侧铣削参数对TC4钛合金表面粗糙度影响的试验研究

采用细晶粒硬质合金铣刀对TC4进行侧铣削试验,研究了铣削表面粗糙度受铣削参数的影响规律,并结合平均切削厚度、材料去除率及铣削振动进行分析.结果表明工件表面粗糙度随着平均切削厚度的增大而增大,受径向切削深度的影响最为明显.     

PVD涂层刀具高速铣削砂岩表面粗糙度研究

砂岩装饰品已逐渐走进人们的生活,提高砂岩加工表面质量已备受关注。利用正交试验,研究PVD涂层刀具高速铣削砂岩的切削速度、进给速度及切削深度对加工表面粗糙度的影响。分析了表面粗糙度随不同切削参数的变化规律:随着进给速度与切削深度的增加,表面粗糙度值变大,随着切削速度的增加,表面粗糙度值减小,当切削速度由3000r/min提高到12000r/min时,表面粗糙度值由4.98μm减小到3.64μm。建立了表面粗糙度的回归预测模型,经F检验具有较高的显著性,为实际生产加工砂岩制品提供了一定理论依据与参考价值。     

轴向超声辅助端面磨削金属表面形貌及粗糙度预测

轴向超声辅助端面磨削被广泛应用于难加工材料加工,而磨削后的表面粗糙度对构件摩擦、疲劳等服役性能有重要影响。超声振幅的大小对轴向超声辅助端面磨削金属表面形貌和粗糙度有较大影响,但是现有模型中并未考虑实际加载对振幅的影响,因此提出了一种考虑加载状态下振幅变化的轴向超声辅助端面磨削金属表面形貌及粗糙度预测方法。根据砂轮粒度及尺寸建立了考虑磨粒随机分布的砂轮端面模型,并对轴向超声辅助端面磨削磨粒的三维磨削轨迹进行了数学描述,生成了加工后的表面三维数据矩阵并对表面粗糙数值进行了计算。在此基础上,研究了粗糙度随振幅的变化规律,提出了振幅衰减形貌映射系数这一概念,并给出了其标定方法。通过振幅衰减形貌映射系数近似计算出加载状态下的振幅并代入到所建立的轴向超声辅助端面磨削表面形貌及粗糙度预测模型中,实现了金属表面形貌模拟及粗糙度预测。最后,通过试验对所建模型的正确性进行了验证。     

基于刀具磨损映射关系的微细铣削力理论建模与试验研究

微细铣削加工过程中,刀具直径小且磨损较快,刀具磨损对微细铣削力有着明显的非线性影响,同时刀具跳动又对刀具每齿的磨损表现出不同的影响效应,这些影响因素会导致加工过程的不稳定性和精度.然而,目前缺乏考虑具有刀具跳动和磨损效应的通用微细铣削力模型,研究了刀具跳动与刀具每齿磨损量之间的变化规律,提出了一种同时包括刀具跳动和刀具...     

微小型车铣加工表面粗糙度正交试验研究

铣车转速比、工件转速、轴向进给量以及切削深度是车铣加工中相对独立的四个重要切削参数.运用正交试验法研究微小型零件车铣加工表面粗糙度与上述四因素的关系.试验数据分析结果表明,该四因素对表面粗糙度的影响程度从大到小依次为:铣车转速比、轴向进给量、工件转速、切削深度.而且转速比的提高对粗糙度值的影响是单调的,转速比越高粗糙度越低.这一结论也与单因素试验结果相吻合.由试验结果分析还得到各切削参数的优化匹配关系.     

