• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>International Journal of Computer Integrated Manufacturing >Modelling of weld-bead geometry and hardness profile in laser welding of plain carbon steel using neural networks and genetic algorithms
【24h】

Modelling of weld-bead geometry and hardness profile in laser welding of plain carbon steel using neural networks and genetic algorithms

机译：基于神经网络和遗传算法的普通碳素钢激光焊接中焊缝几何形状和硬度分布模型

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

An attempt was made to predict weld-bead geometry and its cross-sectional micro-hardness profile produced by laser welding of plain carbon steel (DC05) for a given set of process parameters. Welding was done using ytterbium fibre laser by considering laser power, weld speed and distance of the focal point from the sample surface as the input parameters. Microscopy was used to measure the weld dimensions. Micro-indentation was made to measure the corresponding Vickers' hardness along the horizontal cross section. Two different models were developed. The first model had mean hardness and weld-bead geometry represented by four geometrical dimensions of the weld (that is, top width, depth, mid-width and heat-affected- zone width at mid-depth) as the modelling outputs. The second model had the hardness profile plot interpolation parameters as the modelling outputs. Two different designs of neural networks were used for process-based modelling, namely counter-propagation neural network (CPNN) and feed-forward back-propagation neural network (BPNN), and their prediction capabilities were compared. For the feed-forward neural network, a genetic algorithm was later applied to enhance the prediction accuracy by altering its topology. Back-propagation was implemented using 12 different training algorithms. Mean generalisation error was used to compare the modelling accuracy of the neural networks.
机译：对于给定的一组工艺参数，尝试了预测通过普通碳素钢（DC05）的激光焊接产生的焊缝几何形状及其横截面显微硬度分布。使用considering光纤激光器进行焊接，将激光功率，焊接速度和焦点与样品表面的距离作为输入参数。显微镜用于测量焊缝尺寸。进行微压痕以沿水平横截面测量相应的维氏硬度。开发了两种不同的模型。第一个模型的平均硬度和焊缝几何形状由焊缝的四个几何尺寸（即顶部宽度，深度，中间宽度和中间深度的热影响区宽度）表示，作为模型输出。第二个模型具有硬度分布图插值参数作为模型输出。神经网络的两种不同设计用于基于过程的建模，即反向传播神经网络（CPNN）和前馈反向传播神经网络（BPNN），并比较了它们的预测能力。对于前馈神经网络，后来应用遗传算法通过更改其拓扑来提高预测精度。使用12种不同的训练算法实现了反向传播。使用平均泛化误差来比较神经网络的建模精度。

著录项

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