Using machine learning algorithms to predict failure on the PCB surface under corrosive conditions

被引：5
|
作者：
Bahrebar, Sajjad [1 ]
Homayoun, Sajad [2 ]
Ambat, Rajan [1 ]
机构：
[1] Tech Univ Denmark, Dept Civil & Mech Engn, Sect Mat & Surface Engn, Ctr Elect Corros, DK-2800 Lyngby, Denmark
[2] Tech Univ Denmark, Dept Appl Math & Comp Sci, DK-2800 Lyngby, Denmark
来源：
CORROSION SCIENCE | 2022年 / 206卷
关键词：
Machine learning algorithm; Classification; Regression; Predictive analytics; PCB failure; Leakage current; HUMIDITY-RELATED FAILURES; WEAK ORGANIC-ACIDS; SOLDER FLUX CHEMISTRY; RELIABILITY; ELECTRONICS; TEMPERATURE; ROC;
D O I：
10.1016/j.corsci.2022.110500
中图分类号：
T [工业技术];
学科分类号：
08 ;
摘要：
A printed circuit board (PCB) surface can fail by corrosion due to various environmental factors. This paper focuses on machine learning (ML) techniques to build predictive models to forecast PCB surface failure due to electrochemical migration (ECM) and leakage current (LC) levels under corrosive conditions containing the combination of six critical factors. The modeling methodology in this paper used common supervised ML algorithms by accomplishing significant evaluation metrics to show the performance of each algorithm. The conclusion of this study presents that ML algorithms can create predictive models to forecast PCB failures and estimate LC values effectively and quickly.
引用
收藏
页数：21
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