• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>18th International IFHTSE congress >Modeling of Hardness of Low Alloy Steels by Means of Neural Networks
【24h】

Modeling of Hardness of Low Alloy Steels by Means of Neural Networks

机译：基于神经网络的低合金钢硬度建模

获取原文
获取原文并翻译 | 示例
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

The tempering process aims to get the microstructures that lead to service mechanical properties and to promote the relaxation of the residual stresses generated during quenching. The goal of this work is to predict the effect of tempering time and tempering temperature on hardness by means of neural networks (NNs). Five types of steels, SAE 4140, SAE 4340, SAE 5160, SAE 6150, and SAE 52 100, were tempered in different conditions. The inputs of the NNs were the chemical composition, the tempering time, and tempering temperature, while hardness was the output. The selected temperatures were 100, 150, 200, 250, 300, 400, 500, 600, and 700℃. The time on each temperature was 10, 90, 900, 3600, 9000, and 86 400 s. Many architectures were tested, until the best one that fitted the data was found. To evaluate this NN the correlation coefficient (R value) was calculated and an analysis of variance test was performed.
机译：回火过程旨在获得导致服务机械性能的微观结构，并促进淬火过程中产生的残余应力的松弛。这项工作的目的是通过神经网络（NNs）预测回火时间和回火温度对硬度的影响。在不同条件下回火了五种类型的钢，即SAE 4140，SAE 4340，SAE 5160，SAE 6150和SAE 52100。神经网络的输入是化学成分，回火时间和回火温度，而硬度是输出。选择的温度为100、150、200、250、300、400、500、600和700℃。每个温度下的时间分别为10、90、900、3600、9000和86400 s。测试了许多体系结构，直到找到适合数据的最佳体系结构。为了评估该NN，计算了相关系数（R值）并进行了方差分析。

著录项

  • 来源
    《18th International IFHTSE congress》|2010年|234-247|共14页
  • 会议地点 Rio de Janeiro(BR)
  • 作者

    Renata Neves Penha; Lauralice Franceschini Canale; Antonio Carlos Canale;

  • 作者单位

    Dept. of Materials, Engineering School of Sao Carlos-Univ. of Sao Paulo, Ave.Trabalhador Sao-Carlense, 400, Centro, 13566-590 Sao Carlos-SP, Brazil;

    Dept. of Materials, Engineering School of Sao Carlos-Univ. of Sao Paulo, Ave.Trabalhador Sao-Carlense, 400, Centro, 13566-590 Sao Carlos-SP, Brazil;

    Dept. of Materials, Engineering School of Sao Carlos-Univ. of Sao Paulo, Ave.Trabalhador Sao-Carlense, 400, Centro, 13566-590 Sao Carlos-SP, Brazil;

  • 会议组织
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    tempering; modeling; hardness; neural networks;

    机译：回火造型;硬度;神经网络;

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号