数控加工参数优化技术研究

为了提高机床的利用率，需要在加工前通过对实际数控加工过程的仿真模拟来获取实际加工的最优化参数。在实际生产过程中，数控机床的加工工序不同，其所需要优化的目标就会有所不同，本文主要针对数控机床加工参数优化问题展开相应的探讨，通过建立数控加工的多目标优化模型进行仿真模拟，并结合遗传算法对每一种加工段进行加工参数优化，通过每一个特征段的优化累加可以获得实际的优化效果。经过实例证实，多元目标函数优化模型的优化效果良好。     

Process parameter optimization during EDM of AISI 316 LN stainless steel by using fuzzy based multi-objective PSO

The present contribution describes an application of a hybrid approach using fuzzy logic and particle swarm optimization (PSO) for optimizing the process parameters in the electric discharge machining (EDM) of AISI 316LN Stainless Steel. In this study, each experimentation was performed under different machining conditions of pulse current, pulse on-time, and pulse off-time. Machining performances such as MRR and EWR were evaluated. A Taguchi L9 orthogonal array was produced to plan the experimentation and the regression method was applied to model the relationship between the input factors and responses. A fuzzy model was employed to provide a fitness function to PSO by unifying the multiple responses. Finally, PSO was used to predict the optimal process parametric settings for the multi-performance optimization of the EDM operation. The experimental results confirm the feasibility of the strategy and are in good agreement with the predicted results over a wide range of machining conditions employed in the process.     

基于数字孪生的铣削参数动态多目标优化策略

为解决机床性能动态变化过程中的铣削参数动态多目标优化问题,提出一种基于数字孪生的铣削参数动态多目标优化策略.首先采用梯度提升回归树算法构建加工参数与加工结果间的非线性映射关系;然后基于动态非支配排序遗传算法进行铣削参数动态寻优;最后在Pareto最优解的基础上,结合层次分析法和理想解相似度顺序偏好法建立决策分析模型并进...     

Development of multi-objective optimization models for electrochemical machining process

Owing to the complexity of electrochemical machining (ECM), it is very difficult to determine optimal cutting parameters for improving cutting performance. Hence, optimization of operating parameters is an important step in machining, particularly for unconventional machining procedures like ECM. A suitable selection of machining parameters for the ECM process relies heavily on the operator’s technologies and experience because of their numerous and diverse range. Machining parameters provided by the machine tool builder cannot meet the operator’s requirements. Since for an arbitrary desired machining time for a particular job, they do not provide the optimal conditions. To solve this task, multiple regression model and ANN model are developed as efficient approaches to determine the optimal machining parameters in ECM. In this paper, current, voltage, flow rate and gap are considered as machining parameters and metal removal rate and surface roughness are the objectives. Then by applying grey relational analysis, we calculate the grey grade for representing multi-objective model. Multiple regression model and ANN model have been developed to map the relationship between process parameters and objectives in terms of grade. The experimental data are divided into training and testing data. The predicted grade is found and then the percentage deviation between the experimental grade and predicted grade is calculated for each model. The average percentage deviations for the training data of the linear regression model, logarithmic transformation model, excluding interaction terms and ANN model, are 12.7, 25.6 and 3.03, respectively. The average percentage deviations for the testing data of the three models are 9.83, 26.8 and 2.67. While examining the average percentage deviations of three models, ANN is having less percentage deviation. So ANN is considered as the best prediction model. Based on the testing results of the artificial neural network, the operating parameters are optimized. Finally, ANOVA is used to identify the significance of multiple regression model and ANN model.     

基于聚类分析法的装夹规划算法研究

为解决计算机辅助工艺规划中的装夹规划问题,在定义加工单元的基础上,建立装夹规划数学模型,并提出了应用凝聚的层次聚类法求解该模型.把加工机床和工艺原理约束转换为加工单元间的距离,依据距离大小对加工单元聚类.根据加工顺序优先关系图,应用启发式算法对加工单元分组,对聚类的加工单元进行组间和组内排序.最后以零件ANC-101为例,验证了所提方法的有效性.     

