Investigation of drilling parameters on hybrid polymer composites using grey relational analysis, regression, fuzzy logic, and ANN models

Among many machining operations, drilling has become one of the important machining operations performed in polymer composites. The quality of the drilled hole is closely associated with the drilling parameters and conditions. The current work focuses on the optimization of multiple response charact...

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Bibliographic Details
Published in:Journal of the Brazilian Society of Mechanical Sciences and Engineering 2018-04, Vol.40 (4), p.1-20, Article 214
Main Authors: Anand, G., Alagumurthi, N., Elansezhian, R., Palanikumar, K., Venkateshwaran, N.
Format: Article
Language:English
Subjects:
Delamination
Drilling
Engineering
Feed rate
Fiber reinforced materials
Fuzzy logic
Glass fiber reinforced plastics
Machine shops
Machining
Mathematical models
Mechanical Engineering
Nanocomposites
Optimization
Polymer matrix composites
Polymers
Process parameters
Regression analysis
Technical Paper
Thrust
Torque
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Summary:Among many machining operations, drilling has become one of the important machining operations performed in polymer composites. The quality of the drilled hole is closely associated with the drilling parameters and conditions. The current work focuses on the optimization of multiple response characteristics during drilling of hybrid glass fiber reinforced polymeric nanocomposites. Taguchi’s L25, orthogonal array is used to conduct the experiments and for optimization of the process parameters. The machining parameters such as spindle speed, feed rate, and drill diameter are optimized for the response which includes delamination, thrust force and torque via grey relational analysis technique. From the grey relational grade analysis, it is clear that the drill diameter is the most influencing factor followed by the feed rate and the spindle speed. The optimized process parameter settings were found as spindle speed of 2700 rpm, the feed rate of 30 mm/min and drill diameter of 4 mm, respectively, for lower delamination, torque and thrust force. Among the various modeling techniques used, ANN is found to be suitable for the process with minimum error percentage of 0.526.
ISSN:1678-5878
1806-3691
DOI:10.1007/s40430-018-1137-1