基于遗传算法的夹具布局和夹紧力同步优化

夹具设计是机械加工中一个重要步骤。夹具优化旨在得到最合理的夹具布局和夹紧力。为了弥补分步优化夹具布局和夹紧力以及应用传统优化算法而存在的不足,本文提出了应用遗传算法同步优化夹具布局和夹紧力的方法。使用该方法进行夹具优化的算例结果表明优化得到的设计优于经验设计,该方法是一种有效的夹具优化方法。     

夹具布局和夹紧力的优化方法研究

在分析了目前夹具布局和夹紧力优化设计方法的基础上,基于遗传算法和有限元方法,提出了一种优化夹具布局和夹紧力的方法。通过对一薄壁件的夹具优化分析,验证了该方法的有效性,该方法可以降低由于装夹不当所引起的工件变形程度,从而提高了加工精度。     

基于MATLAB优化算法的汽车缸盖多重夹紧力的优化分析

首先通过分析缸盖与夹具定位元件、夹紧元件之间的接触特点,建立了相应的接触副模型。在假定夹具夹紧点的布局位置以及夹紧顺序固定的基础上,以定位元件与工件接触区域半弹性变形所导致的工件最小位移为第一目标函数,同时以接触点变形最小总余能为第二目标函数。根据工件的具体加工过程静力平衡列出约束条件,构建了夹紧力的多目标优化模型。最后,根据多目标优化模型得到相应的结果。夹具的多重夹紧力经过该模型优化后,对应所需的夹紧力显著降低,这对提高加工精度和减少成本具有重要的意义。     

高速开关阀控制的可变夹紧力夹具

针对恒定夹紧力夹具对零件加工精度的影响,提出一种变夹紧力夹具方案。该夹具根据切削力的大小,优化分析工件最佳的夹紧点位置及各点的夹紧力,能够自动调整夹紧力的大小,以适应切削力,减少加工系统的切削变形。采用同工步数字控制技术,实现自适应夹紧功能,采用高速开关阀作为液压系统的动态控制元件控制夹紧力的大小。     

基于混合遗传算法的机床夹具夹紧力优化

利用改进的遗传算法对机床夹具夹紧力进行了优化分析,首先对遗传算法进行了优化,然后建立了夹紧力的优化目标及相应的约束条件,最后采用混合遗传算法对该优化模型进行优化。结果表明,优化后的夹紧力明显减小,而且各个方向的夹紧力分布也更为合理。该方法对其他夹紧力的计算具有一定的参考意义。     

弱刚度工件夹紧顺序与夹具布局的同步优化

针对弱刚度工件在定位、夹紧过程中易变形的问题,建立了夹紧顺序与接触力及节点位移增量之间关系的数学模型,给出了各夹紧步骤中工件夹具系统的静力平衡方程;在此基础上,根据最小余能原理及库仑摩擦定律,构建了装夹方案优化模型,提出了基于遗传算法的夹具布局与夹紧顺序同步优化方法。算例结果表明,该方法有效降低了由于装夹所引起的工件变形。提高了加工精度。     

夹具夹紧方案优化设计

综合分析夹具夹紧误差的各种影响因素及其影响方式,并根据影响方式归纳产生夹紧变形的两大原因,即由夹紧副变形导致的工件位置误差与由外力导致的工件变形,由此建立夹紧副变形与工件位置误差的通用关系模型;以工件位置误差最小为目标,建立了夹紧力的优化模型,可以同时实现夹紧力大小与作用点的稳健优化设计。最后用一典型实例说明了夹紧力的优化结果。所介绍的方法不仅适用于夹具设计,而且对机器人多手指抓取规划同样适用。     

滚齿夹具螺旋夹紧机构的优化替代方案

螺旋夹紧机构在夹具中广泛使用。这种机构结构简单、夹紧牢靠,但使用时费时费力,且容易引起变形,影响加工精度,因此设计夹具时必须认真考虑其利弊。文中介绍了一种滚齿夹具螺旋夹紧机构的优化替代方案。     

多件装夹铣具的夹紧力计算

铣削较小工件常采用多件装夹铣具,如图1所示就是利用夹具弹性变形来同时夹紧4个如图2所示零件的铣具。铣具被安装在卧式铣床上,用两把三面刃铣刀一次将零件加工到尺寸13±0.09,夹具的结构如图1所示。该夹具装夹方便,夹紧力分布合理(两处同时压紧),夹具的设计参数及作用于扳手的力都恰到好处,因此该夹具使用起来省力,零件也被夹得牢固。设计该夹具主要技术问题是要对夹紧力进行计算。本文将对夹紧力作用于扳手的力的计算作详细介绍。 一、夹具的作用原理 首先将零件分别装入夹具中,操作者用扳手作用于右牙螺母齿轮上。右牙螺母齿轮6转动并沿右牙螺     

基于PLC的夹紧力在线调控系统

为了获得金属切削过程中的合理夹紧力,减少薄板件的铣削变形,将PLC技术应用到机床夹具的设计中,设计了夹紧力大小可动态调控的液压夹具系统(HFAC).HFAC系统包括夹紧机构、液压系统和控制机构,通过PLC闭环控制调节电液比例减压阀的出口压力,以使系统获得高精度夹紧力,并实现夹紧力的动态调控.实验结果表明,HFAC系统可实时调节机床夹具的夹紧力,其夹紧力控制精度为3％左右,即系统具有良好的稳定性以及较高的夹紧力控制精度.     

