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。     

基于自适应遗传算法的数控铣削过程参数优化仿真

基于自适应遗传算法思想建立了数控铣削用量多目标决策智能优化的数学模型，并运用遗传优化方法对铣削加工实例进行优化，实例验证表明，具有自适应交叉率和变异率的遗传算法在收敛速度和获得全局最优解的概率两个方面都有很大提高，并在数控加工动态仿真系统上验证了铣削用量多目标智能优化的数学模型的正确性和实用性。     

瞬时铣削力建模与铣削力系数的粒子群法辨识

为获得精确的瞬时铣削力模型,对微元铣削力进行分析,建立了微元铣削力模型。依据立铣加工的特点,提出了微元铣削刃参与铣削的判断方法,给出了具体的计算公式。在此基础上,建立了包含剪切效应和犁入效应的瞬时铣削力模型。利用粒子群算法收敛速度快的优点,提出了基于粒子群的单位铣削力系数辨识方法,给出了算法的实现步骤。铣削试验结果表明,该方法能够精确辨识出单位铣削力系数,利用所提出的瞬时铣削力模型获得的铣削力预测值与铣削力实测值的大小和变化趋势基本一致。     

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。     

螺纹的数控铣削加工

介绍了三角形螺纹的加工方法，针对较难实现装夹的工件，提出了用加工中心铣削螺纹的方法。分析了螺纹铣削加工工艺特点，以及螺纹铣削刀具的选用，并给出了在两种常用的数控系统（FANUC系统和SINUMERIK系统）的加工中心上，用内螺纹车刀铣削内螺纹的数控加工程序。     

NOMEX蜂窝芯高速铣削加工工艺的优化

为了解决NOMEX蜂窝芯高速铣削加工过程中固持可靠性差和加工效率低以及加工表面质量差的问题，提出一种新的基于强磁场和摩擦学原理的蜂窝芯高速加工固持方法。该方法利用灌入蜂窝孔中的铁粉自重以及强磁场平台对铁粉产生的吸引力，在铁粉与蜂窝孔侧壁之间以及蜂窝、铁粉与固持平台之间产生摩擦力，实现对蜂窝底部的全约束和固定。建立了NOMEX蜂窝芯高速铣削加工的铣削力模型，进行了以铣削力最小为目标的铣削参数的优化。通过铣削参数的优化，保证了加工过程中蜂窝的固持稳定性，同时保证了蜂窝加工效率和加工质量。     

基于切削稳定性与表面质量约束的 铣削工艺参数优化研究

铣削工艺系统的动态特性随着刀具夹持长度改变而变化,影响关联的铣削稳定性与加工表面质量,进而导致铣削加工 的工艺规划具有不确定性。 针对此问题,本文综合刀具悬伸量与传统铣削用量作为输入,分别建立极限切削深度与表面粗糙度 的反向传播神经网络模型(BPNN),并进一步以其表达铣削稳定性与加工质量约束,构建以刀具悬伸量和粗/ 精加工阶段铣削 用量为优化变量,以粗/ 精铣总加工时间为目标的铣削工艺参数优化模型,采用麻雀搜索算法( SSA)对模型进行寻优求解。 以 某型数控机床的夹具型腔铣削加工为例,采用刀具悬伸量与各阶段铣削用量的优化配置进行加工实验,总切削时间 12. 577 min 与表面粗糙度值 3. 01 μm 验证了优化模型的可行性和有效性。     

淬硬钢高速铣削用量确定方法的研究

采用高速铣削技术加工淬硬钢可以大大改善材料去除率和表面粗糙度,并提高淬硬钢加工效率,降低加工成本.为获得必要的加工精度、表面质量及延长刀具寿命,铣削淬硬钢材料除精心选择刀具材料和几何参数外,必须优化铣削用量.基于淬硬钢高速铣削参数对铣削力影响的理论分析,得出高速铣削淬硬钢宜采用高转速、低进给、小切深的方式进行铣削加工的结论.比较分析了确定淬硬钢高速铣削用量的常用3种方法.     

