A Novel Artificial Neural Networks Force Model for End Milling
A Novel Artificial Neural Networks Force Model for End Milling
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DOI：
10.1007/s001700170011
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发表时间：
2001-11
期刊：
影响因子：
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通讯作者：
V. Tandon;H. El-Mounayri
中科院分区：
文献类型：
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作者：
V. Tandon;H. El-Mounayri
多点金属切削的物理过程取决于大量相互联系紧密的参数。文献中描述了一些经验和半力学模型。本文采用人工神经网络（ann）方法，基于一组输入的加工条件，建立了切削力等关键工艺参数的综合模型。选择一组8个输入变量代表加工条件，并预测工艺参数（如最大力和平均力）。进行了详尽的实验来开发模型并对其进行验证。该模型在一个典型的工业加工场景中进行了测试，即口袋铣削。仿真结果与实验结果吻合良好。
The physical process of multipoint metal cutting depends on a large number of parameters that are strongly interlinked. A number of empirical and semimechanistic models are described in the literature. This paper uses the artificial neural networks (ANNs) approach to evolve a comprehensive model for critical process parameters, such as cutting force, based on a set of input machining conditions. A set of eight input variables is chosen to represent the machining conditions, and process parameters (such as maximum force and mean force) are predicted. Exhaustive experimentation is conducted to develop the model and to validate it. The model is tested for a typical machining scenario found in industry, namely pocket-milling. Excellent agreement between the simulated and experimental forces is found.