بهینه سازی ابزار های محکم کردن با استفاده از مدل سازی یکپارچه FE و تکنیک های هوش مصنوعی

In the present work, an optimization of the clinching tools involving extensible dies is performed to increase the clinched joints strength. The clinched joint strength is influenced by the lock parameters, which in turn depend on the clinching tool geomet ry. A finite element model is developed to predict the effect of the clinching tool geometry on lock parameters and recursively optimize the tool geometry. In order to reduce the number of FE simulation runs, an artificial Neural Network (ANN) model is utilized to predict the behavior of clinched joints produced with a given clinching tools configuration. The ANN is trained and validated by us- ing the results of the finite element model produced under different clinching tools configurations. Finally, an optimization tool based on a Genetic Algorithm tool was developed to demonstrate the effectiveness of the proposed approach.