面向加工中心的高速铣刀优选系统

针对加工中心高速铣削中刀具及其切削参数选择问题,以已有高速铣削机理研究成果为基础,进行工件高速铣削加工特征研究,并建立高速铣刀优选目标模型和优选规则,完成了基于关系型数据库的高速铣刀优选系统的开发.结果表明,采用该系统优选的铣刀及其切削参数进行高速铣削加工,能有效提高加工品质和加工效率、降低加工成本.     

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.     

基于遗传算法的复杂槽型铣刀片铣削参数优化

复杂槽型铣刀片具有优于平前刀面铣刀片的切削性能。文中针对复杂槽型铣刀片的加工过程，建立了铣削加工的工艺参数优化模型和约束条件，提出了基于遗传算法的铣削参数多目标优化算法，并进行了优化实例分析，得到了合理的铣削参数。     

Multi-objective optimization of multi-pass face milling using particle swarm intelligence

In this paper, to facilitate manufacturing engineers have more control on the machining operations, the optimization issue of machining parameters is handled as a multi-objective optimization problem. The optimization strategy is to simultaneously minimize production time and cost and maximize profit rate meanwhile subject to satisfying the constraints on the machine power, cutting force, machining speed, feed rate, and surface roughness. An efficient fuzzy global and personal best-mechanism-based multi-objective particle swarm optimization (F-MOPSO) to optimize the machining parameters is developed to solve such a multi-objective optimization problem in optimization of multi-pass face milling. The proposed F-MOPSO algorithm is first tested on several benchmark problems taken from the literature and evaluated with standard performance metrics. It is found that the F-MOPSO does not have any difficulty in achieving well-spread Pareto optimal solutions with good convergence to true Pareto optimal front for multi-objective optimization problems. Upon achieving good results for test cases, the algorithm was employed to a case study of multi-pass face milling. Significant improvement is indeed obtained in comparison to the results reported in the literatures. The proposed scheme may be effectively employed to the optimization of machining parameters for multi-pass face milling operations to obtain efficient solutions.     

Multi-objective optimization of high-speed milling with parallel genetic simulated annealing

In this paper, the optimization of multi-pass milling has been investigated in terms of two objectives: machining time and production cost. An advanced search algorithm—parallel genetic simulated annealing (PGSA)—was used to obtain the optimal cutting parameters. In the implementation of PGSA, the fitness assignment is based on the concept of a non-dominated sorting genetic algorithm (NSGA). An application example is given using PGSA, which has been used to find the optimal solutions under four different axial depths of cut on a 37 SUN workstation network simultaneously. In a single run, PGSA can find a Pareto-optimal front which is composed of many Pareto-optimal solutions. A weighted average strategy is then used to find the optimal cutting parameters along the Pareto-optimal front. Finally, based on the concept of dynamic programming, the optimal cutting strategy has been obtained.     

Optimization of machining parameters using genetic algorithm and experimental validation for end-milling operations

Optimization of cutting parameters is valuable in terms of providing high precision and efficient machining. Optimization of machining parameters for milling is an important step to minimize the machining time and cutting force, increase productivity and tool life and obtain better surface finish. In this work a mathematical model has been developed based on both the material behavior and the machine dynamics to determine cutting force for milling operations. The system used for optimization is based on powerful artificial intelligence called genetic algorithms (GA). The machining time is considered as the objective function and constraints are tool life, limits of feed rate, depth of cut, cutting speed, surface roughness, cutting force and amplitude of vibrations while maintaining a constant material removal rate. The result of the work shows how a complex optimization problem is handled by a genetic algorithm and converges very quickly. Experimental end milling tests have been performed on mild steel to measure surface roughness, cutting force using milling tool dynamometer and vibration using a FFT (fast Fourier transform) analyzer for the optimized cutting parameters in a Universal milling machine using an HSS cutter. From the estimated surface roughness value of 0.71 μm, the optimal cutting parameters that have given a maximum material removal rate of 6.0×10³ mm³/min with less amplitude of vibration at the work piece support 1.66 μm maximum displacement. The good agreement between the GA cutting forces and measured cutting forces clearly demonstrates the accuracy and effectiveness of the model presented and program developed. The obtained results indicate that the optimized parameters are capable of machining the work piece more efficiently with better surface finish.     

