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

作     者：Bahrebar, Sajjad Homayoun, Sajad Ambat, Rajan 

作者机构：Tech Univ Denmark Dept Civil & Mech Engn Sect Mat & Surface Engn Ctr Elect Corros DK-2800 Lyngby Denmark Tech Univ Denmark Dept Appl Math & Comp Sci DK-2800 Lyngby Denmark 

出 版 物：《CORROSION SCIENCE》 (腐蚀科学)

年 卷 期：2022年第206卷

核心收录：

学科分类：080503[工学-材料加工工程] 0806[工学-冶金工程] 08[工学] 0805[工学-材料科学与工程（可授工学、理学学位）] 

基　　金：CELCORR/CreCon consortium Innovation Fund Denmark 

主　　题：Machine learning algorithm Classification Regression Predictive analytics PCB failure Leakage current 

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.