Supervised machine learning models for accurate prediction of CBGA solder joint lifespan DOI
J. Rebaï, A. Ghorbel, Nabih Féki

et al.

Mechanics of Advanced Materials and Structures, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 20

Published: March 18, 2025

This study introduces the first comparative analysis of multiple machine learning models for predicting CBGA solder joint lifespan under thermal cycling, a key factor in electronic reliability. Expanding dataset from 45 to 295 reveals that data augmentation significantly improves prediction accuracy. Basically, DS2 is well validated as it incorporates 11 experimental values boosting its robustness. Among tested models, Random Forest Regression (RFR) provides most accurate predictions expanded dataset. research positions and efficient, scalable alternatives traditional methods, offering faster more reliable approach component prediction.

Language: Английский

Supervised machine learning models for accurate prediction of CBGA solder joint lifespan DOI
J. Rebaï, A. Ghorbel, Nabih Féki

et al.

Mechanics of Advanced Materials and Structures, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 20

Published: March 18, 2025

This study introduces the first comparative analysis of multiple machine learning models for predicting CBGA solder joint lifespan under thermal cycling, a key factor in electronic reliability. Expanding dataset from 45 to 295 reveals that data augmentation significantly improves prediction accuracy. Basically, DS2 is well validated as it incorporates 11 experimental values boosting its robustness. Among tested models, Random Forest Regression (RFR) provides most accurate predictions expanded dataset. research positions and efficient, scalable alternatives traditional methods, offering faster more reliable approach component prediction.

Language: Английский

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