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Artificial Neural Networks: A Manufacturing Engineering Perspective and Case Study Review

机译:人工神经网络:制造工程的观点和案例研究回顾

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摘要

This paper presents a brief review of Artificial Neural Network (ANN) application in a typical manufacturing engineering scenario. The discussion in the first part centres on the underlying principles and learning algorithms with emphasis on the basic structure of ANNs. It would be extremely laborious and tedious to list all types of neural networks herein but for the purpose of this study, an overview of those networks with proven manufacturing engineering applications was deemed necessary. The merits of ANNs and their applicability was demonstrated by reviewing work performed within the last decade in the chosen area of manufacturing engineering application, specifically Tool Condition Monitoring (TCM) in metal cutting operations.
机译:本文简要介绍了人工神经网络(ANN)在典型制造工程场景中的应用。第一部分的讨论集中在基本原理和学习算法上,重点是人工神经网络的基本结构。在此处列出所有类型的神经网络将非常费力且乏味,但是出于本研究的目的,必须对具有成熟制造工程应用程序的那些网络进行概述。通过回顾过去十年来在制造工程应用的选定领域中所做的工作,特别是金属切削操作中的工具状态监测(TCM),证明了人工神经网络的优点及其适用性。

著录项

  • 来源
    《Journal of Communications》 |2019年第8期|636-646|共11页
  • 作者

    Eric Dimla;

  • 作者单位

    School of Science and Technology RMIT University Vietnam 702 Nguyen Van Linh District 7 Ho Chi Minh City Vietnam;

  • 收录信息
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    ANNs; learning in ANNs; Tool wear monitoring;

    机译:人工神经网络在人工神经网络中学习;刀具磨损监控;
  • 入库时间 2022-08-18 04:54:45

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