Department of Mechanical engineering

Permanent URI for this collectionhttp://localhost:4000/handle/123456789/1921

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Author: search.filters.author.Garg, Girish KantSubject: search.filters.subject.Surface roughness

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Now showing 1 - 4 of 4
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    A Comparative Analysis of Surface Roughness Prediction Models Using Soft Computing Techniques
    (Springer, 2020-07) Garg, Girish Kant; Sangwan, Kuldip Singh
    Surface 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.
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    Optimization of Machining Parameters to Minimize Surface Roughness using Integrated ANN-GA Approach
    (Elsevier, 2015) Sangwan, Kuldip Singh; Garg, Girish Kant
    The 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.
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    Prediction and optimization of machining parameters for minimizing power consumption and surface roughness in machining
    (Elsevier, 2014-11) Sangwan, Kuldip Singh; Garg, Girish Kant
    Energy 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.
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    Multi-objective optimization of machining parameters to minimize surface roughness and power consumption using TOPSIS
    (Elsevier, 2019) Routroy, Srikanta; Garg, Girish Kant
    Energy saving in the industrial sector is mandatory for emerging countries to reduce negative environmental impact. Manufacturing consumes a significant amount of energy and releases a large amount of waste (solid, liquid and gas), resulting in the substantial stress on the environment. Negative environmental impact is due to a large amount of energy consumption by the machine tools in discrete manufacturing processes like turning, milling and drilling etc. This paper presents a multi-objective optimization model to optimize the machining parameters in turning process. Two objectives, surface roughness and power consumption are simultaneously optimized. The machining parameters are cutting speed, feed rate and depth of cut. Technique for order preference by similarity to ideal solution (TOPSIS) is used to identify the optimal turning parameters and the obtained results indicate that depth of cut is the most significant factor followed by the feed rate and cutting speed. The results obtained by the TOPSIS approach are compared with the existing grey relational analysis approach results. It is found that both optimization techniques show different optimal values. The confirmations experiments are necessary to select the best optimization approach.