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首页> 外文期刊>International Journal of Computer Integrated Manufacturing >A generic and innovative approach for integrated simulation and optimisation of end milling using solid modelling and neural network
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A generic and innovative approach for integrated simulation and optimisation of end milling using solid modelling and neural network

机译：使用实体建模和神经网络对立铣刀进行集成仿真和优化的通用创新方法

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摘要

Accurate and integrated modelling and optimisation of 2.5 - axis end milling using standard tools and novel approaches is presented here. The resulting system provides a practical bridge between current state-of-the-art CAD/CAM and optimised CNC production. First, a novel and generic approach for extracting the in-cut geometry is implemented using ACIS1 open architecture solid modeller. Next, an ANN (artificial neural network) model is designed and implemented for force prediction. Finally, a new technique for optimising the cutting parameters is applied and verified. The technique is based on reverse mapping of the ANN model for cutting force estimation. As such, the machining process is optimised directly using the learned neural network model by adjusting the net inputs to optimal values that minimise a specified objective function subject to cutting constraints. The optimisation results are used to update the initial CL (cutter location) data file to produce an optimum NC (numerically controlled) code. The proposed approach is demonstrated and verified through a case study to show its validity, practicality, and applicability.
机译：本文介绍了使用标准工具和新颖方法进行的2.5轴端铣削的精确集成建模和优化。最终的系统为当前最先进的CAD / CAM与优化的CNC生产之间的实用桥梁。首先，使用ACIS1开放式结构实体建模器实现了一种新颖且通用的提取切入几何的方法。接下来，设计并实现了用于人工力预测的ANN（人工神经网络）模型。最后，应用并验证了一种用于优化切削参数的新技术。该技术基于ANN模型的反向映射以进行切削力估计。这样，通过将净输入值调整为最佳值，可以使学习的神经网络模型直接优化加工过程，从而使受切割约束的指定目标函数最小化。优化结果用于更新初始CL（刀具位置）数据文件，以生成最佳NC（数控）代码。通过案例研究对提出的方法进行了演示和验证，以显示其有效性，实用性和适用性。

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