No abstract

No abstract

The success of sheet hydroforming process largely depends on the loading pressure path. Pressure path is one of the most important parameters in sheet hydroforming process. In this study, a combination of finite element simulation, artificial intelligence and simulated annealing optimization have been utilized to optimize the pressure path in producing cylindrical-spherical parts. In the beginning, the finite element model was verified based on laboratory experimental results. The experiments were designed and a radial basis neural network model was developed using data generated from verified finite element model to predict the thickness in the critical region of the product. Results indicated that the neural network model could be applied successfully to predict the sheet thickness in the critical region. In addition, the neural network model was used as a fitness function in simulated annealing algorithm to minimize the thickening in the above mentioned critical region. The final results showed that utilization of the optimized pressure path yields good thickness distribution of the part.

No abstract