NAFEMS Americas and Digital Engineering (DE) teamed up (once again) to present CAASE, the (now Virtual) Conference on Advancing Analysis & Simulation in Engineering, on June 16-18, 2020!

CAASE20 brought together the leading visionaries, developers, and practitioners of CAE-related technologies in an open forum, unlike any other, to share experiences, discuss relevant trends, discover common themes, and explore future issues, including:
-What is the future for engineering analysis and simulation?
-Where will it lead us in the next decade?
-How can designers and engineers realize its full potential?
What are the business, technological, and human enablers that will take past successful developments to new levels in the next ten years?

Resource Abstract

Electrification of drive train and autonomous driving are increasing the number of electronic devices in cars and related PCB (Printed Circuit Boards). Due to the dynamic road excitation of such boards and other excitations any damages of the electronics should be avoided. To this end, an increase of the eigenfrequencies of PCB could help to overcome such vibration problems. Any other connected assembly, where bolts, rivets, spot welds, or supports to ground are subject to dynamic excitation, will be a candidate for using the same technology to maximize the eigenfrequencies.



One way to increase such eigenfrequencies is to use a sufficient number of bolts to fix the board. These bolts have to be positioned in an optimal way. These tasks lead to a multi-modal optimization, which simultaneously solves both a shape optimization problem to position the supports and a sizing optimization of the support stiffnesses to assess the need of single supports.



The modeling of each support consists of a spring to ground, which is connected to the PCB by an interpolation function. This allows the arbitrary positioning of a support and it is not necessary to connect supports to nodes of the PCB FE (Finite Element) mesh. In this way, the shape optimization moves the support to the optimal position. In addition, each spring stiffness is scaled by a contribution factor between zero and one, which is optimized by the sizing optimization. A contribution factor close to zero is indicating a support, which can be neglected. If no contribution factor is close to zero, then it is likely that additional supports will give additional opportunities to raise the eigenfrequencies. So, the initially selected number of supports should be on the safe side.



The paper explains how the modeling of PCB supports is done and how optimization task is set up. An industrial example is used to demonstrate the process and to evaluate the results. The analysis and the multi-modal optimization is done in one single job using the industrial software PERMAS.