高速铣削工件表面粗糙度的预测

表面粗糙度是衡量工件表面质量的重要指标。采用正交试验方法,利用圆环面铣刀对模具钢NAK80进行了高速铣削试验,测量了不同工艺参数下的工件表面粗糙度。将试验结果与人工智能中的BP神经网络结合,建立了表面粗糙度预测模型,用于预测不同主轴转速、进给速度、切削深度、切削行距、刀具倾角时被加工工件的表面粗糙度,并通过MATLAB图形用户界面设计了表面粗糙度预测软件。结果表明,该预测模型及其封装后的软件可用于加工前工件表面粗糙度的预测。     

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。     

TC17钛合金低温铣削表面粗糙度预测

进行了TC17钛合金低温铣削试验,研究了不同切削条件下的已加工表面粗糙度.采用回归分析方法建立了表面粗糙度经验模型,研究了射流温度、每齿进给量、铣削速度和径向切削深度对表面粗糙度的影响规律.基于BP神经网络建立了表面粗糙度预测模型,并与经验模型进行了对比分析.研究结果表明,基于经验模型表面粗糙度值与参数间存在强相关性(...     

基于BP神经网络的微铣削切削比能预测

针对微铣削加工过程中功率和加工能耗变化问题,对微铣削机床主轴系统加工功率进行了采集。建立了主轴转速、每齿进给量和切削深度3个重要切削参数影响切削比能的BP神经网络预测模型。通过45#钢子午线轮胎模具微铣削试验,获得试验数据样本来训练和检测BP神经网络,实现了不同切削参数组合下切削比能的预测,并利用遗传算法对切削参数进行寻优。预测和优化结果表明,最小切削比能可在最大切削参数组合下取得。因此在不考虑表面粗糙度和刀具磨损的情况下,高水平的切削参数组合可获得大的材料去除率和相对较小的切削比能,提高加工效率并降低加工能耗。     

基于ELMAN网络的高速平面铣削表面粗糙度的研究

本文使用人工神经网络方法建立了高速平面铣削条件下切削参数对加工表面粗糙度影响的模型。通过高速切削实验，利用正交试验组合数据组训练神经网络。研究和预测切削速度、切削深度和每齿进给量对加工表面粗糙度的影响，通过实测数据测试了模型的性能，取得了较好的效果，该方法可以用于预测高速平面铣削表面粗糙度。     

基于神经网络的车削加工表面粗糙度智能预测

表面粗糙度是机械加工工艺中主要的技术参数,对零件质量和产品性能有着极为重要的影响。以加工表面粗糙度与切削用量三要素的关系为对象,采用正交试验方法,利用立方氮化硼刀具对冷作模具钢Cr12Mo V进行硬态干式车削试验,测量得到选定参数条件下的加工表面粗糙度值,并应用人工智能神经网络方法建立了加工表面粗糙度预测模型。结果表明,该预测模型具有很好的预测精度,其最大误差不超过5%。模型可以对不同切削速度、进给量和切削深度参数组合下加工后的表面粗糙度进行预测,对干式硬车条件下的切削用量选择和零件表面质量的控制具有重要指导意义。     

新型涂层刀具干式铣削加工SKD11切削性能研究

具有高硬度、耐高温、高耐磨性能和良好韧性的涂层刀具在干式切削加工中的应用越来越多,不同涂层刀具的应用场合及先进涂层的开发已成为目前涂层刀具技术研究的重点。本文基于TiAlN、AlTiN和TiAlN+WC/C三种涂层刀具在干式铣削加工SKD11时的切削力、切削温度和刀具磨损等物理量,对其切削性能作了详细分析。研究结果表明:在干铣加工SKD11时TiAlN+WC/C涂层刀具和AlTiN涂层刀具优于TiAlN涂层刀具,其中AlTiN涂层刀具的涂层材料硬度最高,而且在切削高温影响下生成Al2O3的薄膜层能延长该涂层刀具的寿命;TiAlN+WC/C涂层刀具的切削力小、刀具耐用度高,是干式铣削加工模具钢SKD11的理想刀具。     

润滑方式对高速铣削淬硬钢的影响

使用TiSiN和TiAlN涂层刀具在3种不同冷却润滑方式下,在高速加工中心上采用固定的切削工艺参数,对淬硬钢SKD11(HRC 62)进行切削试验,研究加工工件的表面粗糙度、切削力、刀具磨损及切屑形态的不同.结果 表明:TiSiN涂层铣刀相对于TiAlN涂层能更好地降低已加工面的表面粗糙度,减小切削力,降低刀具的磨损;在微量润滑(MQL)方式下,已加工面的表面粗糙度值低于干切削和冷风切削条件;在减小切削力、降低刀具磨损、改善切屑形态方面,冷风切削的效果优于干切削,MQL润滑方式增大了刀具切削力.     

