BITS Faculty Publications

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Author: search.filters.author.Sangwan, Kuldip SinghSubject: search.filters.subject.Optimization

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    Development of a Multi-criteria Optimization Model for Minimizing Carbon Emissions and Processing Time During Machining
    (Elsevier, 2018) Sangwan, Kuldip Singh
    Manufacturing activities consume large amount energy and thus the carbon emissions are also high. Recent environmental policies in many countries have laid an additional financial burden on the manufacturers for high carbon emissions. Optimization of cutting parameters has a direct impact on the efficiency of the machining process and carbon emissions. This paper presents a systematic methodology to quantify the carbon emissions of CNC machine tools. A multi-objective mathematical optimization model is presented for optimizing carbon emissions and processing time during a turning process. The proposed model is validated by using experimental studies. This model can be useful for manufacturing organizations to select optimum cutting parameters for reduction of the carbon emissions and improvement of machining efficiency.
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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
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    Predictive Modelling and Optimization of Machining Parameters to Minimize Surface Roughness using Artificial Neural Network Coupled with Genetic Algorithm
    (Elsevier, 2015) Sangwan, Kuldip Singh; Garg, Girish Kant
    This paper develops a predictive and optimization model by coupling the two artificial intelligence approaches – artificial neural network and genetic algorithm – as an alternative to conventional approaches in predicting the optimal value of machining parameters leading to minimum surface roughness. A real machining experiment has been referred in this study to check the capability of the proposed model for prediction and optimization of surface roughness. The results predicted by the proposed model indicate good agreement between the predicted values and experimental values. The analysis of this study proves that the proposed approach is capable of determining the optimum machining parameters.