Integrating ANSYS Simulation and Machine Learning Techniques for Thermo-Mechanical Analysis of PCBs

Printed circuit boards (PCBs) find widespread use in various electronic applications, including computers. PCBs often undergo thermal strains, which can result in the generation of mechanical stresses without any concerned load. To optimize PCB designs effectively, it's essential to integrate both thermal and mechanical analyses. In this study, the focus is on PCBs constructed from composite materials subjected to different environmental temperature changes. The aim is to identify how changes in environmental temperature affect the factors such as overall temperature in turn these thermal strains generate the mechanical stresses such as Von Mises stresses, normal stresses, shear stresses, and elastic strains in the PCB. Furthermore, machine learning models are employed to extend the understanding and make predictions based on the collected data. This approach allows gaining insights into the behavior of composite PCBs under different conditions and aids in optimizing their design for enhanced performance and reliability.