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Application of ANN in the Machining Fixture Layout Optimization for Minimum Deformation of Workpiece using FEM

机译:人工神经网络在有限元加工夹具布局优化中的应用

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摘要

Machining fixtures are used to locate and constrain a workpiece during a machining operation. Fixture layout is the positioning of fixturing elements such as locators and clamps. To ensure that the workpiece is manufactured according to specified dimensions and tolerances, it must be appropriately located and clamped. Minimizing workpiece deformation due to clamping and cutting forces is essential to maintain the machining accuracy. An ideal fixture design exhibits minimal deformation while machining. The purpose of this research work is 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. This can be achieved by selecting the optimal location of fixturing elements such as locators and clamps. Finite element method (FEM) is used to find out the maximum deformation of the workpiece for various possible fixture layouts. Artificial neural network (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 machining condition. In the querying phase, the ANN results are compared with the FEM results. After the querying process, the trained ANN is used to predict the maximum deformation of the possible fixture layouts within the solution region. The layout which shows minimum deformation is selected as optimal fixture layout.
机译:加工夹具用于在加工过程中定位和约束工件。夹具布局是夹具元素(例如定位器和夹具)的定位。为了确保根据指定的尺寸和公差制造工件,必须正确放置并夹紧工件。最小化由于夹紧力和切削力引起的工件变形对于保持加工精度至关重要。理想的夹具设计在加工时具有最小的变形。这项研究工作的目的是设计一种最佳的夹具布局,以减少因加工时作用在工件上的夹紧力和加工力而导致的工件最大弹性变形。这可以通过选择固定元件(例如定位器和夹具)的最佳位置来实现。有限元方法(FEM)用于找出各种可能的夹具布局的工件最大变形。人工神经网络(ANN)被用作优化工具,以查找定位器和夹具的最佳位置。为了训练ANN,需要将足够多的输入和输出集输入ANN系统。输入内容包括定位器和夹具的位置。输出包括加工条件下相应夹具布局的工件最大变形。在查询阶段,将ANN结果与FEM结果进行比较。在查询过程之后,训练有素的人工神经网络用于预测解决方案区域内可能的灯具布局的最大变形。选择显示最小变形的布局作为最佳灯具布局。

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