微细铣削表面粗糙度预报模型的序列二次规划

微细铣削技术日益广泛地应用于精密微型零件加工,其加工表面质量是研究的重要内容之一。以序列二次规划(Sequential Quadratic Programming,SQP)方法对所建立的微细铣削表面粗糙度预报模型进行数学规划,得到表面粗糙度的最优切削参量组合,以此组合在精密微型铣床上铣削硬铝2Al2,利用激光扫描共聚焦显微镜(OLS3000)测得表面粗糙度的实际加工值与理论最优解之间的误差为8.1%,说明预报模型可靠,可以为微细铣削加工控制表面质量提供理论指导。     

微细铣削工艺参数优化实验研究

以被加工工件表面粗糙度为指标,对微细铣削工艺参数进行了实验研究,采用所研发的基于pmac运动控制器的开放式三轴桌面微细铣削机床,选取轴向切深,径向切深和进给量三个因素安排正交实验,对黄铜H59进行了微细铣削加工。运用白光干涉仪对微细铣削宽槽底部的表面粗糙度进行了测量,通过对测量数据的极差分析和方差分析,确定了各因素对表面粗糙度的影响规律及三个因素对表面粗糙度影响的主次顺序,实验表明径向切深对表面粗糙度值影响最大。     

微细铣削表面形貌形成分析

基于最小切削厚度的概念,提出了微细铣削过程槽底表面几何形貌仿真模型。通过微细铣削表面形貌的仿真和表面粗糙度Ra值的计算以及微细铣削实验,对微细铣削表面粗糙度随着每齿进给量变化的规律进行了分析和描述。     

Study on Surface Roughness of Part in the Micro-end-milling Process

实验研究了用小型数控铣床进行微槽形工件微细端铣削过程中,不同切削条件对工件表面粗糙度的影响。通过对每齿进给量、切削速度、切削深度及刀具直径取不同的值,设计并实施了一系列微槽形工件微细端铣削实验,确定每一因素对表面粗糙度定性、定量的影响特性,分析各因素间交互作用对表面粗糙度值的影响,并确定主要影响因素。根据工件表面粗糙度轨迹特征获悉,刀具跳动不仅影响微细端铣削零件的尺寸精度,同时对零件的表面粗糙度也会造成显著影响,减小刀具跳动对改善零件表面质量意义重大。     

薄片工件的微细铣削加工

为完成在0.2 mm厚不锈钢薄片上铣出深度0.15 mm微细槽的加工任务,选择微细铣削工艺作为加工手段。通过工艺实验,摸索了机床主轴转速、每齿进给量对加工表面粗糙度、加工毛刺的影响规律,并结合微细铣削特点,对实验过程中毛刺特征、表面粗糙度规律进行了阐述。根据实验结果,优选工艺参数,完成了薄片表面微细槽结构的加工。     

PMMA微细铣削实验工艺研究

PMMA材料(聚甲基丙烯酸甲酯)具有低成本、生物相容性和光学特性等优点,常用于制造聚合物微流体装置,通过微细铣削实验,结合最小二乘法拟合,构建表面粗糙度的预测回归模型。以表面粗糙度值为判定依据,在所研发的三轴微细铣削机床上,通过在工件内侧和外侧下刀与顺逆铣相结合共六种不同的微细铣削方式,开展微细铣削工艺研究。研究表明,预测模型较为真实反映实验测量结果,当铣削方式一定时,从工件外侧下刀获得的表面粗糙度值比从工件内侧下刀获得的表面粗糙度值更小,顺逆铣混合时获得最小的表面粗糙度值,逆铣时表面粗糙度值最大。     

微细铣削技术研究综述

微细铣削可以加工三维自由曲面及复杂零件,其应用前景日益广阔。然而由于刀具尺寸及加工参数的急剧减小,微细铣削表现出显著不同于传统铣削的切削特性,因此近年来研究人员对微细铣削技术进行了广泛研究。微细铣削的加工过程复杂,涉及到许多方面,综述国内外在微细铣削方面的研究,总结了微细铣削加工过程中最小切削厚度、切削力、表面粗糙度、刀具磨损及其他方面研究的最新进展。探讨了微细铣削所研究的各方面的发展趋势,并对其应用前景进行了初步讨论。     

