Department of Mechanical engineering
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Item Fabrication of Ti-6Al-4V thin sheet using wire-EDM and its characterisation(Inder Science, 2022-02) Mathew, Nitin TomAn appreciable demand for thin titanium sheets is observed in recent years due to technological development in various sectors. The existing sawing methods are uneconomical to produce high aspect ratio sheets with good dimensional accuracy. The current work focuses on extracting thin sheet of 380 mm × 50 mm × 0.2 mm from Ti-6Al-4V using wire electric discharge machining and investigated the dimensional and surface variations. The preliminary study using usual wire EDM procedure resulted in poor dimensional accuracy for a thickness in the range of 500 µm due to the lateral movement of sheet free end. Hence, in this work, a support system is attached to workpiece to minimise sheet movement and to improve the flushing during extraction. This helped to reduce the thickness variation up to 6% for a 0.2 mm Ti-6Al-4V sheet. The root mean square roughness is found to be consistent up to a height of 240 mm from the top surface. Also, majority of the measured skewness are positive. Altogether, the results of the study can be applied to fabricate thin sheets for additive manufacturing process.Item Experimental Investigation of Pool BHT Performance of R-141b on Micro/Nano-Porous Copper Coating Prepared by a Two-stage Electrodeposition Method(IEI Conferences, 2021) Belgamwar, Sachin U.Pool boiling heat transfer (BHT) of R-141b on po-rous Cu coated heating surface was experimentally studied. Porous Cu coating was fabricated on a plain Cu heating sur-face through a two-stage electrodeposition method. Surface characterization of Cu coating confirmed the successful syn-thesis of micro/nano-porous Cu coating. Experimental re-sults showed that the Cu coated heating surface introduced a significant enhancement in heat transfer coefficient (HTC) and a great reduction in wall superheat compared to the plain Cu surface. The maximum enhancement in HTC for the Cu coated heating surface was approximately 53% compared to the uncoated heating surface. This is believed to have re-sulted from the increase in active surface area, nucleation site density and cavitation activity owing to the microporous structure of Cu coating. Obtained results showed that the mi-croporous Cu coated heating surface could be employed in modern heat transfer applications.Item Fabrication of Cu@G composite coatings and their pool boiling performance with R-134a and R-1234yf(Taylor & Francis, 2022-01) Belgamwar, Sachin U.The present work explores the pool-boiling performance of refrigerants (R-134 and R-1234yf) on the plain Cu and graphene nanoplatelets (G) reinforced Cu matrix (Cu@G) composite coated heating surface. A two-step electrodeposition technique was employed to prepare microporous Cu@G composite coatings. Scanning electron microscopy (SEM) and energy-dispersive X-ray spectroscopy (EDS) studies confirmed the successful fabrication of microporous structure of Cu@G composite coatings. Surface profilometer investigation was done to know the surface roughness of prepared Cu@G composite coatings. Pool boiling experiments were carried out with increasing heat flux from 8.80 kW/m2 to 61.25 kW/m2 at a saturation temperature of 10°C. Test results of R-134 and R-1234yf were compared. The experimental results revealed that the heat transfer coefficients (HTCs) of R-134a were higher than R-1234yf for plain Cu and Cu@G composite coated heating surfaces.Item Experimental investigation of pool boiling heat transfer performance of refrigerant R-134a on differently roughened copper surfaces(Elsevier, 2021) Belgamwar, Sachin U.Pool boiling heat transfer of refrigerant R-134a at saturation temperature of 5 °C was investigated experimentally on copper. The effect of surface roughness was studied at various average roughness (Ra) values ranging from 0.130 to 0.274 µm. All experimental samples were vertically oriented, and experiments were carried out at varying heat flux ranges between 10 and 70 kW/m2. The investigations are carried out to calculate the boiling heat transfer coefficient (h), wall superheat (ΔT) and heat flux (q) for different roughness of heat transfer surfaces. The obtained results revealed that the Cu surface with Ra = 0.274 µm showed outstanding heat transfer performance compared to other Cu surfaces. The overall boiling performances enhanced with an increase in roughness value of the heat transfer surface owing to the increased number of cavities.Item Surface roughness modelling for Double Disk Magnetic Abrasive Finishing process(Elsevier, 2017-01) Kala, PrateekMagnetic Abrasive Finishing (MAF) is a super finishing process having capability to produce surface finish in nano-meter level. The value of surface roughness obtained using MAF process depends upon the material properties of work piece and process factors. In the present work, a mathematical model has been proposed for Double Disk Magnetic Abrasive Finishing (DDMAF) process. DDMAF process is a process that can effectively finish even the flat paramagnetic work piece, which were considered ineffective to be finished by conventional MAF. In the present work, the surface roughness has been modelled as a function of workpiece material properties and process factors namely working gap, abrasive mesh number, percentage weight of abrasive, rotational speed and feed rate. The process model utilizes Lorentz force and Amperes law to estimate the finishing force experienced by an iron particle. The force so obtained has been used to calculate the finishing force transferred to the abrasive particle by using force equilibrium between iron and abrasive particle. The effect of normal distribution of abrasive particle size and the effect of frictional force on finishing forces have also been considered in this work. A MatLab code has been developed to include all the above aspects to determine the change in surface roughness. The model so obtained has been validated using experimental findings and thereafter