基于动力学约束的端面铣削工艺参数优化

工艺参数优化对提高切削过程的加工效率和加工成本具有重要意义。将铣削系统动力学作为主要约束条件,提出端面铣削工艺参数的多目标优化模型。基于铣削系统动力学分析,得到了综合切削稳定性、工件表面粗糙度、主轴转速、切削力、切削功率等约束的工艺参数多目标优化模型。通过调节权重系数实现优化方向的控制,并采用快速粒子群算法对工艺参数进行优化计算。工艺优化实例及试验表明,采用基于动力学约束的工艺参数优化方法可以获得较好的工艺参数优化结果。     

Optimization of multi-pass face milling using a fuzzy particle swarm optimization algorithm

In this paper, a simple methodology to distribute the total stock removal in each of the rough passes and the final finish pass and a fuzzy particle swarm optimization (FPSO) algorithm based on fuzzy velocity updating strategy to optimize the machining parameters are proposed and implemented for multi-pass face milling. The optimum value of machining parameters including number of passes, depth of cut in each pass, speed, and feed is obtained to achieve minimum production cost while considering technological constraints such as allowable machine power, machining force, machining speed, tool life, feed rate, and surface roughness. The proposed FPSO algorithm is first tested on few benchmark problems taken from the literature. Upon achieving good results for test cases, the algorithm was employed to two illustrative case studies of multi-pass face milling. Significant improvement is also obtained in comparison to the results reported in the literatures, which reveals that the proposed methodology for distribution of the total stock removal in each of passes is effective, and the proposed FPSO algorithm does not have any difficulty in converging towards the true optimum. From the given results, the proposed schemes may be a promising tool for the optimization of machining parameters.     

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.     

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。     

Parameter optimization of machining processes using teaching–learning-based optimization algorithm

The optimum selection of process parameters plays a significant role to ensure quality of product, to reduce the machining cost and to increase the productivity of any machining process. This paper presents the optimization aspects of process parameters of three machining processes including an advanced machining process known as abrasive water jet machining process and two important conventional machining processes namely grinding and milling. A recently developed advanced optimization algorithm, teaching–learning-based optimization (TLBO), is presented to find the optimal combination of process parameters of the considered machining processes. The results obtained by using TLBO algorithm are compared with those obtained by using other advanced optimization techniques such as genetic algorithm, simulated annealing, particle swarm optimization, harmony search, and artificial bee colony algorithm. The results show better performance of the TLBO algorithm.     

Investigations on model-based simulation of tool wear with carbide tools in milling operation

This paper deals with tool wear in milling operation using carbide tools. The main purpose of this work is to define a model-based procedure for forecasting tool-wear progression during cutting operation by using machining simulation. Firstly, a multi-axis machining simulation algorithm is proposed based on DEXEL model and local area update method. NC milling machining process simulation software NCToolWearSim is realized by using Visual C++ and OpenGL. The developed process simulation software is used to simulate the cutting process. Secondly, tool-wear simulation algorithm in the machining process is presented with tool-wear model and machining simulation algorithm and is implemented into the machining simulation software NCToolWearSim in order to evaluate the tool wear and to update the tool geometry. The tool-wear value is estimated according to the established tool-wear model from experienced tool-wear data. Thus, tool-wear progression can be visualized in milling operation by using NCToolWearSim. Finally, experimental tests, performed milling integral wheel with carbide tools, were used to calibrate and validate the correctness of tool-wear simulation process based on the tool-wear model.     

复杂曲面环形刀五轴端铣加工的刀具轨迹规划方法

为了降低复杂曲面类零部件加工的刀具路径,减小刀具路径条数,提高加工效率,提出了一种新的复杂曲面环形刀五轴端铣加工刀具轨迹优化方法。在局部可铣性基础上对刀轴矢量角进行自适应优化,采用新型加工带宽计算方法——等残留高度算法,给出了等残留高度算法的刀具轨迹生成具体步骤。仿真结果表明:与传统等残留高速算法相比,刀具轨迹优化算法的刀具路径更短、条数更少,能够有效提高复杂曲面加工效率。     

Optimization of cryogenic milling parameters for aluminum honeycomb treated by ice fixation method

This paper presents the first comprehensive investigation that aluminum honeycomb has inevitable machining defect in milling process, such as deformation, burr, and collapse. Ice fixation method was used to clamp workpieces, and inner-injection liquid nitrogen was employed for a series of cryogenic milling machining. In the machining process, the main machining parameters including in honeycomb orientation, milling width, cutting depth, cutting speed, and feed were executed experimental research. Meanwhile, the machining parameter optimization, range, and significant analysis were adopted to analyze the influence of machining parameters on the machining surface quality, as well as the optimal parameter combination and milling machining surface quality were predicted and verified. The results show that the ice fixation aluminum honeycomb method with cryogenic milling is much advanced than that of conventional ones, and many machining defects are effectively restrained. At the same time, the influence of machining parameters on machining qualities in descending order is cutting depth, cutting speed, honeycomb orientation, feed, and milling width. The minimum roughness value (Ra?=?0.356 μm) of the predicted machining surface is similar to the actual machining result (Ra?=?0.362 μm). It verifies the feasibility of the optimization method. Furthermore, it is proved that the ice fixation + liquid nitrogen cooling method has a positive effect on the high milling quality and implement efficiency for aluminum honeycomb and other difficult-to-machine materials.     

