ANN modeling of nickel base super alloys for time dependent deformation

Alloys 617 and 276 are nickel-based super alloys with excellent mechanical properties, oxidation, creepresistance, and phase stability at high temperatures. These alloys are used in complex and stochastic applications. Thus, it is difficult to predict their output characteristics mathematically. Therefore, the non-conventional methods for modeling become more effective. These two alloys have been subjected to time-dependent deformation at high temperatures under sustained loading of different values. The creep results have been used to develop the new models. Artificial neural network (ANN) was applied to predict the creep rate and the anelastic elongation for the two alloys. The neural network contains twenty hidden layer with feed forward back propagation hierarchical. The neural network has been designed with MATLAB Neural Network Toolbox. The results show a high correlation between the predicted and the observed results which indicates the validity of the models

Study on turbulent characteristics of flow boiling in a micro gap under the influence of surface roughness and micro fins

Micro gap heat sinks with internal micro fins are potential candidates for evaporative cooling of miniature electronic devices. Generation of turbulence during flow boiling in a micro gap is an important issue in two-phase heat transfer analysis. Surface roughness and fins play important role in turbulence generation. In this paper, effects of micro gap height, surface roughness and fin spacing on turbulence generation during flow boiling of pure water in this particular heat sink have been investigated by numerical simulation. Commercial software FLUENT 14.5 release has been used for simulation purpose. Volume of Fluid (VOF) model along with Renormalization Group Theory (RNG) based k − ε turbulence model has been used for fluid flow and heat transfer modeling. Simulation results demonstrate that turbulent kinetic energy increases in the flow direction due to large pressure drop inside micro gap. As pressure drop decreases with the increment of gap height, turbulent kinetic energy also declines. For the same reason, it has been found that generation of turbulent kinetic energy is lower for larger fin spacing. On the other hand, effect of surface roughness on turbulent kinetic energy is dominated by flow scale. For same Reynolds number, turbulence in larger fluid domains is more sensitive to surface roughness than smaller flow fields

Tool life modeling in high speed turning of AISI 4340 hardened steel with mixed ceramic tools by using face central cubic design

Tool life estimation for the cutting tool before the machining process is important due to economic and quality consideration. Thus, developing a model that can predict the tool life with high accuracy is an important issue. This paper deals with developing a new model of tool life for mixed ceramic tools in turning hardened steel AISI 4340 based on experimental tests. The experiments were planned and implemented using Central Composites Design (CCD) of Response Surface Methodology (RSM) with three input factors: cutting speed, feed rate and negative rake angle. The Face Central Cubic Design has been used as a special case of CCD. The analysis of variance (ANOVA) has been conducted to analyze the influence of process parameters and their interaction during machining. The first and second order models have been developed. It was found that the second order model provide higher accuracy prediction than the first order model. It was observed that the cutting speed is the most significant factor that influences the tool life for the two models, followed by the feed rate then the negative rake angle. The predicted values are confirmed by using validation experiments. Copyright © 2013 Praise Worthy Prize S.r.l. - All rights reserved

Crystallization kinetics and thermal behaviors of multi-walled carbon nanotube dispersed jute reinforced composite

Carbon nanotubes (CNTs) were dispersed within polyester resin to improve the thermal properties and to understand the degradation mechanism and reaction kinetics of jute reinforced composite. Viscoelastic behavior via dynamic mechanical analysis, strain rate effect and hygrothermal behavior of CNT filled jute composite were studied. The crystallization kinetics and microstructures were investigated with differential scanning calorimeter (DSC), X-ray diffraction (XRD) and scanning electron microscopy (SEM), respectively. Multiwalled CNT with 0, 1 and 3wt% was added within polyester resin matrix, whereas around 70% volume fraction of jute fiber is maintained in each sample. In dynamic mechanical analysis (DMA), 3% CNT filled composite showed better storage modulus and loss modulus values before the hygrothermal test. Due to the exposure to temperature (80°C) and relative humidity (95% RH) for 15 days in environment chamber, both storage and loss modulus of this composite reduced by around 10%

Tensile parameters evaluation of two solid solution super alloys by ANN modeling

Solid solution nickel base super alloys 617 and 276 possess excellent mechanical properties, oxidation, creep-resistance, and phase stability at high temperatures. These alloys are used in complex and stochastic applications including the structural material of high temperature heat exchanger. Thus, it is difficult to predict their output characteristics mathematically. Therefore, the non-conventional methods for modeling become more effective. These two alloys have been subjected to tensile deformation at high temperatures and different tensile parameters have been used to develop the new models. Artificial neural network (ANN) was applied to predict yield strength (YS), Ultimate Tensile strength (UTS), percent elongation (%El) and percent reduction in area (%RA) for the two alloys. The neural network comprises twenty hidden layer with feed forward back propagation hierarchical. The neural network has been designed with MATLAB Neural Network Toolbox. The results show a high correlation between the predicted and the observed results which indicates the validity of the models