used to study the effect of various process parameters.Item Comparative Study of Estimated Surface Roughness Using GA and PSO Techniques for Milling of Thin-Walled Structures(Springer, 2022-04) Bera, T.C.Thin-walled structures, due to their lightweight, have found significant applications in the aerospace industry. For the manufacturing of any component, its surface quality index is of prime importance. A very well-known measure of this surface quality is surface roughness. For a product of high quality, the surface roughness value is often desired to be minimum. However, the machining parameters for the production of such surfaces often rely on the engineer's experience and expertise, which always do not lead to the best possible results. In this study, a neural network was first created for surface roughness estimation, then evolutionary algorithms such as Genetic Algorithm and Particle Swarm Optimization were used to minimize the surface roughness value. During this process, the impact of milling parameters such as rake angle, nose radius, and approach angle on the surface roughness value was also studied with the aid of surface plots of surface roughness developed by taking two parameters at a time and holding the third parameter constant.Item Investigating the Effect of Cutting Parameters on Average Surface Roughness and Material Removal Rate during Turning of Metal Matrix Composite Using Response Surface Methodology(IJRITCC, 2015) Shrivastava, SharadThis research work investigate the effect of cutting parameters on average surface roughness and material removal rate during turning of Metal Matrix Composite using response surface methodology. The experimental studies are carried out under changing machining parameters like cutting speed, feed and depth of cut during turning of metal matrix composite. Response surface methodology based on the Face centered design technique has been used for the development of mathematical models to predict average surface roughness and metal removal rate. The conclusions revealed that the feed is the most influential machining parameter on the average surface roughness followed by depth of cut and the cutting speed. The depth of cut has significant for both the average surface roughness and metal removal rate for the MMC steel.Item A Comparative Analysis of Surface Roughness Prediction Models Using Soft Computing Techniques(Springer, 2020-07) Garg, Girish Kant; Sangwan, Kuldip SinghSurface roughness is one of the significant index to measure the product quality of the machined parts. The objective of this work is to contribute towards the development of prediction models for surface roughness. In this work, the predictive models were developed for turning operations using soft computing techniques; support vector regression (SVR) and artificial neural network (ANN). The turning experiments are conducted to obtain the experimental data. The developed predictive models were compared using relative error and validated using hypothesis testing. The results indicate that both techniques provide a close relation between the predicted values and the experimental values for surface roughness and are appropriate to predict the surface roughness with significant acceptable accuracy. It is found that ANN performs better as compared to SVR.Item Optimization of Machining Parameters to Minimize Surface Roughness using Integrated ANN-GA Approach(Elsevier, 2015) Sangwan, Kuldip Singh; Garg, Girish KantThe surface roughness is a widely used index of product quality in terms of precision fit of mating surfaces, fatigue life improvement, corrosion resistance, aesthetics, etc. Surface roughness also denotes the amount of energy and other resources consumed during machining. This paper presents an approach for determining the optimum machining parameters leading to minimum surface roughness by integrating Artificial Neural Network(ANN) and Genetic Algorithm (GA). To check the capability of the ANN-GA approach for prediction and optimization of surface roughness, a real machining experiment has been referred in this study. A feed forward neural network is developed by collecting the data obtained during the turning of Ti-6Al-4 V titanium alloy. The MATLAB toolbox has been used for training and testing of neural network model. The predicted results using ANN indicate good agreement between the predicted values and experimental values. Further, GA is integrated with neural network model to determine the optimal machining parameters leading to minimum surface roughness. The analysis of this study proves that the ANN-GA approach is capable of predicting the optimum machining parameters.Item Prediction and optimization of machining parameters for minimizing power consumption and surface roughness in machining(Elsevier, 2014-11) Sangwan, Kuldip Singh; Garg, Girish KantEnergy and environmental issues have become pertinent to all industries in the globe because of sustainable development issues. However, the ever increasing demand of customers for quality has led to better surface finish and thus more energy consumption. The energy efficiency of machines tools is generally very low particularly during the discrete part manufacturing. This paper provide a multi-objective predictive model for the minimization of power consumption and surface roughness in machining, using grey relational analysis coupled with principal component analysis and response surface methodology, to obtain the optimum machining parameters. The statistical significance of the proposed predictive model has been tested by the analysis of variance (ANOVA) test. The obtained results indicate that feed is the most significant machining parameter followed by depth of cut and cutting speed to reduce power consumption and surface roughness. The constructed response surface contours can be used by the shop floor people to find and use the best combination of machining parameters for the given situation. The reduction of peak load through optimization will results in lowering the power consumption of the machine tools during non-cutting idling time.