基于PSO-BP神经网络的磨削表面粗糙度预测模型的研究

基于粒子群算法和BP算法相结合,借助粒子群算法优化BP神经网络的初始权值和阈值构建了PSO-BP神经网络磨削表面粗糙度预测模型。以砂轮粒度、砂轮转速、工件速度和径向进给量为正交实验四因素,设计了L_(75)(3~1×5~3)混合水平表,并获取75组实验数据作为该预测模型的训练和测试样本。实验结果表明:与BP神经网络预测模型相比,PSO-BP神经网络预测模型的预测精度更高,其预测值与实测值的平均误差由0.48%降至0.29%。     

球轴承外圈沟道ELID成形磨削工艺参数优化

为了优化球轴承外圈沟道ELID(Electrolytic In?process Dressing)成形磨削工艺参数,通过多因素正交试验研究了ELID成形磨削过程中磨削参数和电解参数对砂轮磨损和工件表面粗糙度的影响规律,综合砂轮径向磨损量和工件表面粗糙度两个指标对磨削试验进行了综合评估.结果表明,磨削参数中的径向进给速度对砂轮径向磨损量的影响最大,砂轮转速对工件表面粗糙度影响最大;电解参数中的占空比对砂轮径向磨损量的影响较大,电解电压对工件表面粗糙度影响较大;砂轮转速为18000 r/min,工件转速为100 r/min,径向进给速度为1μm/min,占空比为50％,电解电压为90 V(6.7Ω)时,综合效果最优.     

磨削参数对GH4169高温合金磨削表面特征影响研究

采用单因素试验法,使用不同特性的砂轮进行GH4169高温合金的外圆磨削试验,研究了单晶刚玉砂轮和CBN砂轮对GH4169高温合金磨削表面特征中表面粗糙度和表面形貌的影响,分析了各磨削工艺参数对表面粗糙度的影响规律,并分析了单晶刚玉砂轮和CBN砂轮切屑的形态,还检测了磨削加工的表面形貌。结果表明：采用粒度为80、中软级、陶瓷结合剂的单晶刚玉砂轮磨削GH4169高温合金时,其磨削表面粗糙度较小,表面特征较稳定;磨削进给运动轨迹构成了试件已加工表面形貌轮廓的主要特征。在工件速度为8~21.66m/min、砂轮速度为15~30m/s、径向进给量为0.005~0.02mm、纵向进给量为1.3~3.6mm/r范围内,可以保证表面粗糙度Ra在0.14μm以内。     

氮化硅陶瓷镶块低粗糙度磨削的研究

提出氧化铝砂轮磨削陶瓷表面的加工过程是砂轮磨粒与工件表面凸峰的碰撞－碰撞与摩擦共同作用－摩擦抛光。对砂轮速度，工件转速、砂轮横向进给量、光磨次数，陶瓷材料硬度以及切削液等因素对表明粗糙度的影响进行了分析，表明氧化铝砂轮通过挤压和磨削抛光作用使陶瓷工件表面的粗糙度得到显著改善，实现了在普通磨床上对陶瓷材料的高质量加工。     

基于正交实验的氧化锆陶瓷套圈内圆磨削研究

采用树脂结合剂金刚石砂轮磨削氧化锆陶瓷套圈内圆,分析了各磨削工艺参数包括砂轮的粒度、线速度(vs)、轴向振荡速(fa)和径向进给速度(fr)对氧化锆套圈内表面粗糙度的影响。利用正交实验,通过回归分析得到加工表面粗糙度的回归方程。实验结果表明,金刚石砂轮的粒度是对加工表面粗糙度影响最大的因素,随着砂轮粒度的减小,加工表面粗糙度呈明显下降的趋势,而砂轮的线速度、轴向振荡速和径向进给速度的变化对加工表面粗糙度的影响均不显著。     

基于回归分析方法的铣削表面粗糙度预测模型的建立

依据正交试验结果,利用回归分析方法建立了高速钢球头铣刀铣削铝合金工件时表面粗糙度的预测模型,并对该模型的回归方程和系数进行了显著性检验.该模型对预报表面粗糙度具有高度显著性:切削深度和走刀行距对表面粗糙度的影响显著,而主轴转速和每齿进给量对表面粗糙度影响不显著.     