基于混合算法的薄壁件铣削加工工艺参数优化

结合神经网络法和遗传算法的优点,提出了一种以倒传递神经网络法为基础的加工工艺参数优化方法,对薄壁件铣削加工工艺参数进行优化。将田口实验所得数据经倒传递神经网络进行训练与测试,来建立薄壁件铣削加工的信噪比预测器,并通过最大化信噪比,将铣削过程变异降至最低,进而找出最佳加工工艺参数组合。通过数值模拟与加工实验,验证了所提方法在薄壁件铣削加工工艺参数优化中的有效性。     

Optimization of micro milling of hardened steel with different grain sizes using multi-objective evolutionary algorithm

Modern manufacturing processes need high production rates, low costs, and high product quality. Generally, surface roughness is a good reference to determine the performance in machined products. The use of optimization systems can determine the optimum machining parameters in the machining process, especially in milling operations. The present study integrates the least square model based on feed rate, cutting speed, and grain size with a genetic optimization algorithm to provide the optimal process parameter. The NSGA II algorithm was applied due to its coverage and easily to optimize the micro milling of hardened steel. The responses were Fy Force and Mz Torque. The results show that the feed rate was the most significant factor for minimizing Fy force and Mz Torque.     

Surface generation modeling of micro milling process with stochastic tool wear

Micro milling is widely used to manufacture miniature parts and features at high quality with low set-up cost. To achieve a higher quality of existing micro products and improve the milling performance, a reliable analytical model of surface generation is the prerequisite as it offers the foundation for surface topography and surface roughness optimization. In the micro milling process, the stochastic tool wear is inevitable, but the deep influence of tool wear hasn't been considered in the micro milling process operation and modeling. Therefore, an improved analytical surface generation model with stochastic tool wear is presented for the micro milling process. A probabilistic approach based on the particle filter algorithm is used to predict the stochastic tool wear progression, linking online measurement data of cutting forces and tool vibrations with the state of tool wear. Meanwhile, the influence of tool run-out is also considered since the uncut chip thickness can be comparable to feed per tooth compared with that in conventional milling. Based on the process kinematics, tool run-out and stochastic tool wear, the cutting edge trajectory for micro milling can be determined by a theoretical and empirical coupled method. At last, the analytical surface generation model is employed to predict the surface topography and surface roughness, along with the concept of the minimum chip thickness and elastic recovery. The micro milling experiment results validate the effectiveness of the presented analytical surface generation model under different machining conditions. The model can be a significant supplement for predicting machined surface prior to the costly micro milling operations, and provide a basis for machining parameters optimization.     

基于刚度定向的工业机器人铣削姿态优化研究

针对工业机器人结构非对称引起的主刚度方向难以确定、因刚度低导致的铣削过程中容易发生模态耦合颤振的问题,提出了一种机器人加工系统主刚度定向方法,并利用机器人功能冗余特性优化姿态,以提高铣削过程的稳定性。计算工业机器人末端笛卡儿坐标系中的刚度椭球,确定切削平面内机器人的主刚度方向;通过建立加工系统的动力学模型,得到机器人铣削模态耦合颤振的稳定性判据;基于刚度定向方法,提出一种机器人铣削姿态优化算法。实验结果表明,在不改变其他参数的情况下,通过优化工业机器人姿态,可以保证机器人沿给定轨迹加工的稳定性。     

基于遗传算法的复杂槽型铣刀片槽型参数优化

针对三维复杂槽型铣刀片槽型优化问题,进行了铣削温度和铣削力试验及其有限元分析,以铣刀片耦合场最优为优化目标,建立了槽型参数多目标优化数学模型,利用遗传算法求解了固定切削参数和给定约束下的优化槽型参数。仿真计算结果表明,槽型优化后的铣刀片耦合场明显优于其它槽型参数下的铣刀片耦合场,为铣刀片三维复杂槽型的重构提供了依据。     

三维微细电解铣削加工的实时控制与检测

为了实现三维微细电解铣削加工过程的实时监测,建立了基于Labwindows/CVI软件平台的控制与检测系统。对该系统所采用的三维轨迹生成及控制策略,数据采集及抗干扰算法,加工时间误差补偿算法等进行了研究。首先,根据微细电解铣削加工的特点,分析了加工控制与检测系统的需求。接着,搭建了高精度的三维微细铣削加工实验硬件平台。然后,利用虚拟仪器平台建立了基于分层铣削加工方式的三维轨迹进给控制模块,并对刀具轨迹的优化进行了讨论。最后,介绍了数据采集及反馈控制模块以及实时控制的时间补偿函数。基于上述控制与检测系统,实验并成功加工出了单层尺寸为15μm×55μm×15μm的三层阶梯结构,结果表明,本系统可以满足微细电解铣削加工的高精度、快响应、稳定可靠等要求。