Sir Padampat Singhania University

About: Sir Padampat Singhania University is a education organization based out in Udaipur, India. It is known for research contribution in the topics: Diesel fuel & Encryption. The organization has 124 authors who have published 228 publications receiving 2066 citations. The organization is also known as: SPSU.

Integration of GA and neuro-fuzzy approaches for the predictive analysis of gas-assisted EDM responses

Abstract: This research work discusses the application of three intelligent prediction models, based on artificial neural network (ANN) with back-propagation algorithm, adaptive neuro-fuzzy inference system (ANFIS) and hybrid ANFIS and genetic algorithm (ANFIS-GA). These techniques are used for prediction and comparison of machining aspects such as material removal rate (MRR) and surface roughness during gas-assisted electrical discharge machining of D3 die steel. In the present work, helium-assisted EDM with perforated tool has been performed. In this work, parameters considered for machining are discharge current, pulse on time, duty cycle, tool rotation and discharge gas pressure. The suggested approach is based on up-gradation of ANFIS with GA. The GA algorithm is applied to improve the precision of the ANFIS model. The soft computing models were trained, tested and validated with experimental data. Mean square error (MSE), mean absolute error (MAE), root-mean-square error and correlation coefficient (R2), were used to measure the efficacy of models predicting abilities developed through ANN, ANFIS and hybrid ANFIS-GA approaches. The experiment and anticipated measure of MRR and SR of the process, acquired by ANN, ANFIS and hybrid ANFIS-GA, was found to be in good agreement. The prediction potential of proposed models was tested using new set of data for the training and testing process. The ANFIS-GA technique provides more accurate prediction of the responses in comparison with the ANN and the ANFIS. In general, the inference of this work discloses that the hybrid algorithm like ANFIS-GA is an efficient and effective approach for precise prediction of EDM process responses.

Accelerating Bianchi Type-V Cosmology with Perfect Fluid and Heat Flow in Sáez-Ballester Theory

Abstract: In this paper we discuss the law of variation of scale factor $a = (t^{k}e^{t})^{\frac{1}{n}}$ which yields a time-dependent deceleration parameter (DP) representing a new class of models that generate a transition of universe from the early decelerated phase to the recent accelerating phase. Exact solutions of Einstein’s modified field equations in Bianchi type-V space-time with perfect fluid and heat conduction are obtained within the framework of Saez-Ballester scalar-tensor theory of gravitation and the model is found to be in good agreement with recent observations. We find, for n=3,k=1, the present value of DP in derived model as q 0=−0.67 which is very near to the observed value of DP at present epoch. We find that the time-dependent DP is sensible for the present day Universe and give an earmark description of evolution of universe. Some physical and geometric properties of the models are also discussed.

Experimental Investigation and Modeling of Surface Finish in Argon-Assisted Electrical Discharge Machining Using Dimensional Analysis

Abstract: This study investigates the use of argon gas-assisted electrical discharge machining (AGAEDM) of high carbon high chromium die steel. Compressed argon gas in die-sinking EDM under controlled conditions was used to evaluate the surface roughness (SR). The influence of process parameters, viz., discharge current, pulse-on time, duty cycle, tool rotation, and discharge gas pressure, on SR has been investigated as well. Analysis of variance was applied to determine the significant factors affecting SR. In the course of this investigation, a semi-empirical model has been developed to determine SR through dimensional analysis while applying the AGAEDM process. The experimental and predicted values, gathered through the semi-empirical model, have been found to be in accord with each other. The mean error between the predicted and the experimental values was less than 5%. A comparison was performed between the RSM and semi-empirical models. The semi-empirical model was found to predict responses most precisely as compared to RSM model. In this connection, surface morphology analysis has also been done by using a scanning electron microscope in the machined specimens. The energy-dispersive X-ray and X-ray diffraction examination were used to study the relocation of different elements and development of compounds on the surface of the machined specimen.

Effect of alumina nanoparticles as additive on the friction and wear behavior of polanga-based lubricant

Abstract: Many researchers have tried to improve the tribological characteristics of lubricants to decrease coefficients of friction and wear rates. Recently, nanoparticles have emerged as a new kind of additive because of their size, shape, and other properties. A nanolubricant is a new kind of engineering lubricant made by dispersing nanoparticles in a lubricant. Investigations related to the tribological characteristics of lubricants with addition of nanoparticles are reviewed herein. The paper focuses on the effects of the nanoparticle concentration on the tribological performance during oil lubrication. Specifically, measurements of physicochemical properties and tribological analysis were performed along with morphological study of the nanoparticles. The mechanisms of lubrication involving nanoparticles are discussed based on data collected from the experimental analysis and compared with work in literature. The effect of varying the concentration of alumina (Al2O3) nanoparticles in polanga oil on the coefficient of friction and wear was evaluated. The minimum coefficient of friction and wear were observed at 0.075% concentration, but increased at 0.1% concentration. A smooth surface of the pin was observed at 0.075% nanoparticle concentration in comparison with polanga-based oil. The maximum total acid number change was obtained for the 0.1% concentration. The wear scar obtained during the test was also minimum for the 0.075% concentration, and a better surface was observed for this concentration. In terms of future prospects, similar work based on nanoparticles with different shape and size could be carried out for other nonedible oils.