应用BP神经网络预测高速铣削表面粗糙度

表面粗糙度的预测是切削加工质量分析的重要研究方向,为了在保证铣削的同时预测加工表面的粗糙度、提高生产率,将人工神经网络技术应用于铣削加工领域。应用BP神经网络建立高速铣削加工表面粗糙度预测模型,将预报结果与试验真值进行对比验证,结果表明该方法能够得到较好的预测精度,对高速铣削参数的选择和表面质量的控制具有指导意义。     

CVD涂层刀具高速铣削大理石试验

为了探索高速铣削大理石的切削特性,改善大理石加工表面质量,使用CVD氮化钛涂层刀具进行高速铣削大理石试验。通过显微镜观测刀具磨损表面,并采用网格法计算出刀具磨损面积,探讨切削参数的改变与刀具磨损情况的关系;利用粗糙度测试仪检测大理石加工表面粗糙度,研究切削参数对大理石加工表面质量的影响。最终得到刀具磨损量和大理石表面粗糙度均与切削速度负相关,与进给速度和切削深度正相关。试验结果表明:所建立的数学模型显著性很高,能够较准确地揭示刀具磨损量和大理石表面粗糙度与切削参数间的关系,为合理选择切削参数以提高大理石加工表面质量提供了一定理论基础。     

Investigation of the performance of carbide cutting tools with hard coatings in hard milling based on the response surface methodology

Machining of hard materials has become a great challenge for several decades. One of the problems in this machining process is early tool wear, and this affects the machinability of hard materials. In order to increase machinability, cutting tools are widely coated with nanostructured physical vapor deposition hard coatings. The main characteristics of such advanced hard coatings are high microhardness and toughness as well as good adhesion to the substrate. In this paper, the influence of hard coatings (nanolayer AlTiN/TiN, multilayer nanocomposite TiAlSiN/TiSiN/TiAlN, and commercially available TiN/TiAlN) and cutting parameters (cutting speed, feed rate, and depth of cut) on cutting forces and surface roughness were investigated during face milling of AISI O2 cold work tool steel (～61 HRC). The experiments were conducted based on 3¹³ factorial design by response surface methodology, and response surface equations of cutting forces and surface roughness were obtained. In addition, the cutting forces obtained with the coated and uncoated tools were compared. The results showed that the interaction of coating type and depth of cut affects surface roughness. The hard coating type has no significant effect on cutting forces, while the cutting force F _z is approximately two times higher in the case of uncoated tool.     

高速铣削AF1410高强度钢的实验研究

高强度钢具有优异的机械性能和广阔的应用,但切削加工较为困难,存在加工效率低,加工表面质量差等问题.以AF1410高强度钢为研究对象,应用高速铣削的加工方法,使用涂层硬质合金刀片,对AF1410高强度钢进行了高速铣削实验,研究分析了在高速切削条件下刀具磨损、切削力、切削温度以及已加工表面粗糙度的变化规律.研究发现以TiC...     

Prediction of surface roughness in end face milling based on Gaussian process regression and cause analysis considering tool vibration

Surface roughness is a technical requirement for machined products and one of the main product quality specifications. In order to avoid the costly trial-and-error process in machining parameters determination, the Gaussian process regression (GPR) was proposed for modeling and predicting the surface roughness in end face milling. Cutting experiments on C45E4 steel were conducted and the results were used for training and verifying the GPR model. Three parameters, spindle speed, feed rate, and depth of cut were considered; the experiment results showed that depth of cut is the main factor affecting the surface roughness and regression results showed that the GPR model has a good precision in predicting the surface roughness in different cutting conditions. The prediction accuracy was nearly about 84.3 %. Based on the GPR prediction model, 3D-maps of surface roughness under various cutting parameters could be obtained. It is very concise and useful to select the appropriate cutting parameters according to the maps. As experimental results did not conform to the empirical knowledge, frequency spectrums of the tool were analyzed according to the 3D-maps, it was found that tool vibration is also a crucial factor affecting the machined surface quality.     

球头铣刀高速铣削淬硬Cr12模具钢的研究

通过球头铣刀高速铣削Cr12淬硬模具钢的实验,研究了切削用量对切削力和表面粗糙度的影响变化规律,并分析了产生这些变化的原因。研究结果表明：在球头铣刀高速铣削Cr12淬硬模具钢时,轴向力远远大于径向力,为主切削力;随着切削速度的增加,切削力和表面粗糙度值虽然呈现下降的趋势,但下降趋势不如普通切削时明显;切削力和表面粗糙度值随进给速度的增加而增加;当轴向切深在较小的范围内,切削力和表面粗糙度值随轴向切深增加而变化很小,只有当轴向切深超过一定值以后,切削力和表面粗糙度值才随轴向切深增加而迅速增加。     

