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Author: search.filters.author.Sangwan, Kuldip SinghSubject: search.filters.subject.Energy efficiency

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Now showing 1 - 6 of 6
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    Implementation of a cyber-physical cooling storage station in a learning factory
    (Elsevier, 2019) Sangwan, Kuldip Singh
    Learning factories are established means for learning production and process-engineering relevant topics and improving holistic system understanding. Learning factories integrate real-world applications into small-scaled factories to teach students, employees or researchers. Connecting the physical world with virtual (cyber) models to develop cyber-physical systems has become attractive due to low cost, high performance IT infrastructure. However, learning factories and cyber-physical systems have been rarely combined. In this paper, a cyber-physical cooling storage station is presented, which is integrated into an existing learning factory and its potential for engineering education is analysed. In addition, an innovative visualisation enables user interaction for learners. This system allows learners to experience the interaction of thermodynamic processes, industrial sensors and industrial automation to deepen their knowledge in laboratory exercises.
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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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    Development of a Structured Algorithm to Identify the Status of a Machine Tool to Improve Energy and Time Efficiencies
    (Elsevier, 2018) Sangwan, Kuldip Singh
    Energy efficiency is a matter of concern for manufacturing industries due to ever increasing energy costs and strict environmental policies. Efficient monitoring of energy consumption of machine tools is first step towards energy conservation. This study presents a non-intrusive energy monitoring technique to quantify the energy consumption of a machine tool at the unit process level and determines the operational status of the machine tool using the energy data profile. A combination of K-nearest neighbors and principal component analysis is used to develop smart energy sensor which can determine the time and energy spent during each operational state of the machine tools. It also identifies the operational state of various machine tool components such as spindle, coolant pump, automatic tool changer, etc. The time and energy map provided by the proposed sensor will help the practitioners to identify the potential areas of energy and time saving.
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    Development of a sustainability assessment index for machine tools Author links open overlay panel
    (Elsevier, 2019) Sangwan, Kuldip Singh
    Machining is a wealth generating activity which converts raw materials into finished products by consuming energy and other natural resources. It is also responsible for high environmental emissions. Rising energy costs, new environmental policies with associated environmental emission costs, market competition, and increasing customer awareness have triggered the need to improve sustainability performance of the machine tools. However, an index to quantify the sustainable performance of a machine tool considering economic, environmental and social aspects is lacking. A scientific evaluation index is an important tool to assess the sustainability performance of machine tools. This paper provides a sustainability assessment index for machine tools which is envisaged to help manufacturers and users to objectively investigate the sustainability performance of machine tools, provide clear and effective information to decision makers, and support the transition towards greener machine tools.
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    Life Cycle Assessment of Incandescent, Fluorescent, Compact Fluorescent and Light Emitting Diode Lamps in an Indian Scenario
    (Elsevier, 2014) Sangwan, Kuldip Singh
    One of the major challenges for the emerging economies like India is the availability of electricity for its industrial growth and household consumption. Lighting accounts for almost 20% of total electricity demand of the country. It is estimated that by 2030 power consumption by lighting will increase to 120,000 GWh/year from 55,000GWh/year consumed currently. Majority of the lighting needs (40 million light points) in India are met by highly inefficient incandescent lamps. Therefore, Central Government of India has initiated a plan to replace the incandescent lamps by compact fluorescent lamps (CFL). But a section of policy makers has pointed out the end of life environmental hazards of CFLs. This study compares the environmental impacts of four lighting systems in India – incandescent lamp, fluorescent lamp, CFL, and light emitting diode lamps – throughout the life cycle of these lighting systems. The methodology is based on the application of the international standards of life cycle assessment. The environmental impacts generated during life cycle of each lighting system have been analyzed and the robustness of the results has been validated by sensitivity analysis. It is expected that the results will provide the required quantitative assessment of different lighting systems through their life cycle to the policy makers particularly in India.
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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