基于标准粒子群算法的GH4169高速铣削表面粗糙度研究

采用单因素和正交实验法,研究了整体硬质合金刀具高速铣削GH4169高温合金时切削参数对表面粗糙度的影响规律,结果表明,每齿进给量对表面粗糙度的影响十分显著,铣削速度和铣削深度对表面粗糙度的影响较小;基于标准粒子群算法建立了表面粗糙度与切削参数之间的经验公式,并对经验公式进行了实验验证,结果显示,基于标准粒子群算法建立的经验公式能有效预测GH4169高温合金高速铣削过程中的表面粗糙度,为铣削参数优化、铣削表面质量控制提供了依据。     

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。     

铣削20CrMnTi钢已加工表面粗糙度的试验研究

采用正交试验法研究CBN直柄平底立铣刀高速铣削20CrMnTi淬硬钢时切削参数对已加工表面粗糙度的影响。通过极差分析方法研究了切削参数对表面粗糙度的影响程度,通过单因素试验法得到了切削参数对表面粗糙度的影响规律,建立了基于指数函数的切削参数与表面粗糙度的关系模型。利用预测模型得出的表面粗糙度与试验的结果进行误差分析,说明所建立的模型能比较准确地对表面粗糙度进行预测。试验结果表明：各因素的影响程度从大到小依次为铣削深度、每齿进给量和切削速度,表面粗糙度随每齿进给量和铣削深度的增大而增大,随切削速度的增大而减小。     

分形维数与铣削参数的模型关联及验证

基于工程粗糙表面的微观形貌具有统计自相似分形特征,将分形几何学运用于金属材料表面形貌研究。粗糙表面的分形参数与加工条件密切相关。铣削加工过程中,切削参数会影响表面分形维数和表面粗糙度,考察了分形维数和传统表面粗糙度参数之间的关系,分别建立铣削参数与表面分形维数和表面粗糙度之间关系模型,并采用实验进行验证。实验结果表明,铣削加工表面具有分形特征;铣削表面分形维数D基本不随切削速度增加而变化,但表面粗糙度Ra会随切削速度的增加而减小;表面粗糙度与加工进给量成正相关,分形维数先增大后减小,并存在临界点;分形维数D与表面粗糙度Ra呈幂指数关系;所建立模型合理。相关研究结果可以为提高工程表面的使用性能及降低成本提供参考。     

基于响应曲面法的GH4169铣削参数优化及实验研究

机械加工的表面质量会极大地影响零部件的使用寿命。该文以降低镍基高温合金GH4169加工过程中的表面粗糙度为目标，对镍基高温合金GH4169的铣削参数进行优化。基于响应曲面法分析了铣削参数(转速、进给量、切削深度)对表面粗糙度的影响规律，建立了铣削参数与表面粗糙度之间的二次多项式模型并进行了验证，确定了降低表面粗糙度的最优工艺参数组合。研究结果表明，当A=928.34 r/min,B=243.35 mm/min,C=0.2 mm时，粗糙度可达到0.143μm。采用最优参数组合进行加工实验，并对铣削加工后的表面粗糙度进行测量，得出粗糙度实测值与模型预测值的相对误差为0.2%,可见所建立的模型是准确的。可满足某些航空航天高精度零部件表面质量特性，因此该模型对GH4169铣削加工具有指导意义。     

工件初始表面几何状态对铣削力及表面形貌的影响

为研究多工步铣削加工过程工件初始表面几何状态对后续工步铣削力及表面形貌的影响,采用表面算术平均高度和表面最大高度对表面形貌进行定量表征,建立了考虑工件初始表面几何状态的切削层模型。设计并利用两工步铣削试验对切削层模型进行验证。试验表明,工件初始表面几何状态与第二工步铣削的铣削力和表面粗糙度参数呈正相关关系,切削层模型能为多工步铣削过程研究提供理论基础。     

低速铣削中表面粗糙度影响因素及切削参数优化研究

通过铝合金薄壁工件切削试验,对铣削加工中表面粗糙度的影响机理进行分析,建立了铝合金薄壁工件表面粗糙度预测模型,采用多元回归正交分析法获得了表面粗糙度的经验公式。结合正交试验的极差分析,获取不同的加工参数对表面粗糙度的影响显著性。并基于表面粗糙度,以材料去除率为优化目标,对切削参数进行优化。研究结果表明,切削参数对表面粗糙度的影响显著性为:每齿进给切削宽度切削速度;较优化的切削参数为:切削速度201 m/min,每齿进给0.19 mm/齿,径向切宽11 mm。为铝合金工件的铣削加工提供理论方法和试验依据。     

针对奥贝球铁高速铣削力的表面粗糙度建模的实验研究

奥贝球铁(ADI)作为一种综合性能优异的难加工工程材料,其高速铣削下的铣削力对工件表面粗糙度Ra有重要影响。为研究其内在关系,基于多因素正交回归实验建立铣削力和表面粗糙度模型的理论,结合奥贝球铁的材料力学性能,采用以主轴转速、每齿进给量、切削深度、切削宽度等为多因素,高速铣削力和表面粗糙度为因变量的正交试验方法,并通过最小二乘法建立关于F_x、F_y、F_z这3个铣削分力的Ra模型;然后分别检验了回归模型和回归系数的显著性,分析了所建模型的可信度及F_x、F_y、F_z分别对ADI表面粗糙度的影响程度;最后设计了相关实验对所建模型进行验证。实验结果表明:高速铣削分力对表面粗糙度影响程度次序:切向力轴向力径向力;模型验证实验说明了所建模型的可信性和正确性,从而可为ADI材料高速铣削加工提供了重要的理论支持和技术指导。     

