Refinement of primary carbides in AISI DC 53 cold work tool steel by thin section copper permanent mould casting and water atomization

In this study, as alternative methods to conventional production methods involving thin section copper permanent mould casting (fast solidification) and water atomization (rapid solidification) of AISI DC 53 cold work tool steel have been investigated. Thin slabs obtained by copper mould casting was homogenized at 1150 °C for one hour and then hot rolling was be applied. After hot rolling, conventional heat treatment have been applied to thin slabs. The same heat treatment procedure have been performed for a commercially available AISI DC 53 which is manufactured by thick section ingot casting and rolling. In order to investigate also the effect of rapid solidification on AISI DC 53 tool steel, steel powder was produced by water atomization technique. Experiments have revealed that thin steel slabs in permanent copper mould and rapidly solidified tool steel powder by water atomization have a more refined primary M₇C₃ carbides than commercially available steel.     

Influence of graphene doping rate in PVA organic thin film on the performance of Al/p-Si structure

Journal of Materials Science: Materials in Electronics - To determine which of graphene (Gr) doping ratio (pure, 3–5%) into PVA is better or not, Gr-based PVA organic thin film were grown...     

Post-fire behavior of CFRP retrofitted thin-walled steel tanks

Thin-walled shell structures are characterized by a lightweight structural form with high strength. This structure has many applications in various engineering fields and is considered to be a basic tool in the modern industry. This study investigates the effect of CFRP on tanks after post-fire in order to examine the buckling and post-buckling of the cylindrical tanks. Herein, post-fire was introduced at different temperatures (150°C-600°C). Ten laboratory specimens in two groups with verified temperatures, under external pressure, are examined. The models were designed to demonstrate how repairing steel tanks, which are damaged by fire, by using carbon-fiber-reinforced polymer can recover lost capacity. The results of testing under different theories and codes were compared. This study shows that the initial buckling load of the temperature group increased by 30.82% to 57.09% compared to the temperature without-CFRP group. The overall buckling load of the temperature group increased by 37.01% to 67.74% compared to the temperature without-CFRP group. The collapse load of the temperature group increased by 27.04% to 52.64% compared to the temperature without-CFRP group. And using carbon-fiber-reinforced polymer on models, which are damaged by fire, can be an improvement behavior for the buckling and post-buckling specimen tests.     

Investigation of flow parameters of Reiner–Philippoff nanofluid flow with higher-order slip properties,activation energy,and bioconvection by artificial neural networks

In this study, the flow parameters of Reiner–Philippoff nanofluid flow with high-order slip properties, activation energy, and bioconvection have been analyzed using artificial neural networks (ANNs). Local Nusselt number (LNN), local Sherwood number (LSN), and motile density number (MDN) are considered as flow parameters. Numerical values have been obtained by numerical methods using flow equations. To estimate the flow parameters, three different ANN models have been designed. The Levenberg–Marquardt training algorithm is used in multilayer perceptron network models with 10 neurons in the hidden layers. In all, 70% of the data set has been used for training the models, 15% for validation, and 15% for testing. The performance analysis of the network models has been made by calculating the determined performance parameters. The R values for the LNN, LSN, and MDN parameters have been calculated as 0.99261, 0.98769, and 0.99102, respectively, and the average deviation values are −0.65%, 0.06%, and −0.11%, respectively. The attained outcomes showed that the ANNs can predict the LNN, LSN, and MDN, which are the flow parameters of the Reiner–Philippoff nanofluid flow, with high accuracy.