高硬刀片干切削淬硬GCr15轴承钢的表面粗糙度预测

采用陶瓷刀片和CBN刀片干切削淬硬GCr15轴承钢,测量了不同切削参数下切削后工件的表面粗糙度;基于微粒群优化算法建立了表面粗糙度预测模型,并与线性回归法建立的经验公式进行了比较;用扫描电子显微镜观察了切屑形态。结果表明:采用微粒群优化算法建立的表面粗糙度预测模型具有一定的可靠性,与线性回归法相比,能更精确地预测出加工工件的表面粗糙度;切削参数中对表面粗糙度影响最大的是进给量,其次是背吃刀量,切削速度的影响最小;锯齿状切屑能降低切削温度,提高工件表面质量;用陶瓷刀片和CBN刀片切削获得的最低表面粗糙度分别可达0.48μm和0.56μm。     

硬态干式车削淬硬钢SKD11表面粗糙度试验研究

应用单因素法研究了PCBN 刀具硬态干式切削淬硬钢SKD11过程中,进给量、切削速度、背吃刀量、刀尖圆弧半径和倒棱宽度等参数对表面粗糙度的影响规律.     

基于响应曲面法的表面粗糙度预测模型的建立与切削参数的优选

文中通过试验设计的方法研究了高速精加工中切削参数的选择对表面粗糙度的影响.采用响应曲面法建立表面粗糙度的响应模型,分析了切削速度、进给量、背吃刀量对表面粗糙度的影响以及切削参数的优选.结果显示背吃刀量对表面粗糙度的影响最为显著,进给量次之,切削速度影响最小.在表面粗糙度选定的情况下,优先选择背吃刀量可以提高加工效率.     

ZL109铝合金切削加工表面粗糙度演变研究

轻质高强ZL109铝合金应用广泛，切削加工过程中易形成积屑瘤，导致加工表面粗糙度不受控。对ZL109铝合金切削加工表面粗糙度演变进行研究，通过改变背吃刀量和进给量，进行ZL109铝合金棒材切削加工，分析表面粗糙度的演变规律，并分析切削温度、表面微观形貌、切屑形态、刀刃损伤对切削表面粗糙度的影响规律。研究结果表明，加工表面粗糙度值随背吃刀量和进给量的增大而增大，且背吃刀量对表面粗糙度的影响较大。当进给量为0.25～0.5 mm/r,背吃刀量为0.25 mm时，加工表面粗糙度值最小，表面完整性最好，并且刀刃损伤程度最轻。     

精密硬态车削多元非线性回归模型与综合优化

采用均匀设计试验方法,分别建立了表面粗糙度、刀具切削行程、刀具径向振动的多元回归模型;以加工表面粗糙度最小、刀具寿命最大、刀具径向振动最小和切削效率最高为目标,对精密硬态车削过程进行了优化研究。结果表明:切削速度280 m/min,进给量0.03 mm/r,背吃刀量0.15 mm,刀尖圆弧半径0.8 mm为最佳参数组合,取得了良好的切削加工综合效果。研究成果可对精密硬态切削加工的参数选择提供借鉴和指导。     

PCBN刀具断续车削Cr12MoV钢表面粗糙度的试验研究

使用PCBN(聚晶立方氮化硼)刀具对不同硬度淬火模具钢Cr12MoV进行强断续车削试验。基于正交设计法,运用Minitab16统计软件对数据进行极差分析,得出工件硬度及切削参数对表面粗糙度的影响程度,实现切削参数的优选;应用多元回归分析法建立表面粗糙度的线性预测模型和指数预测模型,并对这两种模型进行比较。结果表明:影响表面粗糙度值的主次关系是工件硬度、切削速度、背吃刀量、进给量;线性预测模型平均误差不超过7%,能很好地对表面粗糙度进行预测。     

PCD刀具车削超硬铝合金的切削性能研究

以Al7075-T6为加工对象,通过车削试验对PCD刀具车削超硬铝合金的三向动态切削力和表面粗糙度展开研究,建立基于BP神经网络的切削力和表面粗糙度预测模型。结果表明:随着切削用量三要素的变化,切削力变化显著;对于表面粗糙度而言,背吃刀量、进给量和切削速度之间无交互作用;基于L-M优化算法的BP神经网络对样本的拟合度高,且对切削力和表面粗糙度的预测精度高。     

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。     

6061铝合金高速铣削切削参数对表面粗糙度的影响分析

为了分析切削参数对工件表面粗糙度的影响规律,首先借助Deform-3D软件模拟出不同切削参数下铝合金工件的表面粗糙度值;其次采用二次响应曲面(RSM)法建立6061铝合金经高速铣削后的表面粗糙度预测模型并对模型的真实性进行显著性分析,分析铣削参数对工件表面粗糙度的影响;最后通过切削试验对仿真结果进行验证并得出最优参数组合。结果表明:在试验条件下,对6061铝合金铣削表面粗糙度影响的重要程度依次为背吃刀量主轴转速每齿进给量。     

