BITS Faculty Publications

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Author: search.filters.author.Garg, Girish KantSubject: search.filters.subject.Mechanical Engineering

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    An integrated modelling and optimization approach for the selection of process parameters for variable power consumption machining processes
    (Springer, 2023-08) Routroy, Srikanta; Garg, Girish Kant
    Manufacturing industries are under intense pressure to reduce the energy usage of the machining processes without sacrificing productivity, owing to the fast-rising worldwide market and environmental issues. Variable-power consumption machining processes are highly complex than constant-power consumption machining processes, owing to change in one of the process parameters, i.e. cutting speed during end facing. Besides, integrated modelling and optimization of the variable power consumption machining processes for energy-saving have not received attention, consequently, industry deployment of energy-saving solutions is impeded. To bridge these gaps, in this work, the empirical model developed by the author is integrated for the formulation of a multi-objective optimization model of cutting energy consumption (Ecdry) and average-material removal rate (MRR¯¯¯¯¯¯¯¯¯¯¯¯) expressed by process parameters. First, the optimal parameters are determined for Ecdry and MRR¯¯¯¯¯¯¯¯¯¯¯¯ by mono-objective optimization using the Taguchi technique. Second, Grey relational analysis coupled with the Taguchi method is used to obtain the cumulative performance index of the Ecdry and MRR¯¯¯¯¯¯¯¯¯¯¯¯, and to determine their common optimal parameters, resulting in better-compromised decisions. The MRR¯¯¯¯¯¯¯¯¯¯¯¯ improves to 99.97% with only a 10.08% increase in Ecdry on common optimal parameters compared to optimal parameters with mono optimization of Ecdry. Further, analysis of variance revealed that all considered process parameters have statistical significance, and depth of cut is the most significant parameter followed by spindle speed, feed rate and tool nose radius. It was found that energy consumption values predicted by the integrated modelling and optimization approach are close to the experimental values.
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    Application of Optimization and Statistical Techniques in Post-Harvest Supply Chain: A Systematic Literature Survey
    (CRC Press, 2021) Garg, Girish Kant; Routroy, Srikanta
    The objective of this literature survey is to address the utility and potential of optimization, statistical surveys and mathematical research into post-harvest supply chains (PHSCs) to reduce losses. Empirical approaches have always helped to understand a generic issue with a specific perspective to highlight the shortcomings with the existing practice. The optimization approaches are used in the PHSC literature to design an optimum system to enhance revenues and to minimize losses. The existing PHSCs in developing countries such as India suffer enormous losses – e.g., revenue losses, physical damage of produce, microbial losses, qualitative losses, quantitative losses, etc. – in the absence of market prospects due to inefficient packaging for transportation and handling. This study covers a systematic literature survey covering the mathematical, statistical and optimization research performed in the field of PHSC and critical analysis to highlight the key aspects covered in the literature and their future scope.
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    Fuzzy-TOPSIS based multi-objective optimization of machining parameters for improving energy consumption and productivity
    (Elsevier, 2021) Garg, Girish Kant; Routroy, Srikanta
    Due to the increasingly global market and environmental challenges, there is a lot of pressure on manufacturing industries to reduce the energy consumption of the machining process without compromising productivity. The objective of this work is to develop a multi-objective optimization model for the selection of optimal cutting parameters during the turning of an Aluminum workpiece using carbide inserts. Two performance characteristics: energy consumption and productivity were simultaneously optimized. The Taguchi full factorial orthogonal array L27 was used to obtain the experimental plan. The Fuzzy based Technique for Order Preference by Similarity to Ideal Solution (Fuzzy-TOPSIS) was applied to determine the optimal cutting parameters for multi-objective optimization. The optimal results obtained by Fuzzy-TOPSIS were further validated by using the Taguchi method. ANOVA results show that all the considered cutting parameters were statistically significant. Further, the depth of cut was found the most influencing cutting parameter on the energy consumption and productivity.
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    Selection of optimum cutting parameters for minimization of specific energy consumption during machining of Al 6061
    (IOP, 2019) Garg, Girish Kant
    Manufacturing sector consumes a significant amount of energy globally. Machine tools are one of the major equipment in manufacturing sector and hence major consumer of energy. The electrical energy consumed by the machine tools results in emission of harmful gases and substantial stress on environmental. This work focuses on selection of optimum cutting parameters to minimize specific energy consumption (SEC) during turning of Al 6061 with tungsten carbide inserts in dry condition. Experiment are planned using L27 orthogonal array and Taguchi method is applied to determine optimum and most influencing cutting parameters for minimizing SEC. Results shows that feed is the dominating factor followed by cutting speed and depth of cut. The optimum value of feed (mm/rev), cutting speed (m/min) and depth of cut (mm) are found 0.12, 46.2 and 1.0 respectively. Further the energy consumption maps are developed to analyse the influence of cutting parameters on specific energy consumption. The developed energy consumption maps can be used for correlating the region of minimum SEC with selected cutting parameters.
