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Joint optimization for knowledge mining : evaluating parameters of manufacturing processes

机译:联合优化知识挖掘:评估制造过程的参数

摘要

In various kinds of manufacturing production, predicting the influence of process parameters in terms of machine performance is a necessity as they may have a serious impact on product quality as well as on the probability of machine failure. To address this issue, this paper presents a novel knowledge-based algorithm embedded with artificial intelligence for evaluating the overall suitability of adopting the predicted control parameters suggested by domain experts. The originality of this research is that the proposed knowledge-based system is equipped with fuzzy-guided genetic algorithm, enabling the identification of the best set of process parameters. Simulation using the RIE machine is provided to validate the practicability of the proposed approach.
机译:在各种制造业生产中,必须根据机械性能预测过程参数的影响,因为它们可能会对产品质量以及机械故障的可能性产生严重影响。为了解决这个问题,本文提出了一种新的基于知识的,嵌入人工智能的算法,用于评估采用领域专家建议的预测控制参数的总体适用性。这项研究的独创性是,所提出的基于知识的系统配备了模糊引导遗传算法,从而能够确定最佳的工艺参数集。提供了使用RIE机器进行的仿真,以验证所提出方法的实用性。

著录项

  • 作者

    Tang CXH; Lau HCW;

  • 作者单位
  • 年度 2009
  • 总页数
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类

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