Conference Papers

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Author: search.filters.author.Dayananda Pai, D.Has files: No

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    Application of response surface methodology and enhanced non- Dominated sorting genetic algorithm for optimisation of grinding process
    (Elsevier Ltd, 2013) Dayananda Pai, D.; Rao, S.; D'Souza, R.
    Optimisation of grinding process during grinding of A16061-SiC composites is investigated in this study. Stir cast A16061-SiC composites with varying volume percentage of SiC reinforcement were ground on a conventional grinding machine with diamond grit grinding wheel. Three grinding variables were studied for simultaneous optimization of material removal rate and surface roughness. Initially, the response surface models for grinding process parameters were developed using response surface methodology. Further, the developed models were optimized using enhanced elitist non-dominated sorting genetic algorithm (enhanced NSGA-II), a time saving algorithm in comparison to conventional NSGA-II. The suitable grinding conditions for multi-objective optimization of the grinding process were obtained from enhanced NSGA-II. Finally the confirmation tests were performed to validate the results obtained from response surface methodology and enhanced NSGA-II. It is observed that, experimental results and the results obtained from enhanced NSGA-II are in close conformance. Hence it is concluded that the developed algorithm can effectively be used for optimization of grinding process. © 2013 The Authors. Published by Elsevier Ltd.
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    Multiple response optimisation of process parameters during drilling of GFRP composite with a solid carbide twist drill
    (Elsevier Ltd, 2020) Bhat, R.; Mohan, N.; Sharma, S.; Dayananda Pai, D.; Kulkarni, S.M.
    The article focuses on investigating the effect of operational parameters like feed and speed along with the composite material thickness on the damages caused in the glass fibre reinforced polymer (GFRP) composites during the drilling process. The GFRP composite studied in the presented work comprises E-glass fibre as the reinforcing material and the marine-grade isophthalic polyester as the binding matrix. Multiple responses considered in work comprises Peel-up delamination, push-down delamination and surface roughness. The technique for order of preference by similarity to ideal solution (TOPSIS) is used to develop the performance index and optimise the multiple response problem. Stepwise analysis of variance (S-ANOVA) is used to investigate the significance of each input parameter. The interaction effects of the variables are investigated using the response surface plots. The results indicate that the composite thickness contributes maximum towards the variance in the overall performance index (21.30%) and the optimum combination obtained using TOPSIS approach within the experimental limits for the selected GFRP is N3f1t1 with the maximum value of Pi (0.888). The regression model developed proves to have high goodness of fit with just 6.01% average error between predicted and experimental values. © 2019 Elsevier Ltd.