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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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    Modeling of Stresses and Temperature in Turning Using Finite Element Method
    (Trans Tech Publications Ltd, 2013-02) Sangwan, Kuldip Singh; Garg, Girish Kant
    This paper focuses on finite element modeling of orthogonal cutting process of AISI 1045 steel using Modified Johnson Cook (MJC) as constitutive material flow model under various machining parameters. Finite element solutions of cutting forces, effective stresses and temperature are obtained for a wide range of cutting speeds and feeds. The effect of feed and cutting speed on cutting forces, effective stresses and temperature has been studied over a wide range of values. Percentage variation of each is also studied to predict co-relation with the different machining parameters.
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    Predictive Modeling of Turning Operations Using Response Surface Methodology
    (Trans Tech Publications Ltd, 2013-02) Sangwan, Kuldip Singh; Garg, Girish Kant
    This paper focuses on the development of a predictive model using the measured forces acting on the cutting tool during turning operation of AISI 1045 Steel using a Tungsten Carbide cutting tool insert. On the basis of the experimental results, second order mathematical model is developed in terms of machining parameters by using the Response Surface Methodology (RSM). The results are analyzed statistically and graphically. It has been observed that the predicted values using RSM also follow the same trend as given by the measured values.
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    Development of an Empirical Model for Optimization of Machining Parameters to Minimize Power Consumption
    (IOP, 2018) Garg, Girish Kant; Sangwan, Kuldip Singh
    The manufacturing sector consumes huge energy demand and the machine tools used in this sector have very less energy efficiency. Selection of the optimum machining parameters for machine tools is significant for energy saving and for reduction of environmental emission. In this work an empirical model is developed to minimize the power consumption using response surface methodology. The experiments are performed on a lathe machine tool during the turning of AISI 6061 Aluminum with coated tungsten inserts. The relationship between the power consumption and machining parameters is adequately modeled. This model is used for formulation of minimum power consumption criterion as a function of optimal machining parameters using desirability function approach. The influence of machining parameters on the energy consumption has been found using the analysis of variance. The validation of the developed empirical model is proved using the confirmation experiments. The results indicate that the developed model is effective and has potential to be adopted by the industry for minimum power consumption of machine tools.
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    Predictive Modeling for Power Consumption in Machining Using Artificial Intelligence Techniques
    (Elsevier, 2015) Sangwan, Kuldip Singh; Garg, Girish Kant
    The objective of this work is to highlight the modeling capabilities of artificial intelligence techniques for predicting the power requirements in machining process. The present scenario demands such types of models so that the acceptability of power prediction models can be raised and can be applied in sustainable process planning. This paper presents two artificial intelligence modeling techniques - artificial neural network and support vector regression - used for predicting the power consumed in machining process. In order to investigate the capability of these techniques for predicting the value of power, a real machining experiment is performed. Experiments are designed using Taguchi method so that effect of all the parameters could be studied with minimum possible number of experiments. A L16 (43) 4-level 3-factor Taguchi design is used to elaborate the plan of experiments. The power predicted by both techniques are compared and evaluated against each other and it has been found that ANN slightly performs better as compare to SVR. To check the goodness of models, some representative hypothesis tests t-test to test the means, f-test and Leven's test to test variance are conducted. Results indicate that the models proposed in the research are suitable for predicting the power.
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    Optimization of Machining Parameters for Improving Energy Efficiency using Integrated Response Surface Methodology and Genetic Algorithm Approach
    (Elsevier, 2017) Sangwan, Kuldip Singh; Garg, Girish Kant
    Machine tools consume enormous amount of energy during machining, build-up to machining, post machining and idling condition to drive motors and auxiliary equipments in the manufacturing system. Reduction of energy consumption during the machining phase is extremely important to improve the environmental performance over the entire life cycle. This paper presents a predictive and optimization model based on integrated response surface methodology and genetic algorithm approach to predict the energy consumption and the corresponding machining parameters during the turning of AISI 1045 steel with a tungsten carbide tool. Experiments using Taguchi design are performed to develop the predictive model. The developed predictive model is used to formulate the objective function for genetic algorithm. The confirmation experiments are performed to validate the developed model and the results are found within 4% error. The statistical significance of the developed model has been tested by the analysis of variance test. This research will be beneficial for a number of manufacturing industries for selection of machine tools on the basis of energy consumption. The reduction of peak load through optimization will results in lowering the energy consumption of the machine tools during non-cutting time