• 소성∙가공
• /
• 제24권1호
• /
• pp.52-61
• /
• 2015

Evaluation of the elevated temperature flow stress for thermoplastic fiber metal laminates(TFMLs) sheet, comprised of two aluminum sheets in the exterior layers and a self-reinforced polypropylene(SRPP) in the interior layer, was conducted. The flow stress as a function of temperature should be evaluated prior to the actual forming of these materials. The flow stress can be obtained experimentally by uniaxial tensile tests or analytically by deriving a flow stress model. However, the flow stress curve of TFMLs cannot be predicted properly by existing flow stress models because the deformation with temperature of these types of materials is different from that of a generic pure metallic material. Therefore, the flow stress model, which includes the effect of the temperature, should be carefully identified. In the current study, the flow stress of TFMLs were first predicted by using existing flow stress models such as Hollomon, Ludwik, and Johnson-Cook models. It is noted that these existing models could not effectively predict the flow stress. Flow stress models such as the modified Hollomon and modified Ludwik model were proposed with respect to temperatures of $23^{\circ}C$, $60^{\circ}C$, $90^{\circ}C$, $120^{\circ}C$. Then the stress-strain curves, which were predicted using the proposed flow stress models, were compared to the stress-strain curves obtained from experiments. It is confirmed that the proposed flow stress models can predict properly the temperature dependent flow stress of TFMLs.

• 소성∙가공
• /
• 제27권4호
• /
• pp.227-235
• /
• 2018

The flow stresses have been identified prior to a numerical simulation for predicting a deformation of materials using the experimental or analytical analysis. Recently, the flow stress models considering the temperature effect have been developed to reduce the number of experiments. Artificial neural network can provide a simple procedure for solving a problem from the analytical models. The objective of this paper is the prediction of flow stress on the fiber metal laminate using the artificial neural network. First, the training data were obtained by conducting the uniaxial tensile tests at the various temperature conditions. After, the artificial neural network has been trained by Levenberg-Marquardt method. The numerical results of the trained model were compared with the analytical models predicted at the previous study. It is noted that the artificial neural network can predict flow stress effectively as compared with the previously-proposed analytical models.

• 소성∙가공
• /
• 제31권6호
• /
• pp.365-375
• /
• 2022

The microstructure and high-temperature plastic deformation behavior of the modified Al-0.7Mn alloy were investigated and compared with the conventional Al-0.3Mn (Al3102) alloy. α-Al (matrix) and Al₆(Mn, Fe) phases were identified in both alloys. As a result of microstructure observation, both alloys showed equiaxed grains, and Al-0.7Mn alloy showed larger grain size and higher Al₆(Mn, Fe) fraction than Al-0.3Mn alloy. High temperature compressive tests, the deformation temperatures of 410℃, 450℃, 490℃, 530℃ and strain rats of 10^-2/s, 10^-1/s, 1/s, 10/s, were conducted using Gleeble equipment. The flow stress values of Al-0.7Mn alloy were higher than that of Al-0.3Mn alloy at all strain rates and temperature conditions. Constitutive equations were presented using the flow stresses obtained from experimental results and the Zener-Hollomon parameter. In the true stress-true strain curves of the two alloys, the experimental and predicted values were in good agreement with each other. Based on the dynamic material model, eutectic deformation maps of Al-0.7Mn and Al-0.3Mn alloys were suggested, and the plastic instability region was presented. The modified Al-0.7Mn alloy showed a wider plastic instability region than that Al-0.3Mn alloy. Based on the process deformation maps, the MPE tube parts could be manufactured through the actual extrusion process using the suggested conditions.