Design optimization of polymer composites for lower suspension arms of automotive vehicles

Lower suspension arms deal with the movement of the wheel during knock, turning, and breaking. To replace the metallic suspension arms by laminated polymer composites, suspension arms are designed in this work. The thickness and the fiber orientations for different layers in the laminates are optimized to achieve improved performance. The suspension arms of fiber reinforced composite are designed and analyzed through finite element analysis (FEA) method. The simulated data generated from the FEA is used for developing metamodels by artificial neural network (ANN), which in turn is used as the objective functions or the optimization process using genetic algorithm (GA). The objective of the optimization process is to reduce the stress concentration through better stress distribution and thus increasing the service life. The design optimization problem is handled in multi-objective optimization mode and the evolved Pareto solutions lead to a guideline for developing automotive suspension arm using composites. The FEA based validation of the optimum solutions show variation of the result well within acceptable limit.