TY - JOUR
T1 - Application of newly developed algorithms for improving surface finish in face milling of Ti-6Al-4V
AU - Sahu, Neelesh Kumar
AU - Jaiswal, Ankur
AU - Ali, Mohammad
N1 - Funding Information:
This research work was funded by TEQIP-II, Visvesvaraya National Institute of Technology, Nagpur, under the Ministry of Human Resource Development (MHRD), Government of India, New Delhi. Travel grant was funded by Manipal Institute of Technology, MAHE, Manipal, Karnataka.
Publisher Copyright:
© 2019 Elsevier Ltd.
PY - 2019
Y1 - 2019
N2 - The proposed work describes about application of evolutionary algorithms such as Genetic algorithm (GA), Teaching learning based optimization (TLBO) and newly developed algorithms JAYA algorithms for minimization of irregularities of machined surface. Objective function for machining response is developed using response surface methodology (RSM) after performing systematic face milling operation based on design of experiment. Response model is examined for better prediction using additional milling trials other than the ones used in main design of experiments (DoE). Later on, minimization of irregularity of machined surface was done with response surface based desirability approach. Correlation coefficient (R2) of 98% shows goodness of the model which means 98% of data is explained by the developed model. Results shows that newly developed algorithms TLBO and JAYA are outperforming RSM and GA in terms of minimizing irregularities of machined surface.
AB - The proposed work describes about application of evolutionary algorithms such as Genetic algorithm (GA), Teaching learning based optimization (TLBO) and newly developed algorithms JAYA algorithms for minimization of irregularities of machined surface. Objective function for machining response is developed using response surface methodology (RSM) after performing systematic face milling operation based on design of experiment. Response model is examined for better prediction using additional milling trials other than the ones used in main design of experiments (DoE). Later on, minimization of irregularity of machined surface was done with response surface based desirability approach. Correlation coefficient (R2) of 98% shows goodness of the model which means 98% of data is explained by the developed model. Results shows that newly developed algorithms TLBO and JAYA are outperforming RSM and GA in terms of minimizing irregularities of machined surface.
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U2 - 10.1016/j.matpr.2019.12.235
DO - 10.1016/j.matpr.2019.12.235
M3 - Conference article
AN - SCOPUS:85090166852
SN - 2214-7853
VL - 28
SP - 636
EP - 641
JO - Materials Today: Proceedings
JF - Materials Today: Proceedings
T2 - 2nd International Conference on Recent Advances in Materials and Manufacturing Technologies, IMMT 2019
Y2 - 20 November 2019 through 22 November 2019
ER -