In this study, acoustic emission (AE) signals associated with the behavior of materials in the magnesium alloy (Mg AZ31B) tensile test were analyzed. The AE sensor was attached with the material to measure the AE signals. During the tensile experiment, the AE sensor measured the elastic waves generated inside the specimen. The AE parameters, such as, the signal energy, duration, and frequency centroid, were studied. We also analyzed the effect of the materials size and tensile speed on the AE signals. As a result, the lowest frequency centroid value occurred at the yield and fracture points. As the width and length of the specimen increased, the number of hit counts increased and the peak frequency occurred. Other AE parameters, such as, the duration and frequency centroid, were not affected. As the tensile speed increased, the hit decreased and the frequency centroid decreased in the elastic region. It was found that in the detection of the yield and fracture deformation, the number of counts, and frequency centroid were appropriate.

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.

Sheet metal-forming processes such as stamping, deep drawing, bending, shearing, hydroforming, hydromechanical deep drawing, rubber forming, and incremental forming have been widely used in the automotive, aircraft, and ship-building industries. With the expansion of the automotive industry, research on these processes has been remarkably developed in Korea since the 1980s. Here, we review the history of this research as well as recent trends in sheet metal-forming processes. This overview focuses specifically on the results of research in Korea and on the works of Professor D.Y. Yang, in honor of his retirement.