Purpose

Fixture layout design is concerned with immobilization of the workpiece (engine mount bracket) during machining such that the workpiece elastic deformation is reduced. The fixture holds the workpiece through the positioning of fixturing elements that causes the workpiece elastic deformation, in turn, leads to the form and dimensional errors and increased machining cost. The fixture layout has the major impact on the machining accuracy and is the function of the fixturing position. The position of the fixturing elements, key aspects, needed to be optimized to reduce the workpiece elastic deformation. The purpose of this study is to evaluate the optimized fixture layout for the machining of the engine mount bracket.

Design Methodology Approach

In this research work, using the finite element method (FEM), a model is developed in the MATLAB for the fixture-workpiece system so that the workpiece elastic deformation is determined. The artificial neural network (ANN) is used to develop an empirical model. The results of deformation obtained for different fixture layouts from FEM are used to train the ANN and finally the empirical model is developed. The model capable of predicting the deformation is embedded to the evolutionary optimization techniques, capable of finding local and global optima, to optimize the fixture layouts and to find the robust one.

Findings

For efficient optimization of the fixture layout parameters to obtain the least possible deformation, ant colony algorithm (ACA) and artificial bee colony algorithm (ABCA) are used and the results of deformation obtained from both the optimization techniques are compared for the best results.

Research Limitations Implications

A MATLAB-based FEM technique is able to provide solutions when the repeated modeling and simulations required i.e. modeling of fixture layouts (500 layouts) for every variation in the parameters requires individual modeling and simulation for the output requirement in any FEM-based software’s (ANSYS, ABACUS). This difficulty is reduced in this research. So that the MATLAB-based FEM modeling, simulation and optimization is carried out to determine the solutions for the optimized fixture layout to reach least deformation.

Practical Implications

Many a time the practicability of the machining/mechanical operations are difficult to perform costly and time-consuming when more number of experimentations are required. To sort out the difficulties the computer-based automated solution techniques are highly required. Such kind of research over this study is presented for the readers.

Originality Value

A MATLAB-based FEM modeling and simulation technique is used to obtain the fixture layout optimization. ANN-based empirical model is developed for the fixture layout deformation that creates a hypothesis for the fixture layout system. ACA and ABCA are used for optimizing the fixture layout parameters and are compared for the best algorithm suited for the fixture layout system.

中文翻译：

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发动机支架钻孔夹具布局优化，最大限度地减少工件变形

目的

夹具布局设计涉及加工过程中工件（发动机支架）的固定，从而减少工件的弹性变形。夹具通过夹具元件的定位来夹持工件，使工件产生弹性变形，进而导致形状和尺寸误差，增加加工成本。夹具布局对加工精度有主要影响，是夹具位置的函数。需要优化夹具元件的位置，关键方面，以减少工件的弹性变形。本研究的目的是评估用于发动机悬置支架加工的优化夹具布局。

设计方法论

在这项研究工作中，使用有限元方法 (FEM)，在 MATLAB 中开发了夹具 - 工件系统的模型，以便确定工件的弹性变形。人工神经网络 (ANN) 用于开发经验模型。从有限元法获得的不同夹具布局的变形结果用于训练人工神经网络，最后开发出经验模型。能够预测变形的模型被嵌入到进化优化技术中，能够找到局部和全局最优，以优化夹具布局并找到稳健的布局。

发现

为了有效优化夹具布局参数以获得尽可能小的变形，使用了蚁群算法（ACA）和人工蜂群算法（ABCA），并将两种优化技术获得的变形结果进行比较以获得最佳结果。

研究限制影响

当需要重复建模和仿真时，基于 MATLAB 的 FEM 技术能够提供解决方案，即针对参数中的每个变化对夹具布局（500 个布局）进行建模，需要针对任何基于 FEM 的软件 (ANSYS) 中的输出要求进行单独建模和仿真, 算盘）。本研究降低了这一难度。从而进行基于MATLAB的有限元建模、仿真和优化，以确定优化夹具布局以达到最小变形的解决方案。

实际影响

很多时候，当需要更多数量的实验时，机加工/机械操作的实用性很难执行，既昂贵又耗时。为了解决这些困难，非常需要基于计算机的自动化解决方案技术。对本研究的此类研究呈现给读者。

原创价值

基于MATLAB的有限元建模和仿真技术用于获得夹具布局优化。为夹具布局变形开发了基于人工神经网络的经验模型，为夹具布局系统创建了一个假设。ACA 和 ABCA 用于优化夹具布局参数，并比较适合夹具布局系统的最佳算法。