基于响应曲面法的表面粗糙度预测模型研究

分析以往建立表面粗糙预测模型方法的不足,采用响应曲面法（RSM）建立了钢及其合金铣削加工表面粗糙度预测模型。经检验,该模型预测精度高,泛化能力强,且可简便预测铣削参数对已加工表面的表面粗糙度的影响,有助于准确认识已加工表面质量随铣削参数的变化规律,为切削参数的优先和表面质量的控制提供了依据。     

基于最小二乘支持向量机的铣削加工表面粗糙度预测模型

在分析以往所建立的表面粗糙度预测模型方法不足的基础上，将一种基于最小二乘支持向量机的预测模型引入铣削加工领域，并给出了相应的步骤和算法。该模型能方便地预测铣削加工参数对加工表面粗糙度的影响，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，有助于准确认识已加工表面质量随铣削参数的变化规律。通过具体实例及与其他几种预测方法的对比表明，在相同样本条件下，其模型构造速度比标准支持向量机方法高1～2个数量级，模型预测误差约为支持向量机方法的40％，预测精度比常规BP模型高1个数量级。因此，基于最小二乘支持向量机方法建模速度快、预测精度高、适合加工表面粗糙度预测。     

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。     

基于LS-SVM的汽轮机叶片高速铣削表面质量预测建模

针对汽轮机叶片高速铣削加工中存在的表面质量不易控制问题,借助最小二乘支持向量机原理,建立了被加工不锈钢叶片表面的粗糙度预测模型。实验结果表明,该模型能方便地预测切削速度、主轴转速、进给量、铣削宽度等铣削参数对铣削加工工件表面粗糙度的影响,并能利用有限的实验数据得出整个工作范围内的表面粗糙度预测值,模型适合于表面粗糙度预测,回归预测精度高。     

盖板件高效铣削表面粗糙度预测与工艺参数优化

为了保证7075铝合金盖板件铣削表面质量和提高铣削加工效率,进行工艺参数优化,通过响应曲面法(RSM)构建铣削加工表面粗糙度预测模型,分析铣削工艺参数对表面粗糙度的影响规律。基于表面粗糙度预测模型建立高效铣削工艺参数优化目标方程,采用改进的粒子群算法(PSO)对目标方程进行优化,运用优化后的工艺参数对某盖板件进行铣削加工。试验结果表明：采用优化后的工艺参数进行盖板件铣削加工可以满足表面粗糙度要求,验证了预测模型的准确性和改进PSO方法的可行性。     

航空铝合金三维端铣表面粗糙度的LS-SVM控制研究

为提高加工工件的表面质量,需要有效控制加工工件表面粗糙度,因此有必要建立精度高、泛化能力强的表面粗糙度预测模型。首先基于具有位错动力学物理基础的Z-A材料本构模型,建立航空铝合金7050材料的三维端面铣削有限元仿真模型,并设计正交试验验证有限元模型的可靠性;其次建立最小二乘支持向量机(LS-SVM)预测模型,以仿真所提供的样本数据为输入,拟合铣削参数与表面粗糙度的复杂非线性关系,实现了表面粗糙度的预测,结果表明LS-SVM模型预测的相对误差不超过6%;最后基于LS-SVM表面粗糙度预测模型得出各铣削参数对表面粗糙度的影响,为生产实际提供指导。     

基于人工神经网络的高速加工表面粗糙度预测模型

应用人工神经网络方法建立了高速铣削淬硬模具钢的表面粗糙度预测模型。该模型的预测结果与实测数据吻合良好,可为高速加工切削参数的选择和表面质量控制提供依据。     

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。     

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。     

磨料水射流铣削钛合金深度与表面粗糙度研究

磨料水射流铣削技术柔性大、工艺参数复杂,其加工性能难以有效控制。针对这一问题,本文首先通过响应曲面法研究了磨料水射流铣削钛合金时典型工艺参数对铣削深度和表面粗糙度的影响,并采用传统回归方式建立了经验预测模型;其次在结合磨粒磨损理论、高斯轮廓模型和表面成形分析的基础上,进一步建立了铣削深度和表面粗糙度的半经验预测模型;然后利用实验数据进行了参数标定;最后通过实验验证和对比了两种模型。结果表明,两种预测模型的平均误差均小于15%,相比经验模型,半经验模型既可以解释参数影响和铣削机理,又可以保证预测的准确率和稳定性,对于控制铣削深度和表面质量具有重要价值。     

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.     

Surface roughness modeling in Laser-assisted End Milling of Inconel 718

Inconel 718 is a difficult-to-machine material while products of this material require good surface finish. Therefore, it is essential for the evaluation and prediction of surface roughness of machined Inconel 718 workpiece to be developed. An analytical model for the prediction of surface roughness under laser-assisted end milling of Inconel 718 is proposed based on kinematics of tool movement and elastic response of workpiece. The actual tool trajectory is first predicted with the consideration of overall tool movement, elastic deformation of tool, and the tool tip profile. The tool movements include the translation in feed direction and the rotation along its axis. The elastic deformation is calculated based on the previously established milling force prediction model. The tool tip profile is predicted based on the tool tip radius and angle. The machined surface profile is simulated based on the tool trajectory with elastic recovery, which is considered through the comparison between the minimum thickness and actual cutting thickness. Experiments are conducted in both conventional and laser-assisted milling under seven different sets of cutting parameters. Through the comparison between the analytical predictions and experimental measurements, the proposed model has high accuracy with the maximum error less than 27%, which is more accurate for lower feed rate with error less than 3%. The proposed analytical model is valuable for providing a fast, credible, and physics-based method for the prediction of surface roughness in milling process.     

