BITS Faculty Publications

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Author: search.filters.author.Sangwan, Kuldip SinghSubject: search.filters.subject.Power Consumption

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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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    Modelling of Energy Consumption for Milling of Circular Geometry
    (Elsevier, 2021) Sangwan, Kuldip Singh; Bera, T.C.
    Machine tools are dominant end users of electrical energy in manufacturing, and responsible for high carbon emissions. There is hardly any research work on the energy modelling for curved surface milling. The present study aims to develop energy consumption model for milling of circular geometries as a part of process planning for machining operations to reduce cost, improve energy efficiency and general productivity. The circular geometry may have concave or convex shape which leads to change in magnitude of curvature. Therefore, the magnitude and distribution of cutting forces and concerned cutting powers are quite different in both these machining situations. This necessitates the need to investigate this aspect comprehensively. A process geometry model is developed based on process geometry variables of feed per tooth along cutter contact path, entry and exit angles of tooth, engagement angle, undeformed chip thickness, etc. Next, the process geometry variables in conjunction with mechanistic cutting constants are used to develop a force model for estimating the feed force and normal force components. Lastly, a power consumption model is developed based on the instantaneous force component and velocity of milling cutter to estimate both the instantaneous and average power consumed during the milling process. Machining experiments are performed to conform the validity of the proposed model by comparing the measured power to their predicted counterpart. The developed model can be used for estimating the power consumption for milling of circular geometries reliably and efficiently without conducting the costly experiments. In addition to this, the proposed model extends the existing model by considering the effect of workpiece curvature and aims at providing a useful aid for prediction of power consumption in peripheral milling of circular surfaces. Therefore, an attempt has been made to provide a basic platform for in-depth comprehension and characterization of energy consumption. The proposed model has many applications particularly in die-mold manufacturing and aircraft industry and it can be extended to curved geometries having variable curvatures
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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.