[1]范子铭,田富君,胡子翔,等.基于机器学习的回流焊焊点形貌预测[J].机械与电子,2023,41(01):53-58.
　FAN Ziming,TIAN Fujun,HU Zixiang,et al.Topography Prediction of Reflow Solder Joints Based on Machine Learning[J].Machinery & Electronics,2023,41(01):53-58.
点击复制

基于机器学习的回流焊焊点形貌预测()
分享到：

《机械与电子》[ISSN:1001-2257/CN:52-1052/TH]

卷:
41
期数:
2023年01期
页码:
53-58
栏目:
智能工程
出版日期:
2023-01-25

文章信息/Info

Title:
Topography Prediction of Reflow Solder Joints Based on Machine Learning
文章编号:
1001-2257 ( 2023 ) 01-0053-06
作者:
范子铭 1 ; 田富君 2 ; 胡子翔 2 ; 魏 李 2
1. 中国电子科技集团公司第三十八研究所,安徽 合肥 230088 ;
2. 国家级工业设计中心(中电 38 所),安徽 合肥 230088
Author(s):
FAN Ziming1 ;  TIAN Fujun2 ;  HU Zixiang2 ;  WEI Li2
( 1.No.38 Research Institute of CETC , Hefei 230088 , China ; 2.National Industrial Design Center ( CETC38 ), Hefei 230088 , China
关键词:
机器学习; 回流焊; 焊点形貌;  BP 神经网络;  LightGBM 算法; 数据挖掘
Keywords:
machine learning ;  reflow soldering ;  solder joint topography ;  BP neural network ;  LightGBM algorithm ;  datamining
分类号:
TP181 ; TG407
文献标志码:
A
摘要:
提出一种基于机器学习预测回流焊焊点形貌的方法,通过该方法建立一个针对钽电容回流焊焊点形貌的预测模型,该模型为现有实验方式提供了新的思路。通过峰值温度、降温速率和焊膏厚度 3 种影响因素以及焊点厚度、焊点宽度和焊料爬高 3 种评价焊点形貌的评价标准,分别基于 BPNN 和 LightGBM算法建立钽电容回流焊焊点形貌预测模型。对比实验证明,通过 LightGBM 算法建立的预测模型优于通过 BPNN 建立的预测模型,并通过实际测试帮助实验人员减少实验次数,节约大量时间成本。
Abstract:
A method for predicting the topography of reflow solder joints based on machine learning is proposed.Through this method , a prediction model for the topography of reflow solder joints of tantalum capacitors is established , which provides a new idea for the existing experimental methods.Based on the three influencing factors of peak temperature , cooling rate and solder paste thickness , and three evaluation criteria for evaluating solder joint morphology , solder joint thickness , solder joint width and solder climb , the reflow soldering of tantalum capacitors was established based on BPNN and LightGBM algorithms respectively.For the point topography prediction model , the comparison experiment proves that the prediction model established by the LightGBM algorithm is better than the prediction model established by the BPNN , and helps the experimenter to reduce the number of experiments and save a lot of time and cost through actual testing.

参考文献/References:

[ 1 ] WANG B , YANG X X , ZHANG Y , et al.Experimental study on the effect of reflow soldering temperature profile on the solder joint shape [ C ] ∥2010 11th International Conference on Electronic Packaging Technology and High Density Packaging , 2010 : 1128-1131.