型腔铣削单纯区及其判别方法

型腔类零件的几何形状通常比较复杂,在加工中心或数控铣床上加工时往往需要通过合理拆分加工域来实现刀具序列的优选,进而提高加工效益,从而需要判别加工域是否可分这一基础问题.为此,提出一种用于判别加工域是否可分的概念及方法--型腔铣削单纯区及其判别方法.提出铣削单纯区的概念,给出其定义,指出铣削单纯区是一类当且仅当使用一种刀具就可实现最优加工的特殊区域,该区域具有不可再分性、完全覆盖性和效益最优性等三大特点.给出并论证铣削单纯区的判别条件,从加工成本的角度建立铣削单纯区的判别模型,并在此基础上设计铣削单纯区的判别算法.对某文献中的算例型腔进行铣削单纯区判别,从而证明提出的概念及方法的理论价值和实用价值.     

一种新的螺旋刃球头铣刀铣削力模型

为提高铣削加工的安全性和生产效率，有必要在加工实际进行之前准确地预测切削过程的物理信息，如铣削力、刀具振动等。给出了球头铣刀丸线几何模型，采用理论削力分析与实验--系数识别相结合的方法建立了新的螺旋刃球头铣刀的铣削力模型。对不同切削条件下的铣削力进行了仿真，与实验测量数据吻合良好，证明离线仿真可以对铣削力做出较准确的预测。     

An intelligent process planning system for prismatic parts using STEP features

This paper presents an intelligent process planning system using STEP features (ST-FeatCAPP) for prismatic parts. The system maps a STEP AP224 XML data file, without using a complex feature recognition process, and produces the corresponding machining operations to generate the process plan and corresponding STEP-NC in XML format. It carries out several stages of process planning such as operations selection, tool selection, machining parameters determination, machine tools selection and setup planning. A hybrid approach of most recent techniques (neural networks, fuzzy logic and rule-based) of artificial intelligence is used as the inference engine of the developed system. An object-oriented approach is used in the definition and implementation of the system. An example part is tested and the corresponding process plan is presented to demonstrate and verify the proposed CAPP system. The paper thus suggests a new feature-based intelligent CAPP system for avoiding complex feature recognition and knowledge acquisition problems.     

数控铣削加工参数的多参量智能优化

提出了一种通用和智能的确定数控铣床进给速度的办法。首先简化切削力模型,并给出一个加工参数数据库的例子。建立一种基于径向和轴向切削深度和进给速度为输入的模糊系统来预测切削力。然后确定零件的几何特征,计算几何特征的接触角度,并寻找它们最佳进给率。最后,给出应用此方法的一个切削例子,并对结果在CATIA CAD／CAM系统中进行了模拟以显示次方法的优点。     

Application of fuzzy and rough sets theory in the optimization of machining parameters for mold milling operations

NC milling operation has become one of the main means of mold manufacturing in recent years, and the determination of milling conditions is important to assure the quality and minimize the costs of molds. This paper first constructs an optimization model of machining parameters for mold milling operations, focusing on minimizing the production costs. Then, based on the properties of the machining parameters database, it also proposes an extended model of fuzzy and rough sets theory for incomplete information systems, including incomplete continuous attribute values, and applies the model to rules learning from the machining database. Thus, by rule reasoning, the feasible solution space of optimization model can be easily established. At last, an example is presented to detail the optimization of machining parameters in the case of the cavity milling of an injection plastic mold.     

机床自适应控制的新进展

提出一种可自学习、自组织,兼具约束式和是佳式的特征的铣削适应控制系统。系统实现以软件为主,综合使用模糊数学、神经元网络、专家系统等多种共工智能工具建模。建模方法具有一定的通用性,同时也避免了研究切削规律、精确度建模困难。实验证明,该系统可在刀具耐用度不减的条件下提高生产率４０％以上,并可提高加工精度。     

基于Petri网的加工系统递阶智能控制

运用模糊推理与Petri网方法对加工过程的智能控制进行建模,在建模中采用了递阶智能控制的原理,将其分为组织级、协调级和执行级三个层次。组织级采用模糊推理的方法,并用标识模糊Petri网为其推理过程进行建模;协调级设计中,以一台加工中心、一个机械手组成的加工系统为例,运用模糊Petri网为其协调级进行了合理规划。     

基于模糊群决策的智能仪器故障诊断专家系统

本文介绍了采用专家系统解决智能仪器故障诊断的方法.针对智能仪器的故障特点,采用规则推理和事例推理相结合的混合推理技术.在规则推理中,提出了分层搜索的诊断思路,并且在不同搜索层次中,各规则优先权的确定采用基于模糊多属性群决策的方法.针对这种推理方法,采用数据库技术实现了知识库的设计,并用VS.net软件开发出了一套高效率智能仪器故障诊断专家系统.该系统在实际使用中表现出比第一个版本更高的诊断效率.     

