Existing composite forming processes such as the autoclave, prepreg compression forming (PCF), RTM, etc. require high production costs because of their long processing time. On the other hand, microwave heating process (MHP) can reduce the production costs since both mold and composite material can be heated directly. The aim of this study is to manufacture a mold consisting of polytetrafluoroethylene (PTFE), quartz glass, stainless steel clamps, and polyether ether ketone (PEEK) bolts for fabricating FML based on self-reinforced polypropylene (SRPP) using the MHP. First, the flame test was carried out prior to the MHP to check the temperature on the mold and whether the spark occurred at the mold and the edge of the FML. Second, the uniaxial tensile test was then conducted to obtain the mechanical properties of the FML manufactured by the MHP. The mechanical properties were compared with those of the FML fabricated by the PCF. As a result, the MHP using the PTFE mold can manufacture the FML more rapidly than the PCF, and obtain acceptable mechanical properties.

Piping work of large ships or offshore plants is often done in a narrow and confined space, requiring precise bending and safety. In order to realize an accurate bending angle, it is very important to predict and correct a deformation that may be caused by elasticity in the bending process, that is, an angular deviation due to springback. Therefore, by using CAE analysis to develop a correction angle model for springback based on multiple tube bending angles and using trend line data derived from this correction angle model, at bending the tube as the diameter of the base former and the tube outer diameter change, the springback compensation angle at any angle can be obtained. In this study, the bending mechanism was analyzed to increase the bending precision, and a correction angle model was developed and a trend line was derived in consideration of springback occurring in the bending process. In order to derive a more accurate and reliable trend line, a tube tensile test was performed, and the reliability of the corrected angle trend line was verified by comparing the bending angle measurement and analysis results with a 3D scanner.

In this paper, experimental and numerical study on a combined sheet metal forming and plate forging of a seal part of a passenger car's hub bearing is conducted to develop the new process of which target is to remove machining process by plate forging and to achieve near-net shape manufacturing. The previous process of a sheet metal forming inevitably needed a machining process for making stepped sheet after conventional sheet metal forming in a progressive way. The stepped sheet is intended to be formed by plate forging in this study. Through the systematic way of developing the combined forming process using solid elements based-elastoplastic finite element method (FEM), several conceptual designs are made and an optimized process design in terms of geometric dimensioning and tolerance of straightness of the thin part is found, which is exposed to bending in metal forming of axisymmetric part. The predicted straightness measured by the slope angle of the tilted thin region is compared with the experimental straightness, showing that they are in a good agreement with each other. Through this study, a systematic approach to optimal process design, based on elastoplastic FEM with solid elements, is established, which will contribute to innovating the conventional small-scaled sheet metal forming processes which can be dealt with by solid elements.

In this study, formability and springback behavior of 1.5 GPa grade ultra-high strength steel (UHSS) sheet were predicted through the finite element simulation, and structural stability of the forming dies was verified by the coupled forming-structural analysis. Uniaxial tension and uniaxial tension-compression tests were performed to obtain experimental data for modeling the springback properties of the sheet material. The springback values predicted by simulation were compared with those from actual measurements. The results calculated from the kinematic hardening model were found to be much more accurate than those from the isotropic hardening model. Deformation of the forming die and springback of the product were calculated by the coupled forming-structural analysis. The higher the strength of the die material, the smaller the surface displacement of the die and the springback of the product. The internal stresses of the dies made of three materials, FC300, FCD550 and STD11 were compared with the yield stress of each material. The results provided a basis for determining the most suitable material for each part of the die set. As a result, simulation techniques have been established for predicting formability and springback in the UHSS sheet forming process.

This paper presents a study on the hydrogen embrittlement of Ti-6Al-4V alloys with different microstructures depending on annealing treatment. They were electrochemically charged with hydrogen and subjected to tensile tests to investigate hydrogen embrittlement behavior. Tensile test results showed that the elongation of Ti-6Al-4V alloy specimens was remarkably decreased with increasing the volume fraction of β phase after hydrogen charging. This is because the β phase with a relatively low diffusivity tends to easily form a hydride at grain boundaries during electrochemical hydrogen charging. After hydrogen charging of the Ti-6Al-4V alloy specimen, it found that silver particles were decorated mostly at the grain boundary, and coarser silver particles were usually formed in the specimen annealed at 950 ℃. Therefore, the specimen having higher β phase fraction shows a poor hydrogen embrittlement resistance because the β phase promotes the formation of coarse hydride during electrochemical hydrogen charging, which leads to a large decrease in ductility.

The prediction of final mass and optimized process conditions of injection molded products using Artificial Neural Network (ANN) were demonstrated. The ANN was modeled with 10 input parameters and one output parameter (mass). The input parameters, i.e.; melt temperature, mold temperature, injection speed, packing pressure, packing time, cooling time, back pressure, plastification speed, V/P switchover, and suck back were selected. To generate training data for the ANN model, 77 experiments based on the combination of orthogonal sampling and random sampling were performed. The collected training data were normalized to eliminate scale differences between factors to improve the prediction performance of the ANN model. Grid search and random search method were used to find the optimized hyper-parameter of the ANN model. After the training of ANN model, optimized process conditions that satisfied the target mass of 41.14 g were predicted. The predicted process conditions were verified through actual injection molding experiments. Through the verification, it was found that the average deviation in the optimized conditions was 0.15±0.07 g. This value confirms that our proposed procedure can successfully predict the optimized process conditions for the target mass of injection molded products.