切削加工表面粗糙度的多维多规则云预测方法

针对目前常用切削加工表面粗糙度预测方法存在预测精度不高、泛化能力不强的问题,提出一种融合模糊和随机性的粗糙度云预测新方法。在分析大量试验数据的基础上给出了多维预测云的数字特征和数学模型,设计粗糙度预测的多维多规则定性推理发生器,将各切削用量作为前件云输入,分析其对粗糙度后件云的影响关系,并通过对推理规则的多元组合,实现不同加工工艺规范下对工件表面质量的精准预测,揭示加工表面质量随切削参数变化的规律。试验结果表明,在相同样本条件下所提方法的平均相对预测误差低于4.78%,求解速度和预测精度方面均有所提高,且预测范围更加广泛。     

Modelling of the In uence of Tool Runout on Surface Generation in Micro Milling

Micro milling is a flexible and economical method to fabricate micro components with three-dimensional geometry features over a wide range of engineering materials. But the surface roughness and micro topography always limit the performance of the machined micro components. This paper presents a surface generation simulation in micro end milling considering both axial and radial tool runout. Firstly, a surface generation model is established based on the geometry of micro milling cutter. Secondly, the influence of the runout in axial and radial directions on the surface generation are investigated and the surface roughness prediction is realized. It is found that the axial runout has a significant influence on the surface topography generation. Furthermore, the influence of axial runout on the surface micro topography was studied quantitatively, and a critical axial runout is given for variable feed per tooth to generate specific surface topography. Finally, the proposed model is validated by means of experiments and a good correlation is obtained. The proposed surface generation model o ers a basis for designing and optimizing surface parameters of functional machined surfaces.     

Application of multiple regression and adaptive neuro fuzzy inference system for the prediction of surface roughness

A manufacturing system is oriented towards higher production rate, quality, and reduced cost and time to make a product. Surface roughness is an index for determining the quality of machined products and is influenced by the cutting parameters. Surface roughness prediction in machining is being attempted with many methodologies, yet there is a need to develop robust, autonomous and accurate predictive system. This work proposes the application of two different hybrid intelligent techniques, adaptive neuro fuzzy inference system (ANFIS) and radial basis function neural network- fuzzy logic (RBFNN-FL) for the prediction of surface roughness in end milling. An experimental data set is obtained with speed, feed, depth of cut and vibration as input parameters and surface roughness as output parameter. The input-output data set is used for training and validation of the proposed techniques. After validation they are forwarded for the prediction of surface roughness. Both the hybrid techniques are found to be superior over their respective individual intelligent techniques in terms of computational speed and accuracy for the prediction of surface roughness.     

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(...     

Avoiding neural network fine tuning by using ensemble learning: application to ball-end milling operations

Surface roughness plays a key role in the performance of machined components??specially dies and moulds??manufactured for the aerospace and automotive industries, among others. However, roughness can only be measured off-line after the part has been machined, when cutting conditions may no longer be adjusted to surface roughness requirements. A reliable surface roughness prediction application is presented in this paper. It is based on ensemble learning for vertical high-speed milling operations with ball-end mills for finishing operations on quenched steel 1.2344 (AISI H13) that are widely used in the manufacture of moulds and dies. The new approach was validated with an experimental dataset that includes geometrical tool factors, cutting conditions, dynamic factors and lubricant type. An intensive comparison with an artificial neural network approach for the same dataset is included, to reveal the improvements of the new technique over other well-established ones for this industrial problem. This comparison shows that ensemble learning can by-pass the time-consuming task of tuning neural network parameters and can also improve prediction model accuracy, both of which are features that could lead to greater use of these kinds of prediction models in real workshops. Finally, a methodology, based on this new approach, is presented, in order to illustrate how the prediction model can be used in workshops to optimize cutting conditions in terms of their surface quality and productivity.     

A new approach to analysing machined surfaces by ball-end milling, part I:

Since productivity and product quality are always regarded as important issues in manufacturing technologies, a reliable method for predicting machining errors is essential to meeting these two conflicting requirements. However, the conventional roughness model is not suitable for the evaluation of machining errors for highly efficient machining conditions. Therefore, a different approach is needed for a more accurate calculation of machining errors. This study deals with the geometrical surface roughness in ball-end milling. In this work, a new method, called the ridge method, is proposed for the prediction of the machined surface roughness in the ball-end milling process. In Part I of this two-part paper, a theoretical analysis for the prediction of the characteristic lines of the cut remainder are generated from a surface generation mechanism of a ball-end milling process, and three types of ridges are defined. The trochoidal trajectories of cutting edges are considered in the evaluation of the cut remainder. The predicted results are compared with the results of a conventional roughness model.