精加工表面的分形特征研究

利用触针式轮廓仪和数据采集系统对精车和平磨加工表面的轮廓曲线进行了测量,并基于分形几何理论,分析和讨论了不同工艺参地表面分形维数Ｄ和轮廓算术平均偏差Ｒａ的影响。研究结果表明：Ｄ与Ｒａ反映了表面质量的不同同,它们之间有一定的对应关系,但并不是线性的;对于同种材料,磨削加工的Ｄ值比车削时要大：对于同种加工方法,碳钢材料的Ｄ值比铸铁要大;分形维数有可能揭示材料的可加工性。     

Influence of the milling conditions on the deformation and quality of the machined surface

Russian Engineering Research -     

Investigation of surface integrity in end milling of 55NiCrMoV7 die steel under the cryogenic environments

Abstract

Cryogenic assisted machining is experiencing growing popularity and acceptance as a toxic-free, eco-friendly, hazardless process producing improved structural components. This article deals with the analysis of 3D and 2D roughness profiles, surface morphology, residual stress and microhardness of 55NiCrMoV7 die steel after end milling operation under dry, wet, cryogenic CO₂ and LN₂ cooling environments. Among different cooling methods, the cryogenic CO₂ was seen enhancing the surface topography and morphology due to the presence of minimal wear track. The result indicates the production of an insignificant amount of residual stress in the machined surface by the cooling environments at a spindle speed of 1989?rpm and feed rate of 0.02?mm/rev. The surface microhardness values were higher under cryogenic conditions compared to dry and wet conditions. Cryogenic LN₂ provided the highest microhardness value among the four cooling methods.     

基于 IDRSN-BiLSTM 的铣削加工表面粗糙度预测方法

针对传统的表面粗糙度预测方法过度依赖人工提取特征以及预测精度较低的问题,提出一种基于 Inception 模块改进 的深度残差收缩网络( IDRSN) 和双向长短时记忆网络(BiLSTM) 的表面粗糙度预测方法。 首先,利用深度残差收缩网络 (DRSN)中软阈值化结构和注意力机制对输入信号进行降噪处理。 其次,引入 Inception 模块构建 IDRSN 以提升网络的多尺度 信息获取能力,实现自适应多尺度特征提取。 然后,引入反向长短期记忆(LSTM)构建 BiLSTM 预测网络,利用正反两个 LSTM 提高网络捕捉历史和未来完整信息的能力。 最后,进行实验验证,分别对比 IDRSN、DRSN、BiLSTM 和人工提取特征 4 种方法的 提取特征效果,以及 BiLSTM、卷积神经网络(CNN)、DRSN 和 CNN-LSTM 4 种表面粗糙度预测模型的预测精度。 结果表明所提 方法具有较高的预测精度,为铣削加工表面粗糙度预测奠定了方法基础。     

Position-varying surface roughness prediction method considering compensated acceleration in milling of thin-walled workpiece

Machined surface roughness will affect parts’ service performance. Thus, predicting it in the machining is important to avoid rejects. Surface roughness will be affected by system position dependent vibration even under constant parameter with certain toolpath processing in the finishing. Aiming at surface roughness prediction in the machining process, this paper proposes a position-varying surface roughness prediction method based on compensated acceleration by using regression analysis. To reduce the stochastic error of measuring the machined surface profile height, the surface area is repeatedly measured three times, and Pauta criterion is adopted to eliminate abnormal points. The actual vibration state at any processing position is obtained through the single-point monitoring acceleration compensation model. Seven acceleration features are extracted, and valley, which has the highest R-square proving the effectiveness of the filtering features, is selected as the input of the prediction model by mutual information coefficients. Finally, by comparing the measured and predicted surface roughness curves, they have the same trends, with the average error of 16.28% and the minimum error of 0.16%. Moreover, the prediction curve matches and agrees well with the actual surface state, which verifies the accuracy and reliability of the model.     

基于切削稳定性与表面质量约束的铣削工艺参数优化研究

铣削工艺系统的动态特性随着刀具夹持长度改变而变化,影响关联的铣削稳定性与加工表面质量,进而导致铣削加工的工艺规划具有不确定性.针对此问题,本文综合刀具悬伸量与传统铣削用量作为输入,分别建立极限切削深度与表面粗糙度的反向传播神经网络模型(BPNN),并进一步以其表达铣削稳定性与加工质量约束,构建以刀具悬伸量和粗/精加工阶...     

On-machine tool prediction of flank wear from machined surface images using texture analyses and support vector regression

In this paper, a method for on-machine tool condition monitoring by processing the turned surface images has been proposed. Progressive monitoring of cutting tool condition is inevitable to maintain product quality. Thus, image texture analyses using gray level co-occurrence matrix, Voronoi tessellation and discrete wavelet transform based methods have been applied on turned surface images for extracting eight useful features to describe progressive tool flank wear. Prediction of cutting tool flank wear has also been performed using these eight features as predictors by utilizing linear support vector machine based regression technique with a maximum 4.9% prediction error.     

基于声发射信号的铣削加工表面粗糙度预测技术研究

使用声发射技术对铣削过程进行监测,通过对声发射信号进行频域分析,比较不同频段的能量比来在线预测加工后的表面粗糙度.     