YG8硬质合金刀具干切削铝合金表面粗糙度及刀具磨损研究

采用YG8硬质合金刀具对铝合金进行干切削正交试验,采用共聚焦显微镜测量加工表面粗糙度,分析切削参数进给量、切削速度、背吃刀量对铝合金加工表面粗糙度的影响规律。结果表明:进给量对加工表面粗糙度影响较大,其次是切削速度,而背吃刀量的影响相对较小。采用扫描电子显微镜(SEM)观察刀具磨损表面形貌,采用能谱分析仪(EDS)对刀具磨损表面进行元素分析,分析研究了刀具磨损机理。     

磨料水射流切割碳纤维复合材料的表面粗糙度试验

采用超高压磨料水射流技术对碳纤维复合材料进行切割试验,借助μscan激光共聚焦显微镜重构样品切口的三维表面,测得样品切口表面粗糙度;研究了扫描分辨率对表面粗糙度测量的影响,以及切割速度、样品厚度对样品切口表面粗糙度的影响规律。试验结果表明：扫描分辨率对表面粗糙度的测量无明显的影响;当切割深度较小（0~0.6 mm）时,即在切口入口处,表面粗糙度随切割深度的增大而减小,当切割深度较大（大于0.6 mm）时,表面粗糙度随切割深度的增大而增大;当样品厚度一定时,随着切割速度的增大,切口最大表面粗糙度在整体趋势上是增大的,而样品厚度的大小对表面粗糙度的影响并无明显的规律。     

精密干式硬态车削淬硬工具钢时表面粗糙度的参数优化

使用PCBN刀具对不同淬硬状态工具钢Cr12MoV进行了精密干式硬态车削试验,运用正交实验法分析了切削速度、试件硬度、刀具前角、切削深度4个因素间的交互作用,并得到了最优车削参数.试验表明:影响表面粗糙度最显著的因素是切削速度与淬火硬度,切削深度影响最小.     

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.     

基于标准粒子群算法的GH4169高速铣削表面粗糙度研究

采用单因素和正交实验法,研究了整体硬质合金刀具高速铣削GH4169高温合金时切削参数对表面粗糙度的影响规律,结果表明,每齿进给量对表面粗糙度的影响十分显著,铣削速度和铣削深度对表面粗糙度的影响较小;基于标准粒子群算法建立了表面粗糙度与切削参数之间的经验公式,并对经验公式进行了实验验证,结果显示,基于标准粒子群算法建立的经验公式能有效预测GH4169高温合金高速铣削过程中的表面粗糙度,为铣削参数优化、铣削表面质量控制提供了依据。     

Determining the effect of cutting parameters on surface roughness in hard turning using the Taguchi method

This study focuses on optimizing turning parameters based on the Taguchi method to minimize surface roughness (Ra and Rz). Experiments have been conducted using the L9 orthogonal array in a CNC turning machine. Dry turning tests are carried out on hardened AISI 4140 (51 HRC) with coated carbide cutting tools. Each experiment is repeated three times and each test uses a new cutting insert to ensure accurate readings of the surface roughness. The statistical methods of signal to noise ratio (SNR) and the analysis of variance (ANOVA) are applied to investigate effects of cutting speed, feed rate and depth of cut on surface roughness. Results of this study indicate that the feed rate has the most significant effect on Ra and Rz. In addition, the effects of two factor interactions of the feed rate-cutting speed and depth of cut-cutting speed appear to be important. The developed model can be used in the metal machining industries in order to determine the optimum cutting parameters for minimum surface roughness.     

弧面凸轮高速铣削的试验研究

在五轴联动加工中心上进行了弧面凸轮的高速铣削试验,研究了切削用量对弧面凸轮轮廓表面粗糙度的影响规律。结果表明:提高切削速度、减小切削深度有利于减小轮廓表面粗糙度值;当每齿进给量f<0.08mm时,其对表面粗糙度的影响不明显。     

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.     

The optimization of the surface roughness in the process of tangential turn-milling using genetic algorithm

Turn-milling is a relatively new process in manufacturing technology, where both the workpiece and the tool are given a rotary movement simultaneously. This paper presents an approach for optimization of cutting parameters at cylindrical workpieces leading to minimum surface roughness by using genetic algorithm in the tangential turn-milling process. During testing, the effects of the cutting parameters on the surface roughness were investigated. Additionally, by using genetic algorithms for each of the cutting parameters (depth of cut, workpiece speed, tool speed and feed rate) minimum surface roughness for the process of tangential turn-milling was determined according to the cutting parameters.     

Empirical models and optimal cutting parameters for cutting forces and surface roughness in hard milling of AISI H13 steel

In the present research, an attempt has been made to experimentally investigate the effects of cutting parameters on cutting forces and surface roughness in hard milling of AISI H13 steel with coated carbide tools. Based on Taguchi’s method, four-factor (cutting speed, feed, radial depth of cut, and axial depth of cut) four-level orthogonal experiments were employed. Three cutting force components and roughness of machined surface were measured, and then range analysis and analysis of variance (ANOVA) are performed. It is found that the axial depth of cut and the feed are the two dominant factors affecting the cutting forces. The optimal cutting parameters for minimal cutting forces and surface roughness in the range of this experiment under these experimental conditions are searched. Two empirical models for cutting forces and surface roughness are established, and ANOVA indicates that a linear model best fits the variation of cutting forces while a quadratic model best describes the variation of surface roughness. Surface roughness under some cutting parameters is less than 0.25 μm, which shows that finish hard milling is an alternative to grinding process in die and mold industry.