Modelling and multiobjective optimization for productivity improvement in high speed milling of Ti–6Al–4V using RSM and GA

Productivity can be improved in machining by achieving higher material removal rate (MRR) and better surface finish at lower power consumption along with higher tool life. Present work focuses on analyzing power consumption, material removal rate; surface roughness and tool wear in high speed millin...

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Bibliographic Details
Published in:Journal of the Brazilian Society of Mechanical Sciences and Engineering Vol. 39; no. 12; pp. 5069 - 5085
Main Authors: Sahu, Neelesh Kumar, Andhare, Atul B.
Format: Journal Article
Language:English
Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.12.2017
Springer Nature B.V
Subjects:
Correlation coefficients
Cutting parameters
Cutting speed
Design of experiments
Engineering
Face milling
Feed rate
Genetic algorithms
High speed machining
Material removal rate (machining)
Mechanical Engineering
Milling (machining)
Multiple objective analysis
Optimization
Power consumption
Process parameters
Production planning
Productivity
Response surface methodology
Surface finish
Surface roughness
Technical Paper
Titanium base alloys
Tool life
Tool wear
Variance analysis
Surface roughness
Ti–6Al–4V
Power consumption
Response surface methodology
MRR
ISSN:1678-5878, 1806-3691
Online Access:Get full text
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Summary:Productivity can be improved in machining by achieving higher material removal rate (MRR) and better surface finish at lower power consumption along with higher tool life. Present work focuses on analyzing power consumption, material removal rate; surface roughness and tool wear in high speed milling of Ti–6Al–4V using response surface methodology. Models are developed with experimental data measured after performing face milling operation sequentially using design of experiments. Developed models are validated and reformed using Analysis of variance (ANOVA) and stepwise backward elimination method. Developed models showed correlation coefficient ( R 2 ) more than 95% which means models can best explain the experimental data. Further, multiobjective optimization is performed to minimize power consumption; surface roughness and tool wear as well as to maximize MRR using response optimizer with desirability approach. Optimum process parameters obtained are: cutting speed = 133.5 m/min, feed rate = 0.14 mm/tooth and depth of cut = 2.33 mm. Validation of optimized results is done with three confirmation experiments at the optimum conditions and the responses are taken as average of the three confirmation experiments. Additionally, Pareto optimal points are found for conflicting objective functions using multiobjective genetic algorithm.
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ISSN:1678-5878
1806-3691
DOI:10.1007/s40430-017-0804-y