高速铣削时钛合金刀具的磨损及对工件表面粗糙度的影响

观察高速铣削钛合金刀具时后刀面及被加工表面的形貌,通过对刀具后刀面磨损量和被加工表面粗糙度值的测量研究了刀具磨损对被加工表面粗糙度的影响,揭示了钛合金铣削加工时提高表面质量的规律。     

微细干车削硬铝合金LY12的表面粗糙度研究

采用YG类细晶粒硬质合金刀具和PCD刀具对硬铝合金LY12进行了微细干切削试验,通过单因素切削试验研究了不同刀具材料、刀具前角、刀尖圆弧半径对表面粗糙度的影响。结果表明,在微细干切削条件（v=12．6m／min,aP=0．02mm,f=0．004-0．01mm／r）下,采用PCD刀具可获得Ra0．112～0．30μm的光洁表面;采用大前角、大刀尖圆弧半径的PCD刀具,可获得最好的加工表面粗糙度。     

PCD刀具高速铣削钛合金表面粗糙度的研究

通过利用回归正交设计和单因素试验设计方案进行了PCD刀具高速铣削钛合金TA15的粗糙度试验。建立了表面粗糙度与铣削用量之间的数学模型,并进行了方差分析,验证了模型的可靠度。单因素试验中对影响表面粗糙度较大的因素做了重点研究,分析了影响机理。试验为合理选择PCD刀具高速铣削参数,保证良好的加工表面品质提供了依据。     

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。     

高速铣削RoyAlloy模具钢的表面粗糙度研究

为分析高速铣削RoyAlloy模具钢时表面粗糙度的变化规律,在基于刀具位姿的基础上,由正交试验综合研究前倾角、侧偏角、主轴转速、每齿进给量、铣削深度和铣削宽度等铣削参数的影响,并建立了预测模型。     

微细精密铣削表面粗糙度DRSM研究

DRSM方法具有序贯性、可旋转性、模型的稳健性以及试验次数少等优点,近年来逐渐运用在微细精密车铣加工运用中,笔者着重对微细精密铣削表面粗糙度进行DRSM分析,得出了微细精密铣削条件下工艺参数对表面粗糙度的影响规律,并进行了表面粗糙度的预测,有较强的理论实践和现实意义。     

基于粗糙度形成机理的曲面高速铣精加工参数选用

为在曲面精加工中获得理想的表面粗糙度,通过分析表面粗糙度的形成机理,建立了粗糙度与走刀行距、进给率关系的数学模型;通过实验,建立了高速曲面铣削时粗糙度与加工倾角、主运动线速度关系的图谱,实现了在生产过程中按照加工目标的表面粗糙度确定相应的走刀行距、进给率、加工倾角、主运动线速度等加工参数.研究结果表明,该研究对提高加工...     

Correlation between different cutting conditions,surface roughness and dimensional accuracy when ball end micro milling material measures with freeform surfaces

Abstract

Material measures are used to calibrate topography measuring instruments. Micro-milling is a suitable process for the manufacturing of areal material measures in particular of material measures featuring freeform surfaces. To improve their surface quality and to minimize their deviations from the target geometry, the cutting parameters feed per tooth respectively feed rate and spindle speed are examined in this study. Dependent on the varied parameters the deviation of the manufactured topography from its target geometry, the deviation to the nominal surface texture parameters and the short-wavelength roughness parameters are evaluated. Two different ball end micro-mills and two different target geometries are chosen to investigate whether the observed dependence on the varied parameters are valid independent from the tool and the target geometry. It is illustrated that the feed rate has a large influence on the dimensional accuracy: the dynamic properties of the axes are identified as reason for the decreasing amplitude with increasing feed rate. The spindle speed only influences the short wavelength roughness and has a minor influence on the surface quality.     

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.     

Influence of minimum quantity lubrication parameters on tool wear and surface roughness in milling of forged steel

The minimum quantity of lubrication (MQL) technique is becoming increasingly more popular due to the safety of environment.Moreover,MQL technique not only leads to economical benefits by way of saving ...     

