• 综合
  • 标题
  • 关键词
  • 摘要
  • 学者
  • 期刊-刊名
  • 期刊-ISSN
  • 会议名称
搜索

作者:

Dai, Yanwei (Dai, Yanwei.) (学者:代岩伟) | Wei, Jiahui (Wei, Jiahui.) | Qin, Fei (Qin, Fei.)

收录:

EI Scopus SCIE

摘要:

In this paper, we presented three neural network models including deep neural network (DNN), recurrent neural network (RNN), and long short-term memory neural network (LSTM), which are proposed to predict the cohesive zone parameter of sintered silver DCB joint with different contents of nickel modified carbon nanotube, thus reducing the complexity of CZM parameter acquisition for nanoparticle reinforced adhesive. The bilinear CZM model is used as the prediction target model for sintered silver joints with different contents of nickel -modified carbon nanotube filler, and data sets suitable for different networks are established through experimental and numerical simulation results. Three kinds of networks are trained based on the optimized hyperparameters obtained from the Bayesian hyperparameters tuning process. The results show that DNN, RNN, and LSTM frameworks can all predict CZM parameters of nanoparticle-reinforced sintered silver adhesive through loaddisplacement curves. Based on loss analysis and statistical indicator comparison after K -fold cross -validation, the RNN and LSTM models have better prediction accuracy and performance than the DNN model, and the accuracy of the LSTM model is further improved compared with the DNN model. RNN and LSTM models have high prediction accuracy and stronger recognition ability for the time series data, they can be used as suitable alternative models for inverse recognition of CZM parameters of nanoparticle-reinforced adhesives, and have broad application prospects.

关键词:

Long short-term memory neural network (LSTM) Recurrent neural network (RNN) Cohesive zone model Data-driven Deep neural network

作者机构:

  • [ 1 ] [Dai, Yanwei]Beijing Univ Technol, Inst Elect Packaging Technol & Reliabil, Dept Mech, Beijing 100124, Peoples R China
  • [ 2 ] [Wei, Jiahui]Beijing Univ Technol, Inst Elect Packaging Technol & Reliabil, Dept Mech, Beijing 100124, Peoples R China
  • [ 3 ] [Qin, Fei]Beijing Univ Technol, Inst Elect Packaging Technol & Reliabil, Dept Mech, Beijing 100124, Peoples R China

通讯作者信息:

  • [Dai, Yanwei]Beijing Univ Technol, Inst Elect Packaging Technol & Reliabil, Dept Mech, Beijing 100124, Peoples R China;;[Qin, Fei]Beijing Univ Technol, Inst Elect Packaging Technol & Reliabil, Dept Mech, Beijing 100124, Peoples R China;;

查看成果更多字段

相关关键词:

相关文章:

来源 :

MATERIALS TODAY COMMUNICATIONS

年份: 2024

卷: 39

3 . 8 0 0

JCR@2022

被引次数:

WoS核心集被引频次:

SCOPUS被引频次: 10

ESI高被引论文在榜: 0 展开所有

万方被引频次:

中文被引频次:

近30日浏览量: 0

归属院系:

在线人数/总访问数:461/4877861
地址:北京工业大学图书馆(北京市朝阳区平乐园100号 邮编:100124) 联系我们:010-67392185
版权所有:北京工业大学图书馆 站点建设与维护:北京爱琴海乐之技术有限公司