面向加工特征的刀具和切削参数计算机辅助选择系统

切削刀具制造商面临围绕大量工件材料和加工特征为客户提供合理刀具和切削参数的现状,切削工艺规划的核心步骤也是刀具和切削参数的确定。确定刀具和切削参数一般多从零件材料角度出发,可能导致工件与刀具不匹配。文中提出面向加工特征的刀具和切削参数计算机辅助选择系统的开发。系统包括车削特征、铣削特征、钻削和镗削加工特征,系统利用特征图形作为用户交互式接口,采用关系数据库结合数据驱动和规则推理逻辑来选择刀具和切削参数,利用数学模型计算过程参数包括单工步加工工时、切削功率、最大粗糙度等,并辅助制定工序。以车刀和车削参数选择为例,介绍该系统的实现方法。该系统可以辅助设计师及工艺人员选择合理的刀具和切削参数。     

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.     

Modeling and Analytical Solution of Chatter Stability for T-slot Milling

T-slot milling is one of the most common milling processes in industry. Despite recent advances in machining technology, productivity of T-slot milling is usually limited due to the process limitations such as high cutting forces and stability. If cutting conditions are not selected properly the process may result in the poor surface finish of the workpiece and the potential damage to the machine tool. Currently, the predication of chatter stability and determination of optimal cutting conditions based on the modeling of T-slot milling process is an effective way to improve the material removal rate(MRR) of a T-slot milling operation. Based on the geometrical model of the T-slot cutter, the dynamic cutting force model was presented in which the average directional cutting force coefficients were obtained by means of numerical approach, and leads to an analytical determination of stability lobes diagram(SLD) on the axial depth of cut. A new kind of SLD on the radial depth of cut was also created to satisfy the special requirement of T-slot milling. Thereafter, a dynamic simulation model of T-slot milling was implemented using Matlab software. In order to verify the effectiveness of the approach, the transfer functions of a typical cutting system in a vertical CNC machining center were measured in both feed and normal directions by an instrumented hammer and accelerators. Dynamic simulations were conducted to obtain the predicated SLD under specified cutting conditions with both the proposed model and CutPro?. Meanwhile, a set of cutting trials were conducted to reveal whether the cutting process under specified cutting conditions is stable or not. Both the simulation comparison and experimental verification demonstrated that the satisfactory coincidence between the simulated, the predicted and the experimental results. The chatter-free T-slot milling with higher MRR can be achieved under the cutting conditions determined according to the SLD simulation.     

A grey-fuzzy modeling for evaluating surface roughness and material removal rate of coated end milling insert

The prime factor for selecting equipment is its performance capability and reliability without compromising on quality. Materials for aerospace application such as aluminum and its alloys have limited applications because of their complications in machining, effectively and economically. There is no further development in raising the effectiveness above the optimal level in cutting tool materials. The surface roughness influences the determination of the quality of the product. The present study focuses on finding optimal end milling process parameters by considering multiple performance characteristics using grey fuzzy approach. In this work, Aluminum Alloy 6082T6 (AA6082T6) is used as workpiece material which was end milled using Aluminum Chromo Nitride (AP3) coated milling insert. Three process performance parameters namely Centre Line Average Roughness (Ra), Root Mean Square Roughness (Rq) and Material Removal Rate (MRR) were optimized. The grey output is fuzzified into five membership functions and also with twenty-seven rules. Grey Fuzzy Reasoning Grade (GFRG) is developed and the optimal values were found out from the Grey relational grade. The result of the Analysis of Variances (ANOVA) shows that the maximum contribution in the depth cut is (31.785%) followed by feed (28.212%). Moreover, Adaptive Neuro-Fuzzy Inference System (ANFIS) model has been developed with the help of the same input values compared to the performance of the fuzzy logic model. With the help of detailed analysis, it has been found that the fuzzy logic based model gives more reasonable results when compared to ANFIS model.