基于人工神经网络的微细车铣表面粗糙度预测模型

针对传统切削经验公式无法精确预测微细铣削零件表面粗糙度的问题,提出了一种基于人工神经网络的表面粗糙度预报方法。利用试验选择不同切削参数组合进行铣削试验,将试验结果分为两部分,一部分数据用作BP神经网络的训练样本并最终建立预报模型,另一部分用作测试样本,与相同切削参数条件下的神经网络预测值进行对比。从而证明BP神经网络对于微细铣削表面粗糙度值具有很高的预测精度。     

钛合金精密铣削的表面粗糙度研究

进行了精密铣削钛合金粗糙度试验研究,采用正交分析方法,分析切削三要素铣削速度、进给量、铣削深度对表面粗糙度的影响规律,试验采用直径为2mm的3刃铣刀进行铣削。试验结果表明：表面粗糙度影响顺序依次是进给量、切削速度、切削深度。进给量选为0．01—0．05mm／min时,表面粗糙度随进给量的增大而增大。铣削深度在60—150μm、铣削速度在18．84-47．10m／min试验范围内,最优铣削速度为37．68m／min。而铣削深度对钛合金表面粗糙度影响不大。     

铣削方式对微小精密零件加工表面质量的影响

铣削加工是精密零件加工的重要手段之一,不同铣削加工方式对微小精密零件的表面质量有很大影响。本文通过研究铣刀单齿的运动轨迹模型,分别建立顺铣和逆铣时的粗糙度计算模型,以某微小精密零件实际加工参数为例,对顺逆铣的表面粗糙度进行对比分析;以AISI4340钢为工件材料,对某微小精密零件的顺逆铣进行仿真,研究两种铣削方式的铣削力和表面残余应力。     

2Cr13不锈钢高速铣削表面粗糙度预测模型研究

针对汽轮机叶片常用钢2Cr13不锈钢在切削加工中表面质量存在的问题,对高速铣削条件下2Cr13不锈钢表面粗糙度预测模型进行了研究。将最小二乘支持向量机原理应用到高速铣削2Cr13不锈钢的表面粗糙度预测建模中。得出的模型能方便地预测铣削参数对表面粗糙度的影响,并能利用有限的试验数据得出整个工作范围内的表面粗糙度预测值。经试验验证,应用最小二乘支持向量机原理建立的粗糙度预测模型回归预测精度高。基于最小二乘支持向量机原理建模方法适合于表面粗糙度预测。     

球头铣刀高速铣削玻壳屏凸模表面粗糙度研究

通过用球头铣刀在屏凸模型面做切削试验,研究高速铣削时切削速度、进给量及其它因素对工件表面粗糙度的影响规律,获得了高速铣削屏凸模型面所需的优化切削参数,保证了表面加工精度,提高了加工效率。     

TiAIN涂层铣刀铣削CoCrMo合金切削性能的试验研究

针对医用人工假体材料钴铬钼(CoCrMo)合金的高速铣削加工性能进行了试验研究。深入分析了铣削速度对铣削力、表面粗糙度、表面形貌和刀具的磨损的影响。并获得能够保证对其进行高效高精度加工的合理工艺参数。     