[ 2 ] 赵亚楠,彭丽 . 电触头电阻钎焊温度场模拟[ J ] . 机械与电子,2013 ( 9 ): 77-80.
[ 3 ] JIN X , WANG W , ZHAO W , et al.Reflow soldering process optimization based on surface evolver solder joint shape simulation and finite element analysis of PCB assembly [ C ]// 2021 22nd International Conference on Electronic Packaging Technology( ICEPT ),2021 : 1-4.
[ 4 ] 王星 . 多种器件焊点形态预测及复杂印制板组件振动可靠性分析[ D ] . 西安:西安电子科技大学,2021.
[ 5 ] PAN K , HA J H , PHAM V L , et al.The effect of solder paste volume on solder joint shape and self-alignment of passive components [ C ] ∥2020 IEEE 70th Electronic Components and Technology Conference( ECTC ),2020 : 1289-1297.
[ 6 ] 陶汪,李俐群,陈彦宾,等 . 基于人工神经网络的激光点焊焊点形态预测[ J ] . 机械工程学报, 2009 , 45 ( 11 ): 300-305.
[ 7 ] GAO S , ZHANG H , MI H.Solder joint defect detection based on image segmentation and deep learning [ C ] ∥2019 IEEE International Conference on Signal , Information and Data Processing( ICSIDP ), 2019 : 1-6.
[ 8 ] 胡海涛 . 基于 BP 神经网络和遗传算法的并行迭代优化研究[ J ] . 机械与电子, 2019 , 37 ( 1 ): 26-32.
[ 9 ] ALZAMZAMI F , HODA M , SADDIK A E.Light gradient boosting machine for general sentiment classification on short texts : a comparative evaluation [ J ] .IEEE Access , 2020 ( 8 ): 101840-101858.
[ 10 ] KHOCHARE J , RATHOD J , JOSHI C , et al.A short term wind forecasting framework using ensemble learning for indian weather stations [ C ] ∥2020 IEEE International Conference for Innovation in Technology( INOCON ), 2020 : 1-7.
[ 11 ] 张旭东,钱仲文,沈思琪,等 . 一种基于 LSTM 与 LGBM 的电力负荷预测模型 [ J ] . 系统工程, 2019 , 37( 1 ): 152-158.

相似文献/References:

[1]李鸿奎,程昭龙,周 蕾,等.智能变电站设备管控大数据分析系统研究[J].机械与电子,2020,(10):72.
　LI Hongkui,CHENG Zhaolong,ZHOU Lei,et al.Big Data Analysis System of Intelligent Substation Equipment Management and Control[J].Machinery & Electronics,2020,(01):72.
[2]周岳斌,杨 沫,曹煜晖.无线视频监控系统人脸跟踪功能的实现[J].机械与电子,2021,(06):35.
　ZHOU Yuebin,YANG Mo,CAO Yuhui.Realization of Face Tracking Function in Wireless Video Surveillance System[J].Machinery & Electronics,2021,(01):35.
[3]原 军,张 凯,药 炜,等.基于机器学习的分组交换电力光网络流量异常检测[J].机械与电子,2021,(09):47.
　YUAN Jun,ZHANG Kai,YAO Wei,et al.Anomaly Detection of Packet Switching Power Optical Network[J].Machinery & Electronics,2021,(01):47.
[4]刘 哲,闫佳文,陈长金,等.电力线图像检测方法研究[J].机械与电子,2022,(03):54.
　LIU Zhe,YAN Jiawen,CHEN Changjin,et al.Research on Power Line Image Detection Method[J].Machinery & Electronics,2022,(01):54.
[5]胡子翔,何 旭,朱凯鹏.基于 Boosting-MKELM 的回流焊过程质量预测研究[J].机械与电子,2023,41(05):12.
　HU Zixiang,HE Xu,et al.Quality Prediction Research of Reflow Soldering Process Based on Boosting-MKELM[J].Machinery & Electronics,2023,41(01):12.
[6]宾世杨,张 振,唐俊杰,等.基于机器学习的风电机组机械传动系统故障诊断研究[J].机械与电子,2024,42(01):11.
　BIN Shiyang,ZHANG Zhen,TANG Junjie,et al.Research on Fault Diagnosis of Wind Turbine Mechanical Transmission System Based on Machine Learning[J].Machinery & Electronics,2024,42(01):11.
[7]袁武民,邢建平,杨 栋.基于改进 Stacking 模型的铁路信号设备故障率预测[J].机械与电子,2024,42(01):41.
　YUAN Wumin,XING Jianping,YANG Dong.Prediction of Railway Signal Equipment Failure Rate Based on Improved Stacking Model[J].Machinery & Electronics,2024,42(01):41.
[8]郭文升,李建平.基于机器学习的煤矿输送带 RFID 健康监测方法[J].机械与电子,2025,(04):64.
　GUO Wensheng,LI Jianping.Machine Learning Based RFID Enabled Health Monitoring of Coal Mine Conveyor Belt[J].Machinery & Electronics,2025,(01):64.

备注/Memo

备注/Memo:
收稿日期: 2022-06-10
基金项目:国防基础科研项目( JCKY2020210B007 )
作者简介:范子铭 ( 1996- ),男,安徽合肥人,助理工程师,研究方向为大数据分析与数据挖掘。
更新日期/Last Update: 2023-03-01