P. S. Robi

Application of neural networks in generating processing map for hot working

Abstract An important parameter in the mechanical working of materials is called workability, which is the relative ease with which a metal can be shaped through plastic deformation without the formation of any defect. Workability can be evaluated by means of processing maps, constructed from experimentally generated flow stress variation with respect to strain, strain rate and temperature. The present work demonstrates the use of neural network in generating processing maps for hot working processes. A neural network model was trained and tested for predicting the flow stress by taking data available in the literature for 99.99% pure aluminum. It was found that the trained neural network could predict the flow stress for unseen data quite reliably. At strain of 0.4, power dissipation and instability maps were constructed, utilizing the flow stress prediction by neural network. Superimposition of these maps provided processing maps at 0.4 strain, which was similar to that available in the literature. This established the potential of applying neural network, which is more robust technique than conventional method, for generating the processing map.

Synthesis of nano-crystalline RuAl by mechanical alloying

Nano-crystalline RuAl was synthesised by mechanical alloying. The evolution of the nano-crystalline RuAl phase during the mechanical alloying process using ruthenium and aluminium powders was studied. During the milling process, the peaks corresponding to reflections from the aluminium planes disappeared. The variation of crystallite size and microstrain with milling time was evaluated using X-ray diffraction (XRD) patterns. though the XRD results showed the formation of a RuAl phase after 7h of milling, scanning electron microscopy studies revealed that the RuAl phase was formed after 2h of milling. The analysis revealed that average crystallite sizes of 17 and 120 nm were obtained for RuAl and Ru phases, respectively, during the milling process. Density value of 97 % of the theoretical value was obtained for the milled powder mixture after cold compaction and sintering.

A systematic procedure for the design of a cold rolling mill

Abstract This paper presents a systematic design procedure for the design of a laboratory cold rolling mill. In order to arrive at proper decisions at various stages of the design, the concepts of fuzzy sets and priority decision tables were employed. The design process starts from deciding specifications and gradually reaches the detailed design phase. Specifications were fixed by trading-off various conflicting goals using the fuzzy set-based methodology. The various factors to be considered for deciding the roll diameter are presented. The roll diameter and motor power are chosen using the fuzzy set-based technique. Three possible arrangements for transmitting the power to rolls were conceived. The best among these three design alternatives was chosen by preparing a priority decision table. After the conceptual and embodiment stages of the design, the detailed design was carried out in a conventional way. The present paper gives more emphasis to a systematic design procedure for the conceptual and embodiment stages in the design process.

Microhardness of ternary vanadium pentoxide glasses

Abstract The room temperature microhardness of glasses with composition x V 2 O 5 ·20SnO·(80− x )TeO 2 (18≤ x ≤50), x V 2 O 5 ·40CaO·(60− x )P 2 O 5 (10≤ x ≤30) and x V 2 O 5 ·40CaO·(60− x )B 2 O 3 (10≤ x ≤30) was measured using a Vickers microhardness tester. Composition dependence of the Vickers Hardness Number (VHN) of the V 2 O 5 glasses has been interpreted in terms of the non-bridging oxygen atoms contained in the glass and the softening point of the glasses.

Designing and Utilizing of the Solar Water Heater for Digestion of Lignocellulosic Biomass

Conversion of woody biomass, animal waste such as cattle dung, chicken litters, pig manure, municipal solid, and agricultural wastes to methane gas under oxygen-free environment and favorable temperature range is called anaerobic digestion. But achieving the required temperature range under normal condition is difficult which necessitates the development of a heating system for better digestion process. The endeavor of the present investigation was developing 1.8 m2 solar collectors intended to heat biogas digester and investigating its performance. The solar collector was tested experimentally and numerically for heating water before implementing in actual model. A three-dimensional computational fluid dynamics model was developed by taking a single straight tube attached with an absorber plate at the bottom to predict outlet water and absorber plate temperature. The developed model predicted the outlet water temperature and absorber plate temperature with a reasonable accuracy. Moreover, 53 °C of outlet water temperature and 44% daily average efficiency were achieved at a flow rate of 0.021 kg/s. As the obtained temperature was found to be good enough for anaerobic digestion, the developed solar collector could be regarded as an alternate option.

Design and development of underwater robot

Majority of the design proposed for under water robots have been either biomimetic or motor driven thruster operated. This paper presents a novel design for a 4 degree of freedom thruster operated under water robot minimizing the effect of drag during under water navigation. In the paper possible hull shapes were analyzed. After deciding the shape, pressure distribution and drag forces were calculated. Depending on the result, the mathematical model was built based on suitable assertion. The calculations justify the efficiency of using a cylindrical hull and two perpendicular pairs of thrusters for the underwater robot. Finally, the paper also presents electronics and communication system suitable for the robot considering possible difficulties which may be encountered during underwater navigation.

Deformation Mechanism Maps for Al-Cu-Mg Alloys Micro-Alloyed with Tin

High temperature deformation behavior of Al–5.9%Cu–0.5%Mg alloy and Al–5.9%Cu–0.5%Mg alloy containing 0.06 wt.% of Sn was studied by hot compression tests at various temperatures and strain rates. Addition of trace amounts of Sn into the Al–Cu–Mg alloy system resulted in a significant increase of flow stress for all conditions of temperature and strain rate. 100% and 89% of the flow stress values during hot deformation could be predicted within ± 10% deviation values for the aluminum alloys with and without Sn content, respectively, by artificial neural network (ANN) modeling. From the deformation mechanism maps and microstructural investigation, the safe process regimes for hot working of the base alloy was identified to be at (i) very low strain rate (< 0.003 s−1) at temperature < 450 °C, and (ii) high temperature (> 400 °C) with strain rate > 0.02 s−1. For the micro-alloyed alloy, it was at low strain rates (< 0.01 s-1) for the entire temperature range studied. Flow softening for both alloys was observed to be at low strain rates and was identified to be due to dynamic recrystallization (DRX). The metallurgical instability during deformation was identified due to shear band formation and/or inter-crystalline cracking.

Artificial Neural Network (ANN) Model for Prediction of Mixing Behavior of Granular Flows

Mixing and segregation behavior of granular flows inside a particulate system comprising an oscillating sectorial container is predicted by an artificial neural network (ANN) model. By employing discrete element method (DEM), numerically simulated characteristics of a sectorial container, which is subjected to harmonic angular oscillations, are trained for the development of a neural network model. Binary system of particles is simulated and degree of mixing is estimated by varying different parameters, such as particle size ratio (1:1 to 1:3), frequency of oscillations (1 to 4 Hz), amplitude of oscillations (30 to 60°), volume filling fraction (0.04 to 0.24), and number of cycles (1 to 20). The learning of ANN is accomplished by feed forward back propagation algorithm. It is found that mean mixing concentration predicted by the neural network model developed in this work is in a good agreement with the simulated values. Percentage error predicted by ANN model is less than ± 8% for 82 out of the 90 data values. Development of the neural network model and its use for the prediction of the outcome of the system (especially in cases where several operating parameters, which determine the outcome of the system, have a non-linear relationship with each other) is believed to be an accurate and computationally inexpensive way of understanding the behavior of the system.