TC4钛合金电火花小孔加工多目标优化试验研究

为提升电火花加工TC4钛合金的表面加工质量和加工效率,选取紫铜圆柱电极开展TC4钛合金电火花小孔加工试验,采用正交试验法,以电极相对损耗率、表面粗糙度、工件材料去除体积为工艺指标,分析峰值电流、维持电压、放电脉宽对工艺指标的影响重要性。采用RBF(Radial basis function)神经网络对已有试验数据进行训练,建立放电参数与工艺指标之间的数学预测模型。以该预测模型为适应度函数,将遗传算法与Skyline选择算法结合进行多目标优化仿真,得到最佳工艺指标,最后开展多目标优化验证试验。结果表明：当峰值电流为14 A、维持电压39 V/42 V、放电脉宽102μs/108μs时能够取得最优的加工结果,优化值与试验值误差较小。     

可重构加工系统设计的多目标决策分析

密封条加工系统的设计 ,是一个涉及到多个目标的优化过程 ,它受到多种因素的影响。采用定性与定量相结合的统一处理方式 ,建立了难以用数学模型定量描述的多目标、多因素决策系统的数学模型 ,解决了加工质量、效率、加工系统可靠性、可重构性和成本之间具有的矛盾性和不可公度性 ;提出并应用一种实际操作性强的方法 ,确定了各个目标的权值 ,最终应用 TOPSIS法的原理得出系统设计的优选方案。     

Selection of machining datum and allocation of tolerance through tolerance charting technique

Tolerance charting is an effective tool to determine the optimal allocation of working dimensions and working tolerances such that the blueprint dimensions and tolerances can be achieved to accomplish the cost objectives.The selection of machining datum and allocation of tolerances are critical in any machining process planning as they directly affect any setup methods/machine tools selection and machining time.This paper mainly focuses on the selection of optimum machining datums and machining tolerances simultaneously in process planning.A dynamic tolerance charting constraint scheme is developed and implemented in the optimization procedure.An optimization model is formulated for selecting machining datum and tolerances and implemented with an algorithm namely Elitist Non-Dominated Sorting Genetic Algorithm(NSGA-II).The computational results indicate that the proposed methodology is capable and robust in finding the optimal machining datum set and tolerances.     

孔群加工路径规划问题的进化求解

孔群加工路径规划对于提高多孔类零件的加工效率和质量具有重要意义。建立了两个孔群加工路径规划问题的数学模型,分别归纳为单目标和多目标组合优化问题,并引入进化蚁群系统算法和人工免疫算法求解单目标组合优化问题。这两种算法均能有效防止解空间的“组合爆炸”问题,计算复杂度的阶次低于Hopfield神经网络算法,且性能优于Hopfield算法。采用多目标解的快速排序技术分别对进化蚁群系统算法和人工免疫算法加以改进,开发出多目标进化蚁群系统算法和多目标人工免疫算法。分析表明,改进算法不增加原算法的计算复杂度,能直接用于求解多目标组合优化问题而无需事先给出目标权值向量,并能一次运行求得问题的多个Pareto最优解。     

Multi-objective parametric optimization on machining with wire electric discharge machining

The selection of optimum machining conditions, during wire electric discharge machining process, is of great concern in manufacturing industries these days. The increasing quality demands, at higher productivity levels, require the wire electric discharge machining process to be executed more efficiently. Specifically, the material removal rate needs to be maximized while controlling the surface quality. Despite extensive research on wire electric discharge machining process, determining the desirable operating conditions in industrial setting still relies on the skill of the operators and trial-and-error methods. In the present work, an attempt has been made to optimize the machining conditions for maximum material removal rate and maximum surface finish based on multi-objective genetic algorithm. Experiments, based on Taguchi’s parameter design, were carried out to study the effect of various parameters, viz. pulse peak current, pulse-on time, pulse-off time, wire feed, wire tension and flushing pressure, on the material removal rate and surface finish. It has been observed that a combination of factors for optimization of each performance measure is different. So, mathematical models were developed between machining parameters and responses like metal removal rate and surface finish by using nonlinear regression analysis. These mathematical models were then optimized by using multi-objective optimisation technique based on Non-dominated Sorting Genetic Algorithm-II to obtain a Pareto-optimal solution set.     

