A Two-Stage Optimization Method for the Stencil Printing Process based on Neural Network and Response Surface Method

被引：3
|
作者：
Pan, Ershun [1 ]
Jin, Yao [1 ]
Zhao, Mei [1 ]
Wang, Ying [1 ]
机构：
[1] Shanghai Jiao Tong Univ, Sch Mech Engn, Shanghai 200240, Peoples R China
来源：
ADVANCED MANUFACTURING TECHNOLOGY, PTS 1, 2 | 2011年 / 156-157卷
关键词：
Surface Mount Technology (SMT); Neural Network; Response Surface Method; Interactive Method; Optimization;
D O I：
10.4028/www.scientific.net/AMR.156-157.10
中图分类号：
T [工业技术];
学科分类号：
08 ;
摘要：
A stencil printing process (SPP) optimization problem is studied in this paper. Due to the limitation that neural network requires a large number of samples for the accurate model fitting, a two-stage SPP optimization method is proposed. The design interval can be reduced with small sample by using neural network. In this reduced design interval, response surface method is adopted to obtain the accurate mathematical SPP model. The concept of confidence level is introduced to make the proposed model robust. An interactive method is used to solve the model. The proposed method is compared with the one-stage optimization method and the results show that the proposed method achieves a better performance on each objective.
引用
收藏
页码：10 / 17
页数：8
相关论文
共 50 条
  • [1] Two-stage object tracking method based on Siamese neural network
    Zhang, Hongwei
    Li, Xiaoxia
    Zhu, Bin
    Zhang, Yang
    [J]. Hongwai yu Jiguang Gongcheng/Infrared and Laser Engineering, 2021, 50 (09):
  • [2] A New Geomagnetic Vector Navigation Method Based on a Two-Stage Neural Network
    Chen, Zhuo
    Liu, Zhongyan
    Zhang, Qi
    Chen, Dixiang
    Pan, Mengchun
    Xu, Yujing
    [J]. ELECTRONICS, 2023, 12 (09)
  • [3] A Two-Stage Missing Value Imputation Method Based on Autoencoder Neural Network
    Yu, Jiayin
    He, Yulin
    Huang, Joshua Zhexue
    [J]. 2021 IEEE INTERNATIONAL CONFERENCE ON BIG DATA (BIG DATA), 2021, : 6064 - 6066
  • [4] A Two-Stage Simulation Optimization Method Based on Metamodel
    Liu, Zhizhao
    Li, Wei
    Yang, Ming
    [J]. THEORY, METHODOLOGY, TOOLS AND APPLICATIONS FOR MODELING AND SIMULATION OF COMPLEX SYSTEMS, PT IV, 2016, 646 : 82 - 91
  • [5] A Two-Stage Pose Estimation Method for Noncooperative Targets Based on Convolution Neural Network
    Su, Di
    Zhang, Cheng
    Wang, Ke
    Sun, Kai
    [J]. Beijing Ligong Daxue Xuebao/Transaction of Beijing Institute of Technology, 2023, 43 (07): : 734 - 743
  • [6] Two-stage neural network control method for space soft manipulator
    Cui, Chao-Chen
    Zhang, Xiang
    Xiong, Dan
    Han, Wei
    Huang, Yi-Yong
    [J]. Kongzhi Lilun Yu Yingyong/Control Theory and Applications, 2023, 40 (12): : 2257 - 2264
  • [7] A Two-stage Neural-network Based Method for Cycle Slip Correction of GPS Measurements
    Yi, Tinghua
    Li, Hongnan
    Gu, Ming
    [J]. HEALTH MONITORING OF STRUCTURAL AND BIOLOGICAL SYSTEMS 2009, 2009, 7295
  • [8] Comprehensive voltage control optimization in active distribution network based on two-stage planning method
    Tao, Shun
    Luo, Chen
    Xue, Jiajia
    Xiao, Xiangning
    Chen, Gang
    [J]. Dianli Xitong Zidonghua/Automation of Electric Power Systems, 2015, 39 (20): : 54 - 59
  • [9] A Two-Stage Learning Method for Response Prediction
    Chen, Kuan-Hsi
    Ting, Zih-Yun
    Shen, Jia-Ying
    Hu, Yuh-Jyh
    Liang, Tyne
    [J]. 2015 IEEE INTERNATIONAL CONFERENCE ON DATA MINING WORKSHOP (ICDMW), 2015, : 1336 - 1341
  • [10] A Two-Stage Exact Algorithm for Optimization of Neural Network Ensemble
    Wang, Keliang
    Lozano, Leonardo
    Bergman, David
    Cardonha, Carlos
    [J]. INTEGRATION OF CONSTRAINT PROGRAMMING, ARTIFICIAL INTELLIGENCE, AND OPERATIONS RESEARCH, 2021, 12735 : 106 - 114
12345