BITS Faculty Publications

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Author: search.filters.author.Garg, Girish KantSubject: search.filters.subject.Power Consumption

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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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    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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    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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    Modelling of Variable Energy Consumption for CNC Machine Tools
    (Elsevier, 2021) Routroy, Srikanta; Garg, Girish Kant
    Machining is a prevalent process in manufacturing industries and consumes a considerable amount of energy which causes adverse environmental effects. Establishing an accurate energy evaluation model is essential for a sustainable machining process. An extensive amount of research work is conducted to model energy consumption for the constant material-removal rate machining processes such as turning and milling. However, no significant attempt is made to model energy consumed during variable material-removal rate machining processes like end face turning, grooving etc., which results in a substantial amount of error for the prediction of the total energy required for machining a product. In this work, experiments are performed on a computerized numerical control machine tool to acquire the material-removal energy consumption of end face turning process. The fluke 435 power analyzer is used to measure energy consumption. An empirical model is established between cutting parameters and energy consumed during the end face turning process. The coefficient of determination is used to evaluate the fitness of the model. The results indicate that the model can predict the end face turning energy consumption data accurately. The developed model can be further used to estimate the total energy consumption for machining of a product beforehand in early design stages and to identify the most suitable sustainable machining options.