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。     

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。     

涂层刀具高速铣削模具钢SKD11的表面粗糙度模型预测

为有效控制和预测高硬度模具钢加工的表面质量和加工效率,通过设计正交切削试验,研究了在不同切削参数组合(主轴转速、进给速度、轴向切削深度和径向切削深度)及冷却润滑方式条件下、Ti Si N涂层刀具对模具钢SKD11(62HRC)的高速铣削。应用BP神经网络原理建立表面粗糙度预测模型,并进行试验验证其准确性。研究表明,在不同加工条件下,基于BP神经网络模型建立的涂层刀具铣削模具钢SKD11表面粗糙度模型有较好的预测精度,其预测误差在3.45%-6.25%之间,对于模具制造企业选择加工工艺参数、控制加工质量和降低加工成本有重要意义。     

高速铣削超高强度钢的切削力及表面粗糙度研究

选用涂层硬质合金刀具对300M超高强度钢进行高速铣削试验,通过单因素试验和多因素正交试验法,得出铣削参数(主轴转速、每齿进给量、铣削深度)对切削力及表面粗糙度的影响规律及主次关系。对正交试验结果做最小二乘法分析,建立切削力及表面粗糙度与铣削参数之间的经验模型;对经验模型的回归方程及系数做显著性检验,并对其进行参数优化,得出铣削参数的最优组合。结果表明:主轴转速和铣削深度对切削力的作用较大,而每齿进给量对其影响相对较弱;每齿进给量对表面粗糙度作用最强,铣削深度次之,主轴转速对其作用最弱。     

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。     

Application of GONNS to predict constrained optimum surface roughness in face milling of high-silicon austenitic stainless steel

Surface roughness is a technical requirement for machined products and one of the main product quality specifications. In the present research, a genetically optimized neural network system (GONNS) is proposed for prediction of constrained optimal cutting conditions in face milling of a high-silicon austenitic stainless steel (UNS J93900) in order to minimize surface roughness. In order to attain minimum operation numbers and decrease the cost of machining, an experimental scheme was arranged by using Taguchi method. The considered parameters were cutting speed, feed, depth of cut, and engagement. Cutting force components and surface roughness were measured, and then analysis of variance is performed. The results show that the feed is the dominant factor affecting the surface roughness. Backpropagation artificial neural network was utilized to create predictive models of surface roughness and cutting forces exploiting the experimental data, and the genetic algorithm was employed to find the constrained optimum of surface roughness. Finally, in order to validate the method, an experiment with the obtained optimal cutting condition was carried out, and the results were compared with the predicted value of surface roughness. The corresponding results show the capability of GONNS to predict constrained surface roughness.     

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。     

Regression analysis, support vector machines, and Bayesian neural network approaches to modeling surface roughness in face milling

This study examines the influence of cutting speed, feed, and depth of cut on surface roughness in face milling process. Three different modeling methodologies, namely regression analysis (RA), support vector machines (SVM), and Bayesian neural network (BNN), have been applied to data experimentally determined by means of the design of experiment. The results obtained by the models have been compared. All three models have the relative prediction error below 8%. The best prediction of surface roughness shows BNN model with the average relative prediction error of 6.1%. The research has shown that, when the training dataset is small, both BNN and SVR modeling methodologies are comparable with RA methodology and, furthermore, they can even offer better results. Regarding the influence of the examined cutting parameters on the surface roughness, it has been shown that the feed has the largest affect on it and the depth of cut the least.     

Optimization of milling parameters using artificial neural network and artificial immune system

The present paper is an attempt to predict the effective milling parameters on the final surface roughness of the work-piece made of Ti-6Al-4V using a multi-perceptron artificial neural network. The required data were collected during the experiments conducted on the mentioned material. These parameters include cutting speed, feed per tooth and depth of cut. A relatively newly discovered optimization algorithm entitled, artificial immune system is used to find the best cutting conditions resulting in minimum surface roughness. Finally, the process of validation of the optimum condition is presented.     

正交车铣TC4钛合金表面粗糙度分析

车铣加工技术是近年发展起来的先进切削加工技术之一。本文采用多因素正交试验法,进行了一系列的正交车铣TC4钛合金切削试验,研究了车铣切削用量与表面粗糙度之间的变化规律。通过方差分析确定了各因素对表面粗糙度的影响大小的主次顺序,每齿进给量和偏心量对表面粗糙度的影响较大。采用回归分析原理,建立了表面粗糙度的预测模型,根据统计检验结果表明,已加工表面粗糙度预测模型呈高度显著检验状态,具有很高的可信度。     

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.     

模具钢铣削中刀具磨损的试验研究

以面铣刀刀片磨损为研究对象,结合类神经网络系统建构高速数控铣削加工的预测模型。以加工参数为模型输入条件,刀腹磨耗为输出条件。采用多因素试验方法,选择切削速度、进给速度、切削深度三个试验参数,利用直交表式的试验计划法设计试验点。依照试验点铣削工件后再测量刀具加工后的刀腹磨耗量,进而求得倒传递网络所需的36组训练范例与11组验证数据。刀腹磨耗预测模式是利用类神经网络中的倒传递网络原理,以田口法求得倒传递网络参数的最优值。试验结果显示,刀腹磨耗随着切削速度、进给速度、切削深度增加而上升。铣削模具钢后,刀具磨耗预测值的平均误差为4.72%,最大误差为11.43%,最小误差为0.31%。整体而言,类神经网络对于铣削加工可进行有效预测。