粗糙度最小的切削参数。使用优化后的参数进行车削加工,缺陷率和废品率分别由16.03%和14.23%降至11.25%和7.5%,优化后的切削参数显著提高了薄壁件的合格率。     

Surface roughness analysis,modelling and prediction in selective laser melting

Selective Laser Melting (SLM) is an increasingly employed additive manufacturing process for the production of medical, aerospace, and automotive parts. Despite progresses in material flexibility and mechanical performances, relatively poor surface finish still presents a major limitation in the SLM process.In this study an investigation of surface roughness and morphology is presented for Steel 316L alloy parts made by SLM. In order to characterise the actual surfaces at different sloping angles, truncheon samples have been produced and an analysis has been conducted at different scales, by surface profilometer and scanning electron microscope (SEM). The surface analysis has showed an increasing density of spare particles positioned along the step edges, as the surface sloping angle increases. When layer thickness is comparable to particle diameter, the particles stuck along step edges can fill the gaps between consecutive layers, thus affecting the actual surface roughness.Classic models for roughness prediction, based on purely geometrical consideration of the stair step profile, fail to describe the observed trend of the experimental data. A new mathematical model is developed to include the presence of particles on top surfaces, in addition to the stair step effect, for the accurate prediction of surface roughness. Results show that surface roughness predicted by this model has a good agreement with the experimentally observed roughness. The paper investigates the key contributing factors influencing surface morphology, and a theoretical model for roughness prediction that provides valuable information to improve the surface quality of SLM parts, thus minimising the need of surface finishing.     

基于逐步回归法的TA15钛合金本构模型的建立

采用等温恒应变速率压缩实验研究,揭示了TA15钛合金热变形过程中变形温度、应变速率和应变对流动应力的影响规律.利用逐步回归法确定了影响流动应力的宏观可测变量(变形温度、应变速率和应变)的“最优”自变量子集,基于热压缩实验数据和部分神经网络预测数据,利用最小二乘估计算法求解模型回归参数值,建立了基于逐步回归法的TA15的材料本构模型.实验值与回归计算值的对比表明:所建立的材料本构模型描述TA15钛合金的热变形过程中所发生的动态软化过程(动态再结晶和动态回复)是合理可行的,且所建的材料本构模型形式简单,方便于应用到商用有限元软件所提供接口子程序中.     

Surface roughness prediction using measured data and interpolation in layered manufacturing

Layered manufacturing (LM) technology can efficiently fabricate 3D physical models without the restriction of geometric complexities. However, because of the LM process itself, the surface quality of processed parts is often unsatisfactory compared to that of machined parts made using traditional numerically controlled manufacturing technology. Hence, the surface roughness has become a matter of utmost concern despite the many potential advantages of LM. In the initial step of the LM process, reasonable process planning can be achieved by predicting the surface roughness in advance. Therefore, we propose an elaborate methodology to predict the surface roughness of LM processed parts. Theoretical and real characteristics of surface roughness distributions are investigated to reflect actual roughness distributions in the predictions, and a roughness distribution expression that can obtain surface roughness values for all surface angles is introduced using measured roughness data and interpolation. A prediction application is presented, and the validity and effectiveness of the proposed approach are demonstrated through several application examples.     

基于多元非线性回归分析法的圆锯片锯切花岗岩锯切功率预测模型

根据φ350 mm金刚石圆锯片锯切花岗岩的实验数据,利用多元非线性回归分析法建立了金刚石圆锯片锯切花岗岩的锯切功率预测模型。通过实验数据与模型预测数据比较,验证了该预测模型的可行性。结果表明:多元非线性回归分析法预测模型的拟合效果较好,预测的锯切功率与实验锯切功率相比,相对误差绝对值的平均值仅为2.17%;影响圆锯片锯切功率的最主要因素为进给速度,其次为金刚石磨料的综合强度,最后为锯切深度。      

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.     

基于多元线性回归的6016汽车铝板FLD_0预测模型

成形极限图中FLD0(平面应变处的主应变)的准确性关系到汽车零件CAE仿真分析和安全裕度判定的精度。文章针对15种不同性能的6016汽车铝板,采用相关性分析方法研究了材料厚度、屈强比、延伸率、n值和r值等对6016汽车铝板FLD0的影响规律,并基于多元线性回归方法建立了6016汽车铝板FLD0的预测模型。     

基于逐步回归法研究激光复合焊接工艺参数对焊缝的影响

采用CO2激光-TIG复合焊接3 mm厚的不锈钢板,基于多元线性回归方法,研究了焊接工艺参数对焊缝熔宽及塔深的影响.试验结果表明,对激光-电弧复合焊焊缝熔深影响程度从大到小的焊接工艺参数依次为:激光功率、焊接速度、TIG焊焊接电流和热源间距,其中前2项的影响最为突出;对熔宽影响的焊接程度从大到小的焊接工艺参数依次为:焊接速度、TIG焊焊接电流、热源问距和激光功率,并分别给出了回归教学模型.     

Forecast of shear spinning force and surface roughness of spun cones by employing regression analysis

A series of shear spinning experiments has been performed to produce axi-symmetric cones from blank sheet. The experiments investigated influences of roller nose radius, mandrel revolution and roller feed on the spinning force and the inside/outside surface roughness of spun cone. Statistical analysis was adopted to construct the regression equations governing these parameters. Independent experiments verified the findings. It was discovered that the established regression equations possess a significant degree of reliability.     

基于支持向量回归的二次冷轧轧制力预报模型

为提高二次冷轧兼平整机组在二次冷轧模式下轧制力的预报精度,建立了一种基于摩擦系数自学习的轧制力预报模型。考虑到摩擦系数自学习模型的不足,为进一步提高轧制力的预报精度,提出了一种支持向量回归预测轧制力的计算误差与摩擦系数自学习相结合的轧制力预报方法。结果表明,该模型的计算值与实际值吻合较好,误差控制在±7%以内,满足现场生产要求,具有较高的工程应用价值。     

Surface roughness monitoring using computer vision

In this paper, a comparative experimental surface roughness measurement method based on the speckle pattern caused by a laser beam on a rough surface is presented. Surfaces with known surface roughness are measured using this method to obtain a calibration curve. This information is used to measure surfaces produced by surface grinding, and the results compared with stylus measurements. The online use of this method for tracking the roughness of a workpiece being processed on a surface grinder and to monitor the condition of the grinding wheel is also reported.