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Development of an integrated approach combining artificial neural network material based modeling with finite element analysis of forming processes.

机译:集成方法的开发,该方法将基于人工神经网络材料的建模与成形过程的有限元分析相结合。

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摘要

The use of a finite element model for design and analysis of a metal forming processes is limited by the incorporated material model's ability to predict deformation behavior over a wide range of operating conditions. Conventionally generated rheological models prove deficient in several respects due to the difficulty in establishing complicated relations between many parameters. More recently, artificial neural networks (ANN) have been suggested as an effective means to overcome these difficulties. To this end, a robust ANN with the ability to determine flow stresses based on strain, strain rate, and temperature is developed and linked with finite element-based simulation model. Comparisons of this novel method with conventional means are carried out to demonstrate the advantages of this approach as applied to industrial applications.
机译:有限元模型用于金属成型过程的设计和分析的使用受到合并材料模型预测大范围工作条件下变形行为的能力的限制。由于难以在许多参数之间建立复杂的关系,因此传统上生成的流变模型在几个方面都存在缺陷。最近,人工神经网络(ANN)已被建议作为克服这些困难的有效手段。为此,开发了一种能够基于应变,应变速率和温度确定流应力的鲁棒人工神经网络,并将其与基于有限元的仿真模型相联系。将该新方法与常规方法进行比较,以证明该方法在工业应用中的优势。

著录项

  • 作者

    Kessler, Brian Scott.;

  • 作者单位

    University of Missouri - Columbia.;

  • 授予单位 University of Missouri - Columbia.;
  • 学科 Engineering Mechanical.
  • 学位 Ph.D.
  • 年度 2005
  • 页码 245 p.
  • 总页数 245
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 机械、仪表工业;
  • 关键词

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