数控铣削加工工艺参数优化方法综述

介绍了铣削力、铣削用量等数控铣削加工工艺参数,分析了材料去除率、表面粗糙度、能耗、铣刀颤振等工艺指标,并给出了数控铣削加工工艺参数的优化目标、优化方法、现有试验研究,以及近似模型。所做研究可以为数控铣削加工工艺参数的选择和优化提供理论参考。     

基于GA-BP与NSGA-Ⅱ的数控铣削参数优化研究

高效低碳制造是可持续发展的关键,而数控铣削作为常用的金属表面加工方法存在刀具寿命短、碳排放量高的问题。提出基于NSGA-Ⅱ的GA-BP多目标优化方法,通过分析不同加工参数条件下的数控铣削刀具寿命及碳排放数据集,建立GA-BP神经网络刀具寿命及碳排放预测模型。基于NSGA-Ⅱ算法建立以刀具寿命、碳排放量为目标的主体优化模型,调用构建的GA-BP神经网络模型作为目标函数进行优化求解,得到Pareto最优解集。对Pareto最优解集进行TOPSIS最优解决策,得到综合优化刀具寿命与碳排放量的加工参数组合。优化结果表明：该方法既可以对数控铣削刀具寿命及碳排放量进行准确预测,还可以对两者进行有效优化,对数控铣削参数优化具有一定的理论指导意义。     

面向能耗的2.5D型腔数控铣削加工刀具组合优化选择

为降低数控机床的能量消耗,深入研究了2.5D型腔数控铣削加工过程中的刀具组合优选问题。系统地分析了刀具组合与2.5D型腔数控铣削加工过程能耗的映射关系,揭示了刀具直径和刀具可行域对加工能耗的影响,并构建了多刀具数控铣削加工能耗函数。建立了以可行刀具为优化变量、以能耗和成本为优化目标的数控铣削加工刀具组合多目标优化模型,提出一种基于有向图和Dijkstra算法的刀具组合优化求解方法。以某2.5D型腔数控铣削加工刀具组合优选为例开展实验,验证了所提模型与方法的有效性和实用性。     

基于包络法的高效曲面数控铣削加工技术

基于包络法高效曲面加工技术以及CATIA数控加工编程技术的研究,在CATIA V5平台上开发出优质高效的包络法曲面数控铣削加工编程工具软件。通过系统的切削加工试验优化曲面包络法数控铣削加工软件、刀具结构参数以及切削加工参数,以实现曲面的优质高效数控铣削加工,尤其是大型的小曲率曲面结构件铣削加工。     

铝合金航空薄壁件数控铣削加工变形预测与分析

在分析铣削加工中材料的变形机理、薄壁件数控铣削加工变形的基本特点的基础上,建立了集成于专业CAD/CAM/FEA系统的薄壁件数控铣削加工变形预测框架,以典型铝合金航空薄壁件数控铣削加工变形进行了预测与试验验证.     

面向能量效率的数控铣削加工参数多目标优化模型

切削参数是数控加工工艺中的重要组成,切削参数的合理选择能够显著影响到机床能量效率。为提高机床能量效率,分析数控铣削加工的能耗构成特性和加工时段能耗特性,建立数控铣削加工能耗模型,并通过非线性回归拟合获取能耗模型的相关系数,建立了以最高能量效率和最小加工时间为目标的多目标优化模型,采用连续禁忌算法对模型进行优化求解。通过算法优化结果与试验结果进行对比分析,验证了能效模型的有效性。     

基于田口法和响应面法的数控铣削工艺参数能效优化方法

为研究机床能量效率与工艺参数耦合的复杂机理,提出一种基于田口法和响应面法的数控铣削工艺参数能效优化方法。分析加工过程中的能耗特性,建立了数控铣削加工能量效率函数。基于田口法规划优化实验,并基于响应面法建立工艺参数与比能和加工时间的回归方程,构建了以铣削加工工艺参数为变量、以高能效和高效率为优化目标的多目标优化模型。使用多目标粒子群优化算法对该模型进行求解,并通过实验数据和算法优化结果分析了加工过程中工艺参数与比能及加工时间的关联关系。     

薄壁零件数控铣削加工工艺技术探讨

本文主要以薄壁零件数控铣削加工工艺技术为重点进行阐述,首先分析了薄壁零件数控铣削加工出现变形情况的关联因素;其次,从结合工件力学特征,调整工艺流程、整合走刀与加工顺序,排除切削的耦合作用、规范制定切削指数,稳定完成数控铣削加工几个方面深入说明并探讨薄壁零件数控铣削加工工艺技术的有效应用,继而强化薄壁零件数控铣削加工操作...     