椭球曲面的等间距和双向等间距加工质量的比较

针对非圆曲线的加工问题，以椭球曲面为例进行研究。发现椭球曲面在z轴顶端附近不适合用等位法加工。针对这个问题，采用双向等间距法，在z轴顶端附近区域，采用等缸进行加工，来保证椭球顶部的加工精度；在下半部分，用等Δz进刀加工来满足加工精度要求。利用宏程序模拟了刀路轨迹，并在MVC400机床上，用两种方法加工了椭球曲面。其中双向等间距法得到的曲面质量明显优于等间距法。     

机械加工的表面品质探讨

介绍了机械加工表面品质对零件使用性能的影响,分析了影响切削加工表面品质的因素,并阐述了提高零件加工表面品质的措施。     

三维数字化铣刀成形面展成研究

对采用离散点数据表述的三维数字化铣刀成形面展成进行研究:根据共轭曲面原理,以三维数字化铣刀成形面的展成加工为例,通过仿真计算,获得了实际展成加工中的加工参数,以及实际展成加工的铣刀成形面。该研究完善了传统的解析展成加工理论,并可应用于实际的数字化铣刀的制造与加工工艺。     

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.     

基于准布儒斯特角法研究抛光过程中光学材料的表面质量

光学硬脆材料在机械加工过程中不可避免地形成表面/亚表面损伤,对器件性能、使用寿命等具有至关重要的影响。相较于磨削、研磨两种前道工艺,抛光阶段材料的损伤逐渐减少至极其微量的程度,而最终残留的损伤将伴随材料的使用全周期,研究抛光阶段表面质量的变化过程对掌握抛光的工艺质量十分必要,但测量难度高。针对这一问题,本文在总结抛光阶段损伤形式的基础上,首先仿真分析了准布儒斯特角法检测表面质量的原理和优势,随后以Nd∶GGG激光晶体为研究对象,利用椭偏仪对不同抛光工艺下样品表面质量的变化进行了实验研究。通过与白光干涉法测量的表面形貌进行对比,准布儒斯特角偏移量和相位角变化曲线斜率准确地反映了抛光过程中表面质量的变化,展现了该方法的无损伤和高灵敏度特性,以及辅助研究抛光工艺的可行性。最后,对准布儒斯特角法在表面质量检测方面面临的问题进行了分析和展望。     

Influence of tool inclination on chip formation process and roughness response in ball-end milling of freeform surfaces on Ti-6Al-4V alloy

An experimental test part, which was made up of flat, concave, and convex surfaces of variable curvatures, was designed to be fabricated in a four-axis machining center. Aluminum alloy 7075 and titanium alloy Ti-6Al-4V were used for the fabrication. Within the finishing process, smeared/adhered material was found in some areas of the concave and convex surfaces of the titanium alloy. On the other hand, smeared/adhered material was not found in the aluminum alloy. To characterize the type of defects, the 3D roughness parameters of the surfaces were measured by confocal microscopy, and surface morphology was observed using optical microscopy and scanning electron microscopy. The kinematics of the ball-end milling process was modeled and it was found that the lead and tilt angles between the tool axis and surface normal vector vary continuously, producing transitions between oblique-plunge-push-up milling and oblique-reverse-push-up milling. This causes variation in the thickness behavior during the chip formation process. It was found that the lead angle has a moderate influence, while the tilt angle has a strong effect on the quality of the surfaces of the titanium alloy, with negative tilt angles producing the worst surfaces when there was the oblique-plunge-push-up milling process.     

基于零件使用性能的表面质量决策分析

零件的表面质量是决定其使用性能的重要因素,而零件的使用性能取决于机器的工作需要.从表面质量的内涵出发,分析表面质量与使用性能的内在关系,提出以零件使用性能的具体要求为依据对表面质量进行合理决策的观点.     

Optimization of surface roughness and flank wear using the Taguchi method in milling of Hadfield steel with PVD and CVD coated inserts

In this paper, the Taguchi method and regression analysis have been applied to evaluate the machinability of Hadfield steel with PVD TiAlN- and CVD TiCN/Al₂O₃-coated carbide inserts under dry milling conditions. Several experiments were conducted using the L₁₈ (2 × 3 × 3) full-factorial design with a mixed orthogonal array on a CNC vertical machining center. Analysis of variance (ANOVA) was used to determine the effects of the machining parameters on surface roughness and flank wear. The cutting tool, cutting speed and feed rate were selected as machining parameters. The analysis results revealed that the feed rate was the dominant factor affecting surface roughness and cutting speed was the dominant factor affecting flank wear. Linear and quadratic regression analyses were applied to predict the outcomes of the experiment. The predicted values and measured values were very close to each other. Confirmation test results showed that the Taguchi method was very successful in the optimization of machining parameters for minimum surface roughness and flank wear in the milling the Hadfield steel.     

基于BP神经网络的切削表面粗糙度预测方法

利用神经网络理论,提出一种利用BP神经网络预测切削表面粗糙度的方法。简单分析了粗糙度的影响因素及预测原理。介绍了BP神经网络的特点、原理、算法和公式。在对Matlab及其神经网络工具箱简要介绍的基础上,采用BP网络的方法对钢Q235材料粗糙度进行了训练、预测和分析。结果表明,该方法的预测误差小于3%。