Mm Engineering College

Ambāla, India

Mm Engineering College

Ambāla, India
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Chhibber R.,Indian Institute of Technology Jodhpur | Mehta N.P.,MM Engineering College
Materials Science Forum | Year: 2013

The application of high strength low alloy (HSLA) steels has been limited by unavailability of suitable joining and filler metals in submerged arc welding (SAW) processes. The present work aims at the design and development of flux for Submerged Arc Welding of HSLA steel. In the work L8 array of Taguchi Design is used to formulate eight types of fluxes to vary basicity index (BI) from 1.26 to 2.81 and to study the effect of flux constituents and basicity index on tensile strength, microhardness and microstructure of the weld metal. Empirical models for ultimate tensile strength and microhardness at the centre of weld versus flux constituents and basicity index have been developed. From the experiments it is found that ultimate tensile strength increase with increase of basicity index with minimum at 1.26 increases upto 2.33 and then further decreases whereas opposite in case of microhardness which is highest at 1.26 and minimum at 1.9. Increase of CaO in the flux increases ultimate tensile strength but microhardness remains unaffected whereas increase of SiO2 decreases ultimate tensile strength but microhardness remains constant. Microhardness decreases critically with increase of CaF2. © (2013) Trans Tech Publications, Switzerland.

Sharma V.,Gb Pant Engineering College | Bhaduria H.S.,Gb Pant Engineering College | Prasad D.,MM Engineering College
International Conference on Computing, Communication and Automation, ICCCA 2015 | Year: 2015

Deployment plays an essential role in determining the future performance of any Wireless Sensor Network (WSN). Random deployment is a common method to scatter Sensor Nodes (SNs) within a vast and unreachable region. In this paper we have proposed a Centrifugal Cannon based Sprinkler (CCS) to scatter SNs from helicopter moving over the candidate region. The proposed model is time efficient and effective as it minimizes the number of scans over the region. © 2015 IEEE.

Aggarwal S.K.,Mm Engineering College | Goel A.,National Institute of Technology Kurukshetra | Singh V.P.,Texas A&M University
Water Resources Management | Year: 2012

In this study, forecasting of stage and discharge was done in a time-series framework across three time horizons using three models: (i) persistence model, (ii) feed-forward neural network (FFNN) model, and (iii) support vector machine (SVM) model. For these models, lagged values of the time series constituted the set of input variables which had been selected by principal component analysis (PCA). Parameters of FFNN and SVM models were determined by sensitivity analysis. All the three models were evaluated using data from Mahanadi River, India, and their forecasting performance was then compared. It is shown that over a shorter forecasting horizon, it is difficult to outperform the persistence model. Moreover, results show that forecasting of stage and discharge over a longer time frame by the SVM model is more accurate than that by the other two models. © 2012 Springer Science+Business Media B.V.

Jindal S.,MM Engineering College | Chhibber R.,Thapar University | Mehta N.P.,MM Engineering College
Advanced Materials Research | Year: 2012

The application of High Strength Low Alloy (HSLA) steels has expanded to almost all fields viz. automobile industry, ship building, line pipe, pressure vessels, building construction, bridges, storage tanks. HSLA steels were developed primarily for the automotive industry to replace low-carbon steels in order to improve the strength-to-weight ratio and meet the need for higherstrength materials. Due to higher-strength and added excellent toughness and formability, demand for HSLA steel is increasing globally. With the increase of demand; other issues like the selection of filler grade and selection of suitable welding process for the joining of these steels have become very significant. This paper discusses the various issues regarding selection of suitable grade and selection of suitable welding process for joining of HSLA steels and issues concerning the structural integrity of HSLA steel welds.

Sharma A.,JMIT | Garg M.P.,University of Punjab | Goyal K.K.,Mm Engineering College | Kumar A.,Mm Engineering College
International Journal of Machining and Machinability of Materials | Year: 2016

In recent years, more and more newer and hard to machine materials have been developed with an ever increasing demand of machining the complex shapes which has lead the conventional machining operations reach their limitations. In the present work, an attempt is made to machine the 6063 aluminium metal matrix composite, which is the combination of Al alloy 6063 reinforced with zirconium silicate 5% (i.e., Al 6063/ZrSiO4p ) using wire electrical discharge machining (WEDM). Experiments have been designed through Box-Behnken design (BBD) approach of response surface methodology (RSM). The empirical models for response characteristics viz. cutting rate and surface roughness have been developed considering the WEDM input process parameters. Analysis of variance (ANOVA) and F-test are performed to study the significance of process parameters on the response outputs. To aid in selecting the best combination of settings, the concept of desirability is utilised. Scanning electron microscopy (SEM) of machined surfaces is carried out to understand the effect of various process parameters on work piece material surface characteristics. The confirmation experiments have been conducted for the optimal parameters suggested by developed empirical models and it has been observed that the experimental results are in good agreement with the predicted results. © 2016 Inderscience Enterprises Ltd.

