Machine learning aided modelling of thermomechanical fatigue of solder joints in electronic component assemblies
(2022) International Journal of Fatigue — Vol. 167, p. 107298 (2022)
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Details
- Authors
Van Loock, Frederik 
- Abstract
- Printed board assemblies, i.e. components soldered on printed circuit boards (PCBs), are exposed to thermal cycles responsible for fatigue cracking of solder joints as a result of thermal expansion mismatch between the constituting elements. Advanced finite element simulations are performed using a traction-separation law to represent the cracking process and a temperature-dependent elasto-viscoplastic model for the joint response. Predictions are successfully assessed towards machine learning processed experimental data. In particular, the high sensitivity of thermal ageing reliability to geometric dimensions and solder joint thickness is properly captured. Additional parameters, related to the PCB substrate, are also studied, opening new avenues towards design optimization.
- Affiliations
Thales Alenia Space Belgium
Citations
Voet, V., Van Loock, F., De Fruytier, C., Simar, A., & Pardoen, T. (2022). Machine learning aided modelling of thermomechanical fatigue of solder joints in electronic component assemblies. International Journal of Fatigue, 167, 107298. https://doi.org/10.1016/j.ijfatigue.2022.107298 (Original work published 2022)