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- W4285603581 endingPage "110500" @default.
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- W4285603581 abstract "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." @default.
- W4285603581 created "2022-07-16" @default.
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- W4285603581 date "2022-09-01" @default.
- W4285603581 modified "2023-09-29" @default.
- W4285603581 title "Using machine learning algorithms to predict failure on the PCB surface under corrosive conditions" @default.
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- W4285603581 doi "https://doi.org/10.1016/j.corsci.2022.110500" @default.
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