基于加工质量预测与分析的数控铣削过程仿真系统研究与开发

本文以曲面产品的数控铣削为研究对象，介绍了用于数控铣削加工预测与分析的仿真系统，并着重就系统的结构和有关具体实现问题进行了讨论。     

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。     

基于BP神经网络的铣齿切削功率计算方法研究

通过对物理样机的实验测量,借助MATLAB神经网络工具箱,将BP神经网络的理论和算法应用于铣齿功率建模的研究中。通过铣齿切削试验获取训练样本,对齿轮铣削功率的网络模型进行训练和测试。结果表明,人工神经网络能较准确地预测铣齿功率的大小,解决了传统经验公式误差较大的问题,为铣齿机床的传动设计提供了依据。     

采用改进灰色神经网络的铣床热误差补偿研究

数控铣床在铣削零件过程中,主轴会受到温度变化影响而发生热变形,导致铣削零件误差较大,从而降低产品精度。对此,采用一阶线性微分方程推导GM(1,1)模型,创建灰色预测模型。将神经网络模型与灰色预测模型进行组合,建立灰色神经网络预测模型。引用粒子群算法,在粒子群算法中增加变异操作和修改惯性权重系数,给出改进粒子群算法优化灰色神经网络预测模型的具体操作步骤。采用实验测试铣床铣削过程中所产生的热误差,并与预测模型进行比较。结果显示:在铣床主轴X、Y、Z轴三个方向上,灰色神经网络预测模型对铣床主轴补偿后,得到的残差较大;而改进灰色神经网络预测模型对铣床主轴补偿后,得到的残差相对较小。采用改进粒子群算法优化灰色神经网络预测模型,能够提高铣床主轴铣削精度。     

基于多源异构数据的数控铣削表面粗糙度预测方法

针对传统数控铣削表面粗糙度预测模型泛化性差、精度较低等问题,提出了一种基于多源异构数据的数控铣削表面粗糙度预测方法.获取变工艺条件下数控铣削的工艺参数、刀具直径及工件材料等静态数据和振动信号、力信号及功率信号等动态数据;采用粒子群优化算法(PSO)优化卷积神经网络(CNN)的网络结构参数得到PSO-CNN;运用PSO-...     

基于BP神经网络的球头铣刀铣削力建模与仿真

将BP神经网络的理论和算法应用于球头刀具铣削力建模的研究中.采用LM算法建立了铣削力预测的神经网络模型,模型中考虑了影响铣削力的加工参数,选取铣削力试验数据对神经网络模型进行训练,用训练好的神经网络模型对铣削力进行仿真.仿真结果表明,用BP神经网络方法建立的铣削力模型能够对铣削力进行准确的预测.     

Application of neural network technique to high energy milling process for synthesizing ZnO nanopowders

An artificial-neural-network (ANN) model was developed to estimate the crystalline size of ZnO nanopowder as a function on the milling parameters such as milling times and balls to powder ratio. This nanopowder was synthesized by high energy mechanical milling and the required data for training were collected from the experimental results. The synthesized ZnO nanoparticles are characterized by X-ray diffraction (XRD) and scanning electron microcopy (SEM). It was found that artificial neural network was very effective providing a perfect agreement between the outcomes of ANN modeling and experimental results with an error by far better than multiple linear regressions. An optimization model and this experimental validation of the ball milling process for producing the nanopowder ZnO are carried out.     

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.     

Feedrate optimization based on hybrid forward-reverse mappings of artificial neural networks for five-axis milling

With recent advances in five-axis milling technology, feedrate optimization methods have shown significant effects in regard to enhancing milling productivity, especially when machining complex surface parts. The existing study is aimed at calculating the optimal feedrate values through modeling milling processes. However, due to the complexity of five-axis milling processes, optimization efficiency is the bottleneck of applying them in practice. This paper proposes a novel milling process optimization method based on hybrid forward-reverse mappings (HFRM) of artificial neural networks. The feedrate values are directly used as the outputs of network mappings. Three kinds of artificial neural networks are compared to determine the one with the highest accuracy and the best training efficiency. The study shows that with the collected datasets, the trained Levenberg-Marquardt back-propagation network (LMBPN) could predict feedrate values more precisely than other alternatives. Compared with previous methods, this HFRM-based optimization method is more adept in the area of parameter adjustment because as it has the advantages of high precision and much less calculation time. Combining other multiple milling constraints, an optimization system is developed for five-axis milling processes. The optimized results could be directly used to modify a cutter location (CL) file. A typical milling case was provided to verify the optimization performance of this method, which was found to be effective and reliable.     

PCD刀具高速铣削GH4169合金的刀具磨损规律研究

为研究PCD刀具高速铣削GH4169合金时刀具的磨损规律,采用单因素试验法分别对不同铣削参数下后刀面磨损程度随切削路程的变化进行对比。结果显示主轴转速对高速铣削GH4169合金时刀具磨损的影响不大,采用顺铣、切削液冷却的方式,并适当降低每齿进给量有助于减小刀具磨损。使用BP神经网络对试验数据进行训练,建立了刀具磨损预测的模型,预测结果与实际结果误差在5%以内。