铣削加工粗糙度的智能预测方法

提出了一种基于最小二乘支持向量机的铣削加工表面粗糙度智能预测方法.首先进行了铣削工艺参数对工件表面粗糙度影响的正交实验,再通过对主轴转速、进给速率和切削深度三因素,以及各因素之间交互三水平实验的数据分析,找出了铣削工艺参数对工件表面粗糙度影响的一些规律.利用最小二乘支持向量机算法建立了铣削预测模型,通过该模型能在有限实验基础上利用工艺参数方便地得到粗糙度预测值.实际预测表明,在相同情况下,该模型构造速度比反向传播神经网络建模预测方法高2个～3个数量级,预测精度高10倍左右.     

Surface generation modeling of micro milling process with stochastic tool wear

Micro milling is widely used to manufacture miniature parts and features at high quality with low set-up cost. To achieve a higher quality of existing micro products and improve the milling performance, a reliable analytical model of surface generation is the prerequisite as it offers the foundation for surface topography and surface roughness optimization. In the micro milling process, the stochastic tool wear is inevitable, but the deep influence of tool wear hasn't been considered in the micro milling process operation and modeling. Therefore, an improved analytical surface generation model with stochastic tool wear is presented for the micro milling process. A probabilistic approach based on the particle filter algorithm is used to predict the stochastic tool wear progression, linking online measurement data of cutting forces and tool vibrations with the state of tool wear. Meanwhile, the influence of tool run-out is also considered since the uncut chip thickness can be comparable to feed per tooth compared with that in conventional milling. Based on the process kinematics, tool run-out and stochastic tool wear, the cutting edge trajectory for micro milling can be determined by a theoretical and empirical coupled method. At last, the analytical surface generation model is employed to predict the surface topography and surface roughness, along with the concept of the minimum chip thickness and elastic recovery. The micro milling experiment results validate the effectiveness of the presented analytical surface generation model under different machining conditions. The model can be a significant supplement for predicting machined surface prior to the costly micro milling operations, and provide a basis for machining parameters optimization.     

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.     

Optimum surface roughness in end milling Inconel 718 by coupling neural network model and genetic algorithm

In this study, optimum cutting parameters of Inconel 718 are determined to enable minimum surface roughness under the constraints of roughness and material removal rate. In doing this, advantages of statistical experimental design technique, experimental measurements, artificial neural network and genetic optimization method are exploited in an integrated manner. Cutting experiments are designed based on statistical three-level full factorial experimental design technique. A predictive model for surface roughness is created using a feed forward artificial neural network exploiting experimental data. Neural network model and analytical definition of material removal rate are employed in the construction of optimization problem. The optimization problem was solved by an effective genetic algorithm for variety of constraint limits. Additional experiments have been conducted to compare optimum values and their corresponding roughness and material removal rate values predicted from the genetic algorithm. Generally a good correlation is observed between the predicted optimum and the experimental measurements. The neural network model coupled with genetic algorithm can be effectively utilized to find the best or optimum cutting parameter values for a specific cutting condition in end milling Inconel 718.     

三维表面粗糙度的表征和应用

表面粗糙度会直接影响零部件的耐磨性、密封性以及抗腐蚀性等,是评定机械加工和产品质量的重要指标。现代科技水平的不断提高对零件表面性能的要求也日益严苛。传统的二维表面粗糙度的测量和表征已经不再能够满足技术发展的要求,三维表面粗糙度由于能够更加全面、真实地反映工件表面的状态而受到人们的重视,成为研究热点。本文回顾了三维表面粗糙度的发展历史,系统地介绍了三维表面粗糙度参数及标准的发展现状,分析了表面形貌与功能特性的联系,概述了三维粗糙度参数在制造业、生物医疗、摩擦学与材料科学等领域的广泛应用,并进一步指出了三维表面粗糙度表征和应用的发展方向。未来随着相关研究(比如,三维测量的溯源性、重复性、参数表征体系等问题)的深入以及三维表面测量手段的发展,三维表面粗糙度参数也将不断完善和推广,并更多地与实际功能相结合来预测并指导生产,确保工件的表面质量。