Department of Mechanical engineering

Permanent URI for this collectionhttp://localhost:4000/handle/123456789/1921

Browse

Author: search.filters.author.Garg, Girish KantSubject: search.filters.subject.Optimization

Search Results

Now showing 1 - 4 of 4
  • No Thumbnail Available
    Item
    Fuzzy-TOPSIS based multi-objective optimization of machining parameters for improving energy consumption and productivity
    (Elsevier, 2021) Garg, Girish Kant; Routroy, Srikanta
    Due to the increasingly global market and environmental challenges, there is a lot of pressure on manufacturing industries to reduce the energy consumption of the machining process without compromising productivity. The objective of this work is to develop a multi-objective optimization model for the selection of optimal cutting parameters during the turning of an Aluminum workpiece using carbide inserts. Two performance characteristics: energy consumption and productivity were simultaneously optimized. The Taguchi full factorial orthogonal array L27 was used to obtain the experimental plan. The Fuzzy based Technique for Order Preference by Similarity to Ideal Solution (Fuzzy-TOPSIS) was applied to determine the optimal cutting parameters for multi-objective optimization. The optimal results obtained by Fuzzy-TOPSIS were further validated by using the Taguchi method. ANOVA results show that all the considered cutting parameters were statistically significant. Further, the depth of cut was found the most influencing cutting parameter on the energy consumption and productivity.
  • No Thumbnail Available
    Item
    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
  • No Thumbnail Available
    Item
    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.
  • No Thumbnail Available
    Item
    Multi-objective optimization of machining parameters to minimize surface roughness and power consumption using TOPSIS
    (Elsevier, 2019) Routroy, Srikanta; Garg, Girish Kant
    Energy saving in the industrial sector is mandatory for emerging countries to reduce negative environmental impact. Manufacturing consumes a significant amount of energy and releases a large amount of waste (solid, liquid and gas), resulting in the substantial stress on the environment. Negative environmental impact is due to a large amount of energy consumption by the machine tools in discrete manufacturing processes like turning, milling and drilling etc. This paper presents a multi-objective optimization model to optimize the machining parameters in turning process. Two objectives, surface roughness and power consumption are simultaneously optimized. The machining parameters are cutting speed, feed rate and depth of cut. Technique for order preference by similarity to ideal solution (TOPSIS) is used to identify the optimal turning parameters and the obtained results indicate that depth of cut is the most significant factor followed by the feed rate and cutting speed. The results obtained by the TOPSIS approach are compared with the existing grey relational analysis approach results. It is found that both optimization techniques show different optimal values. The confirmations experiments are necessary to select the best optimization approach.