The subtle feature is one of the characteristic lines and represents the most noticeable line in the automotive panel. In this study, we proposed a method to predict the radius of curvature of products according to the material, its thickness, its punch angle, and its punch radius. The radius of curvature was divided into three regions, namely, the non-linear, transition, and linear regions. In the non-linear region, the prediction model for the radius of curvature with different forming conditions was derived using the finite element analysis. In the linear region, the radius of curvature was assumed to be the sum of the punch radius and the thickness of the material. In the transition region, a model connecting two regions (Non-linear and linear region) was developed based on the continuity condition. The prediction model presented a very small RMSE with the value of 0.314 mm. Using the prediction model, the radius of curvature with various forming variables could be predicted and the required radius of punch, to obtain a certain value of the radius of curvature, could be precisely predicted.

Incremental sheet metal forming can be used to manufacture various products without the punch and die set. However, it is difficult to manufacture the desired shape due to section deflection and springback of the sheet. Section deflection is caused by the force of the blank holder for fixing the sheet and the tool for forming the sheet. In this study, we analyzed the characteristics of the section deflection according to the geometries of the circular cup shapes in the sheet incremental forming process. The section deflection increased with an increase in the entering radius and forming angle in the section deflection region. However, section deflection was constant according to the exit radius. In addition, the secondary forming process for reducing the shape error was introduced. The secondary incremental forming process was conducted in the opposite direction. Characteristics of the shape error according to the entering depth of the tool among the forming parameters for reducing the shape error of the cup shape were analyzed. The springback in the cup-shape was reduced by the additional forming process with an optimum entering depth of the tool.

When the penetrator collides with the target, the penetrator has different penetrating characteristics and residual velocity after penetration, according to the geometry of the penetrator. In this study, we optimized the geometry of the penetrator using the artificial neural network and the genetic algorithm to derive the best penetration performance. The Latin hypercube sampling method was used to collect the sample data, Simulation for predicting the behavior of the penetrator was conducted with the finite cavity pressure method to generate the training data for the artificial neural network. Also, the optimal hyper parameter was derived by using the Latin hypercube sampling method and the artificial neural network was used as the fitness function of the genetic algorithm to optimize the geometry of the penetrator. The optimized geometry presented the deepest penetration depth.