In this study, the methodology of the springback prediction of automotive parts applied 3rd generation AHSS was investigated using the response surface model analysis based on a regression model, and the meta model analysis based on a Kriging model. To design the learning data set for constructing the springback prediction models, and the experimental design was conducted at three levels for each processing variable using the definitive screening designs method. The hat-shaped member, which is the basic shape of the member parts, was selected and the springback values were measured for each processing type and processing variable using the finite element analysis. When the nonlinearity of the variables is small during the hat-shaped member forming, the response surface model and the meta model can provide the same processing parameter. However, the accuracy of the springback prediction of the meta model is better than the response surface model. Even in the case of the simple shape parts forming, the springback prediction accuracy of the meta model is better than that of the response surface model, when more variables are considered and the nonlinearity effect of the variables is large. The efficient global optimization algorithm-based Kriging is appropriate in resolving the high computational complexity optimization problems such as developing automotive parts.

The purpose of this study is to investigate the effect of sintering condition on the microstructure evolution and tensile properties of the Ti-6Al-4V alloy sample processed by metal injection molding (MIM) in terms of the sizes of the alpha morphology and pore found in the matrix. For this purpose, a series of MIM were conducted on this sample at various sintering temperatures ranging from 1173 to 1373 K for three hours followed by furnace cooling, observed by the scanning electron microscopy. The microstructures sintered in this study showed that, with increasing sintering temperature over beta transus temperature, the transformation of the equiaxed alpha into transformed beta was attained while the size of pores would tend to decrease. Thus, the strength remained unchanged significantly in the tension while ductility increased to some extent as sintering temperature increased. Such mechanical behavior would be explained in relation to the microstructure evolution of the Ti-6Al-4V sample via the MIM.

This paper is concerned with the influence of the plastic property of the rivet on the numerical prediction of the Self-Piercing Rivet (SPR) Joining. In order to predict the plastic property of the rivet, a ring compression specimen was directly fabricated from the rivet used for the mechanical joining of dissimilar materials, and the FE analysis together with the ring compression test was iteratively carried out by changing the plastic property of the rivet. For reliable FE analysis, a friction coefficient was estimated based on a friction calibration curve, measuring the reductions in inner diameter and height of the ring specimen after the compression test. From each simulation result, the force-displacement curves were then compared from each other so as to obtain the rivet plastic property that shows good agreement with the experimental result. The SPR joining between GA590 1.0t and Al5052 2.0t was conducted, and the numerical prediction was performed with the use of the plastic property evaluated based on the inverse analysis and the one referred from Mori et al. [11]. Comparison of the experiment and the numerical predictions in terms of the interlock and bottom thickness revealed that the reliable evaluation of the plastic property of the rivet is necessary for the trustworthy numerical prediction of the SPR joining.

The purpose of this paper is to analyze the numerical simulation accuracy according to the CFRP modeling method in the CFRP-Al alloy SPR (Self-Piercing Rivet) joint process. The mechanical properties of the CFRP, aluminum sheet are precisely obtained from the tensile test according to the loading direction. Additionally, the hardening curve of rivet was calculated from the inverse analysis of the machined rivet-ring compression test. For the CFRP-Al alloy SPR simulation, two kinds of the CFRP modeling methods were established based on the continuum and layer-by-layer approaches. The simulation results showed that the CFRP layer-by-layer modeling method can provide more reliable prediction shape of the fractured sheets and deformed rivet. This simulation technique can be used in evaluating the CFRP-Metal SPR performance and designing the SPR process conditions.

As the thickness of mobile communication devices is getting thinner, the size of the internal parts is also getting smaller. Among them, the battery case requires a high-level deep drawing technique because it has a rectangular shape with a large aspect ratio. In this study, the initial blank shape was optimized to minimize earing in a multi-stage deep drawing process using an artificial neural network(ANN). There has been no reported case of applying artificial neural network technology to the initial blank optimal design for a square case with large aspect ratio. The training data for ANN were obtained though simulation, and the model reliability was verified by performing comparative study with regression model using random sample test and goodness-of-fit test. Finally, the optimal design of the initial blank shape was performed through the verified ANN model.

Flexible roll forming is an advanced sheet-metal-forming process that allows the production of parts with various cross-sections. During the flexible process, material is subjected to three-dimensional deformation such as transverse bending, inhomogeneous elongations, or contraction. Because of the effects of process variables on the quality of the roll-formed products, the approaches used to investigate the roll-forming process have been largely dependent on experience and trial- and-error methods. Web-warping is one of the major shape defects encountered in flexible roll forming. In this study, an SVR model was developed to predict the web-warping during the flexible roll forming process. In the development of the SVR model, three process parameters, namely the forming-roll speed condition, leveling-roll height, and bend angle were considered as the model inputs, and the web-warping height was used as the response variable for three blank shapes; rectangular, concave, and convex shape. MATLAB software was used to train the SVR model and optimize three hyperparameters (λ, ε, and γ). To evaluate the SVR model performance, the statistical analysis was carried out based on the three indicators: the root-mean-square error, mean absolute error, and relative root-mean-square error.