基于正交实验的玻璃陶瓷铣削加工表面质量研究

采用正交试验法研究球头铣刀铣削加工牙科玻璃陶瓷时铣削参数对零件加工表面粗糙度的影响。设计了以铣削速度、每齿进给量、切削深度、径向切削宽度为主要因素的正交试验。通过极差分析方法研究了切削参数对表面粗糙度的影响规律,明确了主要影响因素。结果表明:各因素的影响程度从大到小依次为每齿进给量、径向切削宽度、铣削速度、切削深度。并建立了牙科玻璃陶瓷铣削加工表面的表面粗糙度预测模型。     

某航天型号轴类工件的ELID精密磨削加工工艺技术研究

为了探究SiCp/Al复合材料轴类工件精密加工的新途径,采用ELID精密超精密磨削加工技术,对其进行精密加工试验,并分析加工机理及试验影响因素。试验结果表明:砂轮转速、进给量、磨削深度和进给速度是影响表面加工质量的主要因素。当砂轮转速在1 500 r/min、磨削深度是3μm和进给量为0. 25μm时,磨削效果最佳,可以有效地提高加工效率,改善工件表面加工质量,得表面粗糙度R_a为0. 163μm、圆柱度为0. 85μm的已加工表面。     

基于响应曲面法的表面粗糙度预测模型研究

通过正交试验,研究了高速端铣加工中切削参数对表面粗糙度的影响。采用田口设计方法和响应曲面法构建了表面粗糙度预测模型,分析了主轴转速、进给量、切深对表面粗糙度的影响。结果显示,进给量对表面粗糙度的影响最显著,主轴转速次之,切深的影响不大。模型预测精度为99．84％,达到了较高的预测水平。     

工程陶瓷主轴沟道表面磨削加工的实验研究

基于实验室自主设计研发的全陶瓷电主轴,利用曲线磨床对工程陶瓷主轴沟道进行磨削加工以及运用手工研磨的方法进行研磨。研究砂轮转速、工件转速、进给量、横向进给速度等磨削工艺参数对沟道表面粗糙度的影响,以及研磨工艺参数、磨料粒度、研磨时间、主轴转速对沟道表面轮廓度的影响。揭示了磨削参数与研磨参数对氧化锆陶瓷主轴沟道表面质量的影响,为硬脆材料高效的成型磨削加工提供参考依据。     

介观尺度心轴的表面粗糙度预测模型建立及参数优化

为控制惯性约束聚变靶制备中介观尺度心轴的表面粗糙度,提出一种应用旋转设计技术安排试验的方法,通过非线性回归分析,建立基于进给量、背吃刀量、主轴转速和刀尖角四个主要切削参数的介观尺度心轴的表面粗糙度二次预测模型。分析结果表明,该模型的拟合值能较好地反映心轴车削表面粗糙度,并且具有比理论表面粗糙度计算值更高的精度。在主要切削参数中,进给量和刀尖角比背吃刀量和主轴转速对心轴表面粗糙度的影响更显著。利用优化得到的最佳表面粗糙度为目标切削条件,选用直线切削刃超细晶粒硬质合金刀具,在φ0.6 mm的心轴上得到Ra16.53 nm的表面粗糙度。     

微细铣削硬铝时切削用量对表面粗糙度的影响

利用自研的三轴微小型立式数控铣床和微径立铣刀(直径小于1mm),通过双因素及中心复合实验设计的方法,分析微细铣削硬铝LY12过程中的铣削参数(包括每齿进给量、轴向切深、主轴转速、刀具直径以及刀具悬伸量)对工件表面粗糙度的影响,重点探讨了每齿进给量和轴向切深对表面粗糙度的交互影响,并建立了数学模型,为微细铣削工艺参数的选择和表面质量的控制提供了基本依据。     

Development of Empirical Models for Surface Roughness Prediction in Finish Turning

Surface roughness plays an important role in product quality. This paper focuses on developing an empirical model for the prediction of surface roughness in finish turning. The model considers the following working parameters: workpiece hardness (material); feed; cutting tool point angle; depth of cut; spindle speed; and cutting time. One of the most important data mining techniques, nonlinear regression analysis with logarithmic data transformation, is applied in developing the empirical model. The values of surface roughness predicted by this model are then verified with extra experiments and compared with those from some of the representative models in the literature. Metal cutting experiments and statistical tests demonstrate that the model developed in this work produces smaller errors than those from some of the existing models and have a satisfactory goodness in both model construction and verification. Finally, further research directions are presented.     

