BITS Faculty Publications

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Author: search.filters.author.Sangwan, Kuldip SinghSubject: search.filters.subject.Multi-objective optimization

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    A comparative study of ε-constraint, lp-metric, and weighted sum multi-objective optimization methods in a circular economy
    (Elsevier, 2024) Kulshrestha, Rakhee; Sangwan, Kuldip Singh
    Approximately 74.7 Mt (Million Metric Tonnes) of e-waste is expected to be produced in 2030, and laptop e-waste is one of the major constituents of this. The goal of this paper is to develop and optimize a mixed-integer linear programming (MILP) mathematical model for a laptop manufacturer in India, based on a framework that integrates secondary reuse concept associated with traditional circular economy waste avoidance strategies. The multi-objective solution techniques of ε-constraint, LP-metric, and weighted sum methods are used to optimize the circular economy model. The proposed model aids as a policy tool to decide the optimum number of inspection/collection centers, sales/distribution centers, disassembly centers, refurbishing centers, recycling centers, and their optimum locations and allocations. This study results suggest that reuse, secondary customer centers, refurbishing, and recycling of the laptops is not only economically beneficial to the organization but also environment friendly and helps to create more jobs in the rural economy.
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    Modelling and simultaneous optimization of environmental, economic, and technological factors in machining
    (Springer, 2023-10) Sangwan, Kuldip Singh; Kulshrestha, Rakhee
    In the current era, manufacturing industries are facing multifaceted challenges related to increasing environmental awareness, decreasing economic gains, and technology obsolesce. These challenges become more apparent during the machining of difficult-to-machine materials due to high tool wear rates, high cutting forces, undesirable surface quality, high tool replacement costs, and a stagnating productivity. The developed approach aims at improving environmental, economic, and technological factors by optimizing four performance characteristics–energy demand, surface roughness, tool wear, and material removal rate during the milling of H13 tool steel by using an integrated artificial neural network and genetic algorithm. The proposed methodology provides Pareto solutions for minimum energy demand, surface roughness, & tool wear, and maximum material removal rate. The novelty of this work lies in generating Pareto fronts for analyzing conflicting responses, and determining preferred solutions without sacrificing environmental, technological, and economic considerations, simultaneously. The present work will be significant to practitioners in adopting better management strategies and simultaneously dealing with these challenges. The potential of the research lies in directly integrating the proposed optimization module with the machine tool system to serve as an online tool for machine tool process optimization.
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    Multi-objective optimization for energy efficient machining with high productivity and quality for a turning process
    (Elsevier, 2019) Sangwan, Kuldip Singh
    The global competition and rising concerns over the environmental issues have forced the manufacturing industry to balance the energy consumption, production rate and product quality. This requires the power consumption to be reduced and the production rate to be maximized in accordance with the required quality of the product. The required quality, dictated by the surface finish, is based on the customer preferences, the functional requirements of the product and the product itself. In machining context, these quantities mainly depend upon the choice of process parameters. This study is an attempt to obtain a suitable combination of the turning parameters to optimize material removal rate (MRR) and power for different targeted values of surface roughness. The predictive model has been developed using response surface methodology (RSM). Model fitness and adequacy have been confirmed with analysis of variance (ANOVA).
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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.