多目标的装夹方案优化及变夹紧力优化

夹具布局和夹紧力大小影响切削变形的大小和分布.基于遗传算法和有限元方法,提出一种夹具布局和夹紧力优化设计方法.该方法将同步优化夹具布局和夹紧力大小以及施加变夹紧力相结合,首先以加工变形最小化和变形分布最均匀为目标同步优化夹具布局和夹紧力大小,然后在优化后的夹具布局的基础上求解使得加工变形最小的变夹紧力大小.使用该方法进行底座薄壁零件的夹具优化设计,结果表明优化得到的设计优于经验设计和多目标优化方法,该方法有效地降低了加工过程中工件的变形,提高变形均匀度.     

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。     

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。     

可转位刀片周边刃磨夹具的结构分析与优化

针对硬质合金可转位刀片的周边精密刃磨加工,基于有限元方法对刃磨夹具进行了结构静力学和动力学分析研究。研究在磨削力和夹紧力作用下夹具各个方向的变形情况。通过模态分析和谐响应分析,研究夹具的振动特性,获得夹具随激振力频率变化的幅频响应曲线,识别出产生共振的激振频率。根据分析结果找出了夹具设计的薄弱环节,进而提出了优化夹紧顶尖结构和材料方法,结果表明该方法可有效提高夹具的定位精度和磨削加工的可靠性。     

高精度盘类薄壁零件加工工艺技术

针对盘类薄壁零件合格率低、质量问题较多,特别是两端面平行度及重点尺寸超差严重等问题,在加工实践与有限元分析技术相结合的基础上,通过采取设计专用夹具,优选夹紧布局和夹紧力,优化车削刀具参数和切削参数,合理选用切削液,降低切削温度等措施,较好地解决了盘类薄壁零件加工变形的问题,使该零件的加工质量得到了大幅度提升,成品率达到了90%以上。     

机床夹具在设计过程中夹紧力的计算

在机床夹具的设计过程中,夹紧力起着非常重要的作用。本文结合应用实例,主要介绍了在实际加工中如何正确计算夹紧力的大小,基本思路是根据静力平衡原理求出理论夹紧力的大小,然后乘以安全因数得到合理的实际夹紧力的大小。     

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 ANSYS的车身柔性薄板焊装夹具优化设计

介绍薄板焊装夹具在国内外研究进展,然后在＂N-2-1＂定位原理的基础上,建立了适合车身焊接工艺的工件定位点优化设计的数学模型,提出了一种可以快速确定工件定位点位置以及夹具布局方案的设计方法。利用ANSYS有限元软件对模型进行了实验分析和验证,得出板料有限元网格的大小、夹具的定位点、夹紧点及夹紧力的大小等对接触变形的影响。结果表明该模型和方法快速、准确、合理,达到了预期设计的目标。     

关节轴承精加工夹具设计和夹紧力的计算

针对关节轴承精加工变形严重的问题,提出了在精加工中如何合理地设计夹具及如何准确计算夹紧力以减小关节轴承的加工变形、提高关节轴承的加工质量。基本思路是依据关节轴承结构和加工工艺设计夹具结构,依据轴承尺寸设计夹具尺寸;对夹具进行静力分析,依据静力平衡原理建立夹紧力的数学模型;通过关节轴承精加工实验验证了夹具和夹紧力数学模型的可靠性,有效地减小了关节轴承精加工过程中产生的变形,得到了表面质量满足要求的关节轴承。     

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.     

Development of a finite element analysis tool for fixture design integrity verification and optimisation

Machining fixtures are used to locate and constrain a workpiece during a machining operation. To ensure that the workpiece is manufactured according to specified dimensions and tolerances, it must be appropriately located and clamped. Minimising workpiece and fixture tooling deflections due to clamping and cutting forces in machining is critical to machining accuracy. An ideal fixture design maximises locating accuracy and workpiece stability, while minimising displacements.The purpose of this research is to develop a method for modelling workpiece boundary conditions and applied loads during a machining process, analyse modular fixture tool contact area deformation and optimise support locations, using finite element analysis (FEA). The workpiece boundary conditions are defined by locators and clamps. The locators are placed in a 3-2-1 fixture configuration, constraining all degrees of freedom of the workpiece and are modelled using linear spring-gap elements. The clamps are modelled as point loads. The workpiece is loaded to model cutting forces during drilling and milling machining operations. Fixture design integrity is verified. ANSYS parametric design language code is used to develop an algorithm to automatically optimise fixture support and clamp locations, and clamping forces, to minimise workpiece deformation, subsequently increasing machining accuracy. By implementing FEA in a computer-aided-fixture-design environment, unnecessary and uneconomical “trial and error” experimentation on the shop floor is eliminated.     

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.     

A setup planning methodology for prismatic parts considering fixturing aspects

Setup planning is an important part of process planning that has been widely investigated by various researchers. However, the output of the traditional setup planning approaches is limited and insufficient for the upstream process planning activity, fixture design. In this work, a setup planning system is developed that provides sufficient inputs to the fixture designer in terms of recommended depth of cut and feed, fuzzy clamping forces, near optimal locator and clamp layout, and sizes of the locators and clamps. The fixture designer can further optimize the fixture plan by taking these inputs. The methodology is illustrated with the help of an example.     

基于花授粉算法的曲面薄壁件定位布局优化

优化定位布局是减小薄壁件装夹变形的重要手段,现有研究大多以节点法向变形最小为优化目标而忽略其他方向上的变形,为此提出了一种新的基于花授粉算法的夹具布局优化方法。针对曲面薄壁件,在建立法向约束定位模型的基础上,通过应变能来描述所有方向上的变形,以薄壁件的整体应变能最小为目标,结合花授粉算法和基于Python语言的参数化有限元分析,实现薄壁件的定位布局寻优。最后以飞机蒙皮定位布局优化为例验证方法的有效性,并通过与遗传算法的对比表明,花授粉算法在优化薄壁件的定位布局时具有更优的性能。     

A feature-based approach for optimal workpiece localization and determination of feasible clamping regions

In this paper, we present an approach for optimally selecting the locating positions of workpieces and identifying feasible clamping regions that meet the requirements of the form-closure principle for fixture layout. This approach firstly finds an optimal configuration of locating points on a set of faces of the workpiece which minimizes the error transfer from locating points to machining features. In the formulation of the optimization, the error transfer is modeled using an error transfer matrix. The eigenvalues of the matrix are used as objective functions of the optimization. Secondly, this approach computes the clamping wrench cone and its member wrench can form a form-closure fixture layout, together with the formerly selected locating points. Finally, it generates the feasible clamping regions by projecting the clamping wrench cone onto faces of the clamping feature faces. An example is included to illustrate the effectiveness of the proposed approach. Compared with traditional methods in which only error transfer from the locating points to the workpiece’s mass-center is considered, the error transfer control in this approach is more effective and of greater efficiency. In addition, the clamping wrench cone projection method for the generation of feasible clamping regions is more suitable for handling cases with concave clamping faces than traditional convex-combination-based methods.