Saroha S.,Mm Engineering College | Aggarwal S.K.,Mm Engineering College
2014 Recent Advances in Engineering and Computational Sciences, RAECS 2014 | Year: 2014

This paper presents a comparison of three different classes of artificial neural networks (ANN) for multi-step ahead time series forecasting of wind power. The neural network needs past wind generation measurement as an input. For time series prediction, the time lag data pattern is required & for this purpose the statistical tool called autocorrelation function (ACF) facilitates to work out on the input variables of neural networks. The three models which have been used are: linear neural network with time delay (LNNTD), feed forward neural network (FFNN) and Elman recurrent neural network (ERNN). The performance comparisons of the models are on the basis of mean absolute error (MAE) & mean absolute percentage error (MAPE). Data of wind power from Ontario Electricity Market for the year 2011-2012 has been considered for the case study and tested for a period of one week for twelve multi-steps ahead forecasting. It is observed that all class of neural networks shows almost equal results. © 2014 IEEE.

Tandon B.,Mm Engineering College | Narayan S.,PEC University | Kumar J.,PEC University
International Journal of Automation and Control | Year: 2015

The aim of this paper is to compare different toolboxes used for solving the linear matrix inequalities (LMIs) to design a robust controller for a feedback linearised continuous stirred tank reactor (CSTR). The problem of robust controller design is converted to a problem of solution of linear matrix inequalities (LMIs) and computationally simple non-iterative and iterative algorithms can be used for controller tuning. LMI-based H8 state feedback controller is designed for CSTR and MATLAB environment enables to compare the results of the LMI Lab in robust control toolbox with the results obtained by using public domain softwares - YALMIP and CVX toolbox. Copyright © 2015 Inderscience Enterprises Ltd.

Saroha S.,Mm Engineering College | Aggarwal S.K.,Mm Engineering College
Proceedings of 6th IEEE Power India International Conference, PIICON 2014 | Year: 2014

In present day scenario statistical (time series) and physical (NWP) models are utilized for wind power forecasting and many of them are using neural networks to obtain greater accuracy of wind power prediction at final stage. In a time series framework, forecasting is categorised into two ways single step ahead and multi-step ahead. In this paper an advanced time-series model for multi-step ahead wind power prediction based on artificial intelligence techniques is presented. This method requires an input of past measurements for prediction & input is settled on the basis of statistical tool called Auto Correlation Function (ACF). Genetic Algorithms based Neural Network (GANN) and Feed Forward Neural Network (FFNN) trained by Levenberg-Marquardt (LM) training algorithm are employed. Mean absolute error (MAE) and mean absolute percentage error (MAPE) are considered as the performance metric and both models are also compared with persistence model. The data of wind power has been collected from Ontario Electricity Market for the year 2009-12 and tested for one year up to 12 multi-steps ahead forecasting. It has been observed that GANN gives better performance as compared to FFNN. © 2014 IEEE.

Rattan S.S.,National Institute of Technology Kurukshetra | Mehta N.P.,Mm Engineering College | Bhushan G.,National Institute of Technology Kurukshetra
Journal of Engineering Technology | Year: 2011

Sometimes the line of action of the load does not pass through the axis of a bearing and is shifted on either side by a few degrees. The effect of load orientation on the stability of a three-lobe pressure dam bearing has been studied in this article. A three-lobe pressure dam bearing is produced by incorporating two pressure dams in the upper lobes and a relief track in the lower lobe of an ordinary three-lobe bearing. The results show that the stability of a three-lobe pressure dam bearing supporting either rigid or flexible rotor is increased for the positive values of load orientation, that is, when the load line is shifted in the opposite direction of rotation.

Gupta R.D.,Mm Engineering College | Ghai S.,Indian Institute of Technology Delhi | Jain A.,National Institute of Technology Kurukshetra
Journal of Engineering Technology | Year: 2011

In this paper, the findings of boiler house efficiency improvement study carried out in a large boiler house unit of a pulp and paper mill has been presented. The causes of poor boiler efficiency were various heat losses such as loss due to unburnt carbon in refuse, loss due to dry flue gas, loss due to moisture in fuel, loss due to radiation, loss due to blow down, and loss due to burning hydrogen, etc. The various heat losses were analyzed and a set of recommendations were made to the plant management for implementation, so that efficiency of boiler can be increased. Five important recommendations were implemented by plant management, and it has been seen that there is tremendous increase in boiler efficiency. Economic analysis reveals that the expenditure on the proposed system will be recovered in a short span of time. This work, with only five recommendations implemented, has resulted in net increase of 2% in overall boiler efficiency and an annual saving of Rs. 34,12,395. In addition, it is observed that carefulness in the operation of boiler can help a great deal in energy efficiency improvement in boiler.

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