机械加工过程中表面粗糙度测量建模的人工神经网络技术综述

A. Zain, H. Haron, S. Sharif
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引用次数: 27

摘要

前者定义为加工过程的建模,为确定加工过程的目标函数提供基本的数学模型。使用统计回归技术等传统方法,开发显式模型需要对建模过程进行复杂的物理理解。利用非常规方法或人工智能技术,如人工神经网络、模糊逻辑和基于遗传算法的建模,在网络、规则和基因的权重矩阵中创建隐式模型,更容易实现。本文以表面粗糙度性能测量为重点,概述并讨论了人工神经网络在加工过程建模中的概念、应用、能力和局限性。展望了人工神经网络在加工过程建模中的应用前景。
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Review of ANN Technique for Modeling Surface Roughness Performance Measure in Machining Process
The former, which is defined as modeling of machining processes, is essential to provide the basic mathematical models for formulation of the certain process objective functions. With conventional approaches such as Statistical Regression technique, explicit models are developed that required complex physical understanding of the modeling process. With non conventional approaches or Artificial Intelligence techniques such as Artificial Neural Network, Fuzzy Logic and Genetic Algorithm based modeling, implicit model are created within the weight matrices of the net, rules and genes that is easier to be implemented. With the focus on surface roughness performance measure, this paper outlines and discusses the concept, application, abilities and limitations of Artificial Neural Network in the machining process modeling. Subsequently the future trend of Artificial Neural Network in modeling machining process is reported.
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