A Fuzzy-Net-Based Multilevel In-Process Surface Roughness Recognition System in Milling Operations

This paper describes a fuzzy-nets approach for a multilevel in-process surface roughness recognition (FN-M-ISRR) system, the goal of which is to predict surface roughness (Ra ) under multiple cutting conditions determined by tool material, workpiece material, tool size, etc. Surface roughness was measured indirectly by extrapolation from vibration signal and cutting condition data, which were collected in real-time by an accelerometer sensor. These data were analysed and a model was constructed using a neural fuzzy system. Experimental results showed that parameters of spindle speed, feedrate, depth of cut, and vibration variables could predict surface roughness (Ra) under eight different combinations of tool and workpiece characteristics. This neural fuzzy system is shown to predict surface roughness (Ra ) with 90% prediction accuracy during a milling operation.     

Surface roughness and cutting forces modeling for optimization of machining condition in finish hard turning of AISI 52100 steel

An experimental investigation was conducted to analyze the effect of cutting parameters (cutting speed, feed rate and depth of cut) and workpiece hardness on surface roughness and cutting force components. The finish hard turning of AISI 52100 steel with coated Al₂O₃ + TiC mixed ceramic cutting tools was studied. The planning of experiment were based on Taguchi’s L₂₇ orthogonal array. The response table and analysis of variance (ANOVA) have allowed to check the validity of linear regression model and to determine the significant parameters affecting the surface roughness and cutting forces. The statistical analysis reveals that the feed rate, workpiece hardness and cutting speed have significant effects in reducing the surface roughness; whereas the depth of cut, workpiece hardness and feed rate are observed to have a statistically significant impact on the cutting force components than the cutting speed. Consequently, empirical models were developed to correlate the cutting parameters and workpiece hardness with surface roughness and cutting forces. The optimum machining conditions to produce the lowest surface roughness with minimal cutting force components under these experimental conditions were searched using desirability function approach for multiple response factors optimization. Finally, confirmation experiments were performed to verify the pertinence of the developed empirical models.     

金刚石磨头刀具磨铣加工对高体份SiCp/Al复合材料加工表面质量的影响

针对高体份SiCp／Al复合材料,采用佥刚石磨头刀具磨铣切削的加工方法,研究了高速磨铣加工中机床主轴转速、工件进给速度及背吃刀量对材料加工表面形貌损伤以及表面粗糙度的影响规律。研究表明,机床主轴转速的提高、工件进给速度的减小都能够减小材料表面形貌的损伤情况,改善加工表面粗糙度质量：背吃刀量的改变对材料表面形貌损伤以及表面粗糙度的影响不大。     

ALON高陡度薄壁保形非球面的超精密磨削工艺

为了实现新型红外陶瓷ALON高陡度薄壁保形非球面的超精密磨削加工,首先根据ALON的材料属性和高陡度薄壁保形非球面的结构特性,进行了其超精密磨削加工工艺性分析,并基于有限元计算方法,完成了面向ALON高陡度薄壁保形非球面的精密夹具的设计以及关键参数的优化。然后完成了ALON的超精密磨削工艺实验,工艺实验结果表明减小工件转速和砂轮粒度都会降低ALON的平均表面粗糙度Ra值,但砂轮粒度对磨削后ALON的表面粗糙度影响更显著。最后实现了ALON高陡度薄壁保形非球面的超精密磨削加工,磨削后的ALON高陡度薄壁保形非球面的面形精度PV值为2μm,表面粗糙度Ra值可达8.6nm。     

Analysis of surface roughness and cutting force components in hard turning with CBN tool: Prediction model and cutting conditions optimization

In this study, the effects of cutting speed, feed rate, workpiece hardness and depth of cut on surface roughness and cutting force components in the hard turning were experimentally investigated. AISI H11 steel was hardened to (40; 45 and 50) HRC, machined using cubic boron nitride (CBN 7020 from Sandvik Company) which is essentially made of 57% CBN and 35% TiCN. Four-factor (cutting speed, feed rate, hardness and depth of cut) and three-level fractional experiment designs completed with a statistical analysis of variance (ANOVA) were performed. Mathematical models for surface roughness and cutting force components were developed using the response surface methodology (RSM). Results show that the cutting force components are influenced principally by the depth of cut and workpiece hardness; on the other hand, both feed rate and workpiece hardness have statistical significance on surface roughness. Finally, the ranges for best cutting conditions are proposed for serial industrial production.     

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.