Cutting forces are important factors for determining machine serviceability and product quality. Factors such as speed feed, depth of cut and tool noise radius affect on surface roughness and cutting forces in turning operation. The artificial neural network model was used to predict cutting forces with related to inputs including cutting speed (m/min), feed rate (mm/rev), depth of cut (mm) and work piece hardness (Map). The outputs of the ANN model are the machined cutting force parameters, the neural network showed that all (outputs) of all components of the processing force cutting force FT (N), feed force FA (N) and radial force FR (N) perfect accordance with the experimental data. Twenty-five samp

In this study the simple pullout concrete cylinder specimen reinforced by a single steel bar was analyzed for bond-slip behavior. Three-dimension nonlinear finite element model using ANSYS program was employed to study the behavior of bond between concrete and plain steel reinforcement. The ANSYS model includes eight-noded isoperimetric brick element (SOLID65) to model the concrete cylinder while the steel reinforcing bar was modeled as a truss member (LINK8). Interface element (CONTAC52) was used in this analysis to model the bond between concrete and steel bar. Material nonlinearity due to cracking and/or crushing of concrete, and yielding of the steel reinforcing bar were taken into consideration during the analysis. The accuracy of this

In the literature, several correlations have been proposed for hold-up prediction in rotating disk contactor. However,
these correlations fail to predict hold-up over wide range of conditions. Based on a databank of around 611
measurements collected from the open literature, a correlation for hold up was derived using Artificial Neiral Network
(ANN) modeling. The dispersed phase hold up was found to be a function of six parameters: N, vc , vd , Dr , c d m / m ,
s . Statistical analysis showed that the proposed correlation has an Average Absolute Relative Error (AARE) of 6.52%
and Standard Deviation (SD) 9.21%. A comparison with selected correlations in the literature showed that the
developed ANN correlation noticeably

In this article, the casting method was used to prepare poly(methyl methacrylate)/hydroxyapatite (PMMA/HA) nanocomposite films incorporated with different contents (0.5, 1, and 1.5 wt%) of graphene nanoplatelets (Gnp). The chemical properties and surface morphology of the PMMA/HA blend and PMMA/HA/Gnp nanocomposite were characterized using FTIR, and SEM analysis. Besides, the thermal conductivity, dielectric and electrical properties at (1–107 Hz) of the PMMA/HA blend and PMMA/HA/Gnp composites were investigated. The structural analysis showed that the synthesized composites had a low agglomerated state, with multiple wrinkles of graphene flakes in the PMMA/HA blend. The thermal conductivity was improved by more than 35-fold its value for

Samarium ions (Sm +3), a rare-earth element, have a significant optical emission within the visible spectrum. PMMA samples, mixed with different ratios of SmCl3.6H2O, were prepared via the casting method. The composite was tested using UV-visible, photoluminescence and thermogravimetric analysis (TGA). The FTIR spectrometry of PMMA samples showed some changes, including variation in band intensity, location, and width. Mixed with samarium decreases the intensity of the CO and CH2 stretching bands and band position. A new band appeared corresponding to ionic bonds between samarium cations with negative branches in the polymer. These variations indicate complex links between the Sm +3 ion and oxygen in the ether group. The optical absorption