模具钢高速切削表面粗糙度的试验研究

用硬质合金刀具对NAK80模具钢进行了高速精密切削试验,研究了切削条件、切削用量对加工表面粗糙度的影响。切削试验结果表明:提高切削速度与减小进给量有利于改善模具钢工件的加工表面质量;当切削速度超过某一范围后,随着切削速度的进一步提高,加工表面粗糙度的降低并不明显。     

Surface topography and roughness of high-speed milled AlMn1Cu

The aluminum alloy AlMn1Cu has been broadly applied for functional parts production because of its good properties. But few researches about the machining mechanism and the surface roughness were reported. The high-speed milling experiments are carried out in order to improve the machining quality and reveal the machining mechanism. The typical topography features of machined surface are observed by scan electron microscope(SEM). The results show that the milled surface topography is mainly characterized by the plastic shearing deformation surface and material piling zone. The material flows plastically along the end cutting edge of the flat-end milling tool and meanwhile is extruded by the end cutting edge, resulting in that materials partly adhere to the machined surface and form the material piling zone. As the depth of cut and the feed per tooth increase, the plastic flow of materials is strengthened and the machined surface becomes rougher. However, as the cutting speed increases, the plastic flow of materials is weakened and the milled surface becomes smoother. The cutting parameters (e.g. cutting speed, feed per tooth and depth of cut) influencing the surface roughness are analyzed. It can be concluded that the roughness of the machined surface formed by the end cutting edge is less than that by the cylindrical cutting edge when a cylindrical flat-end mill tool is used for milling. The proposed research provides the typical topography features of machined surface of the anti-rust aluminum alloy AlMn1Cu in high speed milling.     

An association rule mining and maintaining approach in dynamic database for aiding product–service system conceptual design

Many previous researches on high-speed machining have been conducted to pursue high machining efficiency and accuracy. In the present study, the characteristics of cutting forces, surface roughness, and chip formation obtained in high and ultra high-speed face milling of AISI H13 steel (46–47 HRC) are experimentally investigated. It is found that the ultra high cutting speed of 1,400?m/min can be considered as a critical value, at which relatively low mechanical load, good surface finish, and high machining efficiency are expected to arise at the same time. When the cutting speed adopted is below 1,400?m/min, the contribution order of the cutting parameters for surface roughness Ra is axial depth of cut, cutting speed, and feed rate. As the cutting speed surpasses 1,400?m/min, the order is cutting speed, feed rate, and axial depth of cut. The developing trend of the surface roughness obtained at different cutting speeds can be estimated by means of observing the variation of the chip shape and chip color. It is concluded that when low feed rate, low axial depth of cut, and cutting speed below 1,400?m/min are adopted, surface roughness Ra of the whole machined surface remains below 0.3?μm, while cutting speed above 1,400?m/min should be avoided even if the feed rate and axial depth of cut are low.     

An optimization method of the machining parameters in high-speed machining of stainless steel using coated carbide tool for best surface finish

High-speed machining (HSM) has emerged as a key technology in rapid tooling and manufacturing applications. Compared with traditional machining, the cutting speed, feed rate has been great progress, and the cutting mechanism is not the same. HSM with coated carbide cutting tools used in high-speed, high temperature situations and cutting more efficient and provided a lower surface roughness. However, the demand for high quality focuses extensive attention to the analysis and prediction of surface roughness and cutting force as the level of surface roughness and the cutting force partially determine the quality of the cutting process. This paper presents an optimization method of the machining parameters in high-speed machining of stainless steel using coated carbide tool to achieve minimum cutting forces and better surface roughness. Taguchi optimization method is the most effective method to optimize the machining parameters, in which a response variable can be identified. The standard orthogonal array of L₉ (3⁴) was employed in this research work and the results were analyzed for the optimization process using signal to noise (S/N) ratio response analysis and Pareto analysis of variance (ANOVA) to identify the most significant parameters affecting the cutting forces and surface roughness. For such application, several machining parameters are considered to be significantly affecting cutting forces and surface roughness. These parameters include the lubrication modes, feed rate, cutting speed, and depth of cut. Finally, conformation tests were carried out to investigate the improvement of the optimization. The result showed a reduction of 25.5% in the cutting forces and 41.3% improvement on the surface roughness performance.     

Surface roughness analysis of hardened steel after high-speed milling

The work refers to analysis of various factors affecting surface roughness after end milling of hardened steel in high-speed milling (HSM) conditions. Investigations of milling parameters (cutting speed v(c) , axial depth of cut a(p) ) and the process dynamics that influence machined surface roughness were presented, and a surface roughness model, including cutter displacements, was elaborated. The work also involved analysis of surface profile charts from the point of view of vibrations and cutting force components. The research showed that theoretic surface roughness resulting from the kinematic-geometric projection of cutting edge in the workpiece is significantly different from the reality. The dominant factor in the research was not feed per tooth f(z) (according to the theoretical model) but dynamical phenomena and feed per revolution f.     

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。