Optimal design of magneto-rheological damper comparing different configurations by finite element analysis

Magnetorheological (MR) damper is one of the most advanced applications of semi active damper in controlling vibration. Due to its continuous controllability in both on and off state its practice is increasing day by day in the vehicle suspension system. MR damper’s damping force can be controlled by changing the viscosity of its internal magnetorheological fluids (MRF). But still there are some problems with this damper such as MR fluid’s sedimentation, optimal design configuration considering all components of the damper. In this paper both 2-D Axisymmetric and 3-D model of MR Damper is built and finite element analysis is done for design optimization. Different configurations of MR damper piston, MR fluid gap, air gap and Dampers housing are simulated for comparing the Dampers performance variation. From the analytical results it is observed that among different configurations single coil MR damper with linear plastic air gap, top and bottom chamfered piston end and medium MR fluid gap shows better performance than other configurations by maintaining the same input current and piston velocity. Further an experimental analysis is performed by using RD-8041-1 MR Damper. These results are compared with the optimized MR Damper’s simulation results, which are clearly validating the simulated results

ANSYS finite element design of an energy saving magneto-rheological damper with improved dispersion stability

The magnetorheological (MR) damper is one of the utmost progressive applications of asemi-active damper. Uninterrupted controllability in both on and off state is an important factor of its plenitude application. Current research is attempting to make the damper more effective and efficient by minimizing the existing limitations such as MR fluid’s sedimentation, power consumption and temperature rising, and design optimization. We have broadly analyzed the optimization of MR dampers design with finite element simulation where various parameters of the MR damper have been considered for more accurate results. A prototype MR fluid has been prepared by coating the carbonyl iron particles with xanthan gum to reduce sedimentation. The SEM and Turbiscan results noticeably verify the improved sedimentation stability. In addition, a power-saving MR damper model has been developed by finite element analysis using ANSYS software. Prolonged operation raises the damper’s body temperature and degrades the performance. However, in this energy-saving MR damper model the temperature is not rising to a higher value compared to the conventional dampers, and consequently promotes damper efficiency

Effect of geometrical parameters on boiling heat transfer and pressure drop in micro finned micro gap

Micro gap heat sinks are potential candidates of evaporative cooling. Additional fins in micro gap enhance heat transfer rate by increasing surface area and generating turbulence. The scope of this paper is to numerically investigate the influence of various geometrical parameters on thermal and hydraulic performance of a micro finned micro gap during flow boiling. For this purpose, flow boiling of water in a micro finned micro gap heat sink has been simulated using FLUENT 14.5 release. Thermal resistance and pressure drop have been calculated for various fin width-to-fin spacing ratio and ratio of base thickness-to-micro gap height. The results demonstrate that thermal resistance decreases for increasing both ratios. However, the descending rate is inconsistent. For higher ratios, decrement rate of thermal resistance is very slow, while pressure drop is very high. Hence, it is suggested that the dimensions should be optimized for extensive cooling performance

Surface roughness modeling in high speed hard turning using regression analysis

Surface roughness plays an important role in the final quality of the machining parts. Therefore, predicting and simulating the roughness before the machining process is an important issue. The purpose of this research is to develop a reliable model for predicting and simulating the average surface roughness (Ra) in high speed hard turning. An experimental investigation was conducted to predict the surface roughness in the finish hard turning with higher cutting speed. A set of sparse experimental data for finish turning of hardened steel (AISI 4340) and mixed ceramic inserts made up of aluminum oxide and titanium carbide were used as work piece and cutting tools materials. Four different models for the surface roughness were developed by using regression analysis and artificial neural network techniques. Two different techniques have been used in the regression analysis; Box Behnken Design (BBD) and Face Central Cubic Design (FCC).. The BBD model gave better prediction than the FCC in the design boundar

Energy cost optimization in high speed hard turning using simulated annealing algorithm

Selecting the cutting conditions to optimize the economics of machining process as assessed by energy machining cost is essential. The aim of this research is to determine the optimum cutting parameters that minimize the energy cost needed for removing one cubic centimetre of material in High Speed Hard Turning (HSHT) process. To achieve that, a set of experimental machining data to cut hardened steel AISI 4340 was obtained with different ranges of cutting speed, feed rate, depth of cut and negative rake angle using mixed ceramic as a cutting tool. Regression models have been developed by using Box-Behnken design as a design of experiment. Then, the Simulated Annealing Algorithm (SAA) has been used to optimize the cutting parameters. The data collected was statistically modelled. The results show that the range of minimum energy cost to remove one cubic centimetre of material for the three techniques can be achieved in the range of 300 to 308 as a cutting speed, -12 for cutting rake angle, 0.125 as a feed rate and 0.15 as a depth of cu