Strength and manufacturability enhancement of a composite automotive component via an integrated finite element/artificial neural network multi-objective optimization approach

This study addresses the enhancement of an injection-molded fiber-reinforced plastic / metal hybrid automotive structure and its plastic injection molding process through the integration of the finite element method, artificial intelligence and evolutionary search methods. Experiments are conducted to validate the finite element models. The orthogonal array and Latin hypercube methods are employed to generate a database via finite element analysis. The database is then used to train artificial neural networks that accurately evaluate component distortion, manufacturing time, and structural strength. A genetic optimization algorithm is applied to identify optimal process parameters. The procedure was demonstrated to simultaneously reduce product warpage and manufacturing time by 10 and 62 %, respectively, when compared with the reference manufacturing process while strength is kept above the required levels with a reduced number of required data points. A more in-depth investigation into the causes of strength variation and deformation is also provided. The results contribute to the advance of robust composite automotive structures with superior quality, manufactured through efficient processes.