INDUCTION-HEATED TOOL MACHINING OF ELASTOMERS—PART 2: CHIP MORPHOLOGY, CUTTING FORCES, AND MACHINED SURFACES

The induction-heated tool and cryogenically cooled workpiece are investigated for end milling of elastomers to generate desirable shape and surface roughness. Elastomer end milling experiments are conducted to study effects of the cutting speed, tool heating, and workpiece cooling on the chip formation, cutting forces, groove width, and surface roughness. At high cutting speed, smoke is generated and becomes an environmental hazard. At low cutting speeds, induction heated tool, if properly utilized, has demonstrated to be beneficial for the precision machining of elastomer with better surface roughness and dimensional control. Frequency analysis of cutting forces shows that the soft elastomer workpiece has low frequency vibration, which can be correlated to the surface machining marks. The width of end-milled grooves is only 68 to 78% of the tool diameter. The correlation between the machined groove width and cutting force reveals the importance of the workpiece compliance to precision machining of elastomer. This study also explores the use of both contact profilometer and non-contact confocal microscope to measure the roughness of machined elastomer surfaces. The comparison of measurement results shows the advantages and limitations of both measurement methods.     

An experimental investigation on micro machining of fine-grained graphite

Industrial applications of the micro milling process require sufficient experimental data from various micro tools. Research has been carried out on micro milling of various engineering materials in the past two decades. However, there is no report in the literature on micro milling of graphite. This paper presents an experimental investigation on micro machinability of micro milling of moulded fine-grained graphite. Full immersion slot milling was conducted using diamond-coated, TiAlN-coated and uncoated tungsten carbide micro end mills with a uniform tool diameter of 0.5 mm. The experiments were carried out on a standard industrial precision machining centre with a high-speed micro machining spindle. Design of experiments (DoE) techniques were applied to design and analysis of the machining process. Surface roughness, surface topography and burrs formation under varying machining conditions were characterized using white light interferometry, SEM and a precision surface profiler. Influence of variation of cutting parameters including cutting speeds, feedrate and axial depth of cut on surface roughness and surface damage was analysed using ANOVA method. The experimental results show that feedrate has the most significant influence on surface roughness for all types of tools, and diamond tools are not sensitive to cutting speed and depth of cut. Surface damage and burrs analysis show that the primary material removal mode is still brittle fracture or partial ductile in the experimental cutting conditions. 3D intricate micro EDM electrodes were fabricated with good dimensional accuracy and surface finishes using optimized machining conditions to demonstrate that micro milling is an ideal process for graphite machining.     

TiAlN涂层铣刀铣削9SiCr钢切削性能试验研究

采用TiAlN涂层刀具,对合金工具钢9SiCr的高速铣削加工性能进行试验研究,分析铣削速度对铣削力、表面粗糙 度、表面形貌、切屑变形和刀具的磨损的影响。并获得能够保证对其进行高效高精度加工的合理工艺参数。     

SiC颗粒增强铝基复合材料高速铣削实验研究

对SiC颗粒增强铝基复合材料的高速切削加工性能进行了试验分析，研究了铣削速度对铣削力、切削温度、加工表面粗糙度、表面形貌和刀具磨损的影响，从而获得了能够保证对其进行高效率、高精度加工的合理工艺参数。     

INDUCTION-HEATED TOOL MACHINING OF ELASTOMERS—PART 2: CHIP MORPHOLOGY,CUTTING FORCES,AND MACHINED SURFACES

ABSTRACT

The induction-heated tool and cryogenically cooled workpiece are investigated for end milling of elastomers to generate desirable shape and surface roughness. Elastomer end milling experiments are conducted to study effects of the cutting speed, tool heating, and workpiece cooling on the chip formation, cutting forces, groove width, and surface roughness. At high cutting speed, smoke is generated and becomes an environmental hazard. At low cutting speeds, induction heated tool, if properly utilized, has demonstrated to be beneficial for the precision machining of elastomer with better surface roughness and dimensional control. Frequency analysis of cutting forces shows that the soft elastomer workpiece has low frequency vibration, which can be correlated to the surface machining marks. The width of end-milled grooves is only 68 to 78% of the tool diameter. The correlation between the machined groove width and cutting force reveals the importance of the workpiece compliance to precision machining of elastomer. This study also explores the use of both contact profilometer and non-contact confocal microscope to measure the roughness of machined elastomer surfaces. The comparison of measurement results shows the advantages and limitations of both measurement methods.     

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.