基于自适应蚁群算法的工艺路线优化

针对计算机辅助工艺设计中最优方案选择,提出一种以制造资源更换率最低为目标的自适应蚁群优化方法(Adaptive ant colony algorithm,AACA)。通过分析零件特征,根据精度要求对制造特征进行分解,提出加工元概念。加工元被定义为特定的制造特征、加工阶段、加工方法、制造资源、装夹位置的集合,工艺路线的确定被转换为对加工元的优化顺序安排问题。以制造特征之间的几何位置约束,各加工阶段的先后顺序约束为基本元素,构造加工元优选约束矩阵,给出基于优选约束矩阵的加工元优选原则。在加工元优先顺序约束和可用制造资源的共同约束下,将缩短加工周期、提高加工质量和降低加工成本的综合目标表达为制造资源更换率最低,进行优化目标函数的数学建模。指出加工元优化排序可类比旅行商问题,并选择AACA进行优化求解。实例分析表明提出的方法可以可靠和有效地得到符合生产实际的工艺路线。     

[综述]高速数控转台优化设计方法研究现状与展望

在机床的高速加工过程中,因数控转台各种热源发热与变载荷的共同作用而产生热-力耦合效应,但当前的数控转台优化设计方法往往忽略了热-力耦合效应对转台动态特性的影响。在深入剖析国内外数控转台研究成果的基础上,提出探索热-力耦合效应及其产生的机理,建立转台的热-力耦合模型,得到高速数控转台热-力耦合动态特性。鉴于蜂窝结构具有良好的散热性及力学性能,提出研究基于蜂窝的转台仿生原理,获取高速数控转台热-力耦合仿生设计准则。以提高转台热-力耦合动态特性为目的,提出运用仿生设计准则对结构进行多目标优化,进而形成一套新颖的高速数控转台热-力耦合仿生优化设计方法。     

A real time machining error compensation method based on dynamic features for cutting force induced elastic deformation in flank milling

During the machining process, cutting forces cause deformation of thin-walled parts and cutting tools because of their low rigidity. Such deformation can lead to undercut and may result in defective parts. Since there are various unexpected factors that affect cutting forces during the machining process, the error compensation of cutting force induced deformation is deemed to be a very difficult issue. In order to address this challenge, this article proposes a novel real time deformation error compensation method based on dynamic features. A dynamic feature model is established for the evaluation of feature rigidity as well as the association between geometric information and real time cutting force information. Then the deformations are calculated based on the dynamic feature model. Eventually, the machining error compensation for elastic deformation is realized based on Function Blocks. A thin-walled feature is used as an example to validate the proposed approach. Machining experiment results show that the errors of calculated deformation with the monitored deformation is less than 10%, and the thickness errors were between ?0.05 mm and +0.06 mm, which can well satisfy the accuracy requirement of structural parts by NC (Numerical Control) machining.     

精密磁致伸缩致动器的动态非线性多场耦合建模

为提高超磁致伸缩致动器(GMA)的精度,描述其在动态和准静态环境下的复杂磁滞行为,设计了具有精密位移输出的GMA,建立了包含磁滞及涡流损失的动态非线性多场耦合模型。首先,采用模块化方法设计了GMA;然后,利用热力学理论和能量守恒定律,建立了超磁致伸缩材料非线性多场耦合本构模型;最后,通过分析材料非线性本构行为与系统结构动态行为间的耦合过程,提出了GMA的动态非线性多场耦合模型。实验分析了能量损失及预紧力对系统特性的影响规律。结果表明:预紧力可改善系统输出特性且存在最佳预紧状态;建立的模型能够较准确预测位移,平均相对误差约为4.5%。另外,随着频率增加,异常和涡流能量损失以及磁滞量会增大,磁滞行为源于磁畴不可逆运动过程中的能量损失。实验还显示:对于精密GMA系统,不能忽略高频涡流效应。建立的模型较准确地描述了动态及准静态环境下GMA的复杂磁滞行为,由于考虑了材料本构行为耦合和系统动态行为耦合,进一步提高了GMA系统的精度。     

薄板类零件加工精度可靠性分析及工艺参数优化

针对薄板类零件加工过程中加工变形导致加工精度低的问题,利用有限元法和高斯过程回归算法建立了加工变形预测模型,综合考虑机床运动误差与工件加工变形,对薄板件加工精度可靠性进行分析,建立了以加工效率和平均加工变形为目标、加工精度可靠度为约束的铣削加工工艺参数优化设计模型,并利用多目标优化算法进行求解,确定了协调加工效率和加工变形最优的工艺参数组合。案例研究结果表明,经优化设计后最低加工精度可靠度达到98.21%,平均加工变形减小21.14%,加工效率提高了4.18%,为薄板类零件铣削加工工艺参数选择提供了一种可行的方法。     

