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首页> 外文期刊>Archives of Metallurgy and Materials >Modeling of Self-Induced Vibrations that Occur During the Machining Process of Casting Patterns with the Use of The Fuzzy-Neural Networks Method
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Modeling of Self-Induced Vibrations that Occur During the Machining Process of Casting Patterns with the Use of The Fuzzy-Neural Networks Method

机译:应用模糊神经网络方法对铸型加工过程中产生的自激振动进行建模

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摘要

This article outlines a methodology of modeling self-induced vibrations that occur in the course of machining of metal objects, i.e. when shaping casting patterns on CNC machining centers. The modeling process presented here is based on an algorithm that makes use of local model fuzzy-neural networks. The algorithm falls back on the advantages of fuzzy systems with Takagi-Sugeno-Kanga (TSK) consequences and neural networks with auxiliary modules that help optimize and shorten the time needed to identify the best possible network structure. The modeling of self-induced vibrations allows analyzing how the vibrations come into being. This in turn makes it possible to develop effective ways of eliminating these vibrations and, ultimately, designing a practical control system that would dispose of the vibrations altogether.
机译:本文概述了一种对在金属物体加工过程中发生的自感应振动进行建模的方法,即在CNC加工中心上塑造铸型时。这里介绍的建模过程基于使用局部模型模糊神经网络的算法。该算法回落了具有Takagi-Sugeno-Kanga(TSK)后果的模糊系统和具有辅助模块的神经网络的优势,这些模块有助于优化并缩短识别最佳可能网络结构所需的时间。通过对自激振动进行建模,可以分析振动的产生方式。这进而使得有可能开发出消除这些振动的有效方法,并最终设计出一种可以完全消除振动的实用控制系统。

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