Machining fixture layout design using ant colony algorithm based continuous optimization method

In any machining fixture, the workpiece elastic deformation caused during machining influences the dimensional and form errors of the workpiece. Placing each locator and clamp in an optimal place can minimize the elastic deformation of the workpiece, which in turn minimizes the dimensional and form errors of the workpiece. Design of fixture configuration (layout) is a procedure to establish the workpiece–fixture contact through optimal positioning of clamping and locating elements. In this paper, an ant colony algorithm (ACA) based discrete and continuous optimization methods are applied for optimizing the machining fixture layout so that the workpiece elastic deformation is minimized. The finite element method (FEM) is used for determining the dynamic response of the workpiece caused due to machining and clamping forces. The dynamic response of the workpiece is simulated for all ACA runs. This paper proves that the ACA-based continuous fixture layout optimization method exhibits the better results than that of ACA-based discrete fixture layout optimization method.     

Design and optimization of machining fixture layout using ANN and DOE

In machining fixtures, minimizing workpiece deformation due to clamping and cutting forces is essential to maintain the machining accuracy. This can be achieved by selecting the optimal location of fixturing elements such as locators and clamps. Many researches in the past decades described more efficient algorithms for fixture layout optimization. In this paper, artificial neural networks (ANN)-based algorithm with design of experiments (DOE) is proposed to design an optimum fixture layout in order to reduce the maximum elastic deformation of the workpiece caused by the clamping and machining forces acting on the workpiece while machining. Finite element method (FEM) is used to find out the maximum deformation of the workpiece for various fixture layouts. ANN is used as an optimization tool to find the optimal location of the locators and clamps. To train the ANN, sufficient sets of input and output are fed to the ANN system. The input includes the position of the locators and clamps. The output includes the maximum deformation of the workpiece for the corresponding fixture layout under the machining condition. In the testing phase, the ANN results are compared with the FEM results. After the testing process, the trained ANN is used to predict the maximum deformation for the possible fixture layouts. DOE is introduced as another optimization tool to find the solution region for all design variables to minimum deformation of the work piece. The maximum deformations of all possible fixture layouts within the solution region are predicted by ANN. Finally, the layout which shows the minimum deformation is selected as optimal fixture layout.     

Optimal Fixture Design Accounting for the Effect of Workpiece Dynamics

This paper presents a fixture layout and clamping force optimal synthesis approach that accounts for workpiece dynamics during machining. The dynamic model is based on the Newton– Euler equations of motion, with each fixture–workpiece contact modelled as an elastic half-space subjected to distributed nor-mal and tangential loads. The fixture design objective in this paper is to minimise the maximum positional error at the machining point during machining. An iterative fixture layout and clamping force optimisation algorithm that yields the "best" improvement in the objective function value is presented. Simulation results show that the proposed optimis-ation approach produces significant improvement in the work-piece location accuracy. Additionally, the method is found to be insensitive to the initial fixture layout and clamping forces.     

Fixture Clamping Force Optimisation and its Impact on Workpiece Location Accuracy

Workpiece motion arising from localised elastic deformation at fixture-workpiece contacts owing to clamping and machining forces is known to affect significantly the workpiece location accuracy and, hence, the final part quality. This effect can be minimised through fixture design optimisation. The clamping force is a critical design variable that can be optimised to reduce the workpiece motion. This paper presents a new method for determining the optimun clamping forces for a multiple clamp fixture subjected to quasu-static machining forces. The method uses elastic contact mechanics models to represent the fixture-workpiece contact and involves the formulation and solution of a multi-objective constrained oprimisation model. The impact of clamping force optimisation on workpiece location accuracy is analysed through examples involving a 3-2-1 type milling fixture.     

Deformation control through fixture layout design and clamping force optimization

Workpiece deformation must be controlled in the numerical control machining process. Fixture layout and clamping force are two main aspects that influence the degree and distribution of machining deformation. In this paper, a multi-objective model was established to reduce the degree of deformation and to increase the distributing uniformity of deformation. The finite element method was employed to analyze the deformation. A genetic algorithm was developed to solve the optimization model. Finally, an example illustrated that a satisfactory result was obtained, which is far superior to the experiential one. The multi-objective model can reduce the machining deformation effectively and improve the distribution condition.     

薄壁弧形件铣削加工装夹方案的优化

在航空制造业中,为了减轻工件的重量,大量使用薄壁结构零件。对于这类工件,改进工件的夹具布局能够有效地减少工件的变形。本文通过对弧形薄壁件铣削加工夹紧点的位置进行优化来减少工件的变形,并提出了6条启发式规则。这些规则根据各区域内工件的最大变形,依次确定各夹紧点的移动方向,具有较快的优化速度。     

Multi-objective optimization design of a fixture layout considering locator displacement and force–deformation

The fixture determines the workpiece position in a machining process; therefore, an increasing amount of attention has been given to fixture layout design. While machining, the workpiece position is affected by two major sources: (a) the locator displacement and (b) the force–deformation of the workpiece–fixture system. In the beginning of this paper, a geometric model considering the shape of a locator is developed to analyze the location performance, followed by the presentation of a simplified solving method and a location layout performance index. Second, to complete the force–deformation analysis, a finite element method-based force–deformation model is built and accelerated by a new method with a lower computer memory cost. Based on these two models, multiple objects of fixture layout optimization problems are proposed, and a multi-objective genetic algorithm-based optimization method is constructed. Finally, testing examples are approved to examine the validity of the method represented in this paper. These methods can provide a more accurate prediction of the locating performance in more widely used cases, and they have faster calculating speeds with lower computer memory costs.     

面向大规模定制的夹具优化设计系统

针对大规模定制生产环境下夹具相似性的特点,提出了以工件的加工变形最小和变形最均匀为目标的夹具优化设计方法.基于SolidWorks平台,以VC为开发工具,采用遗传算法和有限元方法相结合的优化方法,开发了参数化夹具优化设计系统,并进行了验证.