圆周铣削参数多目标模糊优化建模

金属切削加工参数优化问题本质上是一个模糊问题。分析了圆周铣削传统优化模型的缺点 ,建立了圆周铣削参数多目标模糊优化模型 ,并根据模糊集合原理 ,将其转化为一个传统的单目标约束优化问题 ,从而可用任一非线性规划求解器进行求解。算例表明 ,模糊优化解具有优越性。     

Multi-criteria end milling parameters optimization of AISI D2 steel using genetic algorithm

This paper focuses on using multi-criteria optimization approach in the end milling machining process of AISI D2 steel. It aims to minimize the cost caused by a poor surface roughness and the electrical energy consumption during machining. A multi-objective cost function was derived based on the energy consumption during machining, and the extra machining needed to improve the surface finish. Three machining parameters have been used to derive the cost function: feed, speed, and depth of cut. Regression analysis was used to model the surface roughness and energy consumption, and the cost function was optimized using a genetic algorithm. The optimal solutions for the feed and speed are found and presented in graphs as functions of extra machining and electrical energy cost. Machine operators can use these graphs to run the milling process under optimal conditions. It is found that the optimal values of the feed and speed decrease as the cost of extra machining increases and the optimal machining condition is achieved at a low value of depth of cut. The multi-criteria optimization approach can be applied to investigate the optimal machining parameters of conventional manufacturing processes such as turning, drilling, grinding, and advanced manufacturing processes such as electrical discharge machining.     

一种并联机器人误差综合补偿方法

针对并联机器人轨迹规划和轨迹跟踪过程中,同时存在机构误差引起的期望轨迹与理想轨迹之间的偏差和非线性摩擦、负载变化等扰动因素引起的动态误差,提出一种并联机器人误差综合补偿方法:在轨迹规划过程中,基于并联机器人位姿误差模型将位姿误差补偿转化为驱动杆参数组合优化问题,进而利用粒子群算法寻优驱动杆参数,修正并联机器人期望轨迹;在轨迹跟踪过程中,设计基于自适应迭代学习控制算法的动态误差补偿策略,实现对期望轨迹的有效跟踪。在Stewart平台下基于ADAMS和Matlab进行仿真试验,在轨迹规划和轨迹跟踪过程中,分别修正期望轨迹偏差并补偿轨迹跟踪动态误差,实现并联机器人误差综合补偿。进一步,基于混联机床进行工件加工试验,验证方法对于提高并联机器人工作精度的有效性。     

基于禁忌制造特征动态调整的STEP-NC工艺路线蚁群优化方法

针对STEP-NC制造特征的加工工艺路线生成与优化问题,提出了一种以机床、夹具及刀具更换率最低为目标函数的禁忌制造特征动态更新的工艺路线蚁群优化方法。首先,根据加工工艺对制造特征进行分解,将工艺路线优化问题转化为对制造特征的排序;然后根据制造特征在加工过程中存在的加工遮挡关系以及刚性工艺性约束,提出禁忌制造特征动态更新的工艺路线生成方法;最后将禁忌制造特征动态更新方法与Ant-Cycle模型的蚁群算法相结合,求解制造特征工艺路线的优化问题。实例应用表明,提出的方法能较好解决加工工艺路线优化过程中的刚性约束处理问题。     

Dynamic characterization of machining robot and stability analysis

Machining robots have major advantages over cartesian machine tools because of their flexibility, their ability to reach inaccessible areas on a complex part, and their important workspace. However, their lack of rigidity and precision is still a limit for precision tasks. Innovations and design optimization of robotic structure, links, and power transmission allow robot manufacturers to propose business solutions for machining applications. Beyond accuracy problems, it is also necessary to quantify the vibration phenomena that may affect, as in machine tools, the quality of machined parts and the tools and spindle lifespan. These vibrations occurred at specific machining conditions depending on robot and spindle dynamic properties. The robot’s posture evolved significantly in its workspace and induces dynamic’s changes observed at the tool tip that in turn impact the stability of the machining process. The objective of this paper is to quantify the dynamic behavior’s variation of an ABB IRB 6660 robot equipped with a high-speed machining (HSM) spindle in its workspace and analyze the consequences in terms of machining stability. Through an experimental modal characterization, significant variability of modal parameters is observed at the tool tip and impacts the stability of machining. The results show that an adjustment of the cutting conditions must accompany the change of robot posture during machining to ensure stability.