Optimization of robot plasma coating efficiency using genetic algorithm and neural networks / S.Prabhu and B.K.Vinayagam

S., Prabhu and B.K., Vinayagam (2017) Optimization of robot plasma coating efficiency using genetic algorithm and neural networks / S.Prabhu and B.K.Vinayagam. Journal of Mechanical Engineering, 14 (1). pp. 125-150. ISSN 1823-5514

Abstract

This work describes the Taguchi analysis coupled with Artificial Neural network and Genetic algorithm to optimize the robot deposition parameters used for plasma coating on titanium aluminum alloy material. L27 orthogonal array have been used for coating the work piece using robot. The Arc current (Amp), Arc voltage (volt), powder feed rate(mm/sec), substrate Surface Roughness (μm), Spray gun distance (mm) and TiO2 content in feedstock (%) have been considered as input parameters and coating efficiency is considered as output parameters. Using feed forward Artificial Neural Networks (ANNs) trained the experimental values with the Levenberg–Marquardt algorithm, the most influential of the factors were determined. Regression analysis are used to predict the robot coating efficiency and ANOVA analysis are used to contribute the individual process parameter on robot deposition coating efficiency. The developed mathematical model was further analyzed with Genetic algorithm to find out the optimum conditions leading to the maximum coating efficiency.

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Metadata

Item Type: Article
Creators:
CreatorsID Num.
S., PrabhuUNSPECIFIED
B.K., VinayagamUNSPECIFIED
Subjects: Q Science > QA Mathematics > Analysis
Q Science > QA Mathematics > Instruments and machines > Electronic computers. Computer science > Algorithms
Q Science > QA Mathematics > Instruments and machines > Electronic computers. Computer science > Algorithms
Divisions: Faculty of Mechanical Engineering
Journal or Publication Title: Journal of Mechanical Engineering
ISSN: 1823-5514
Volume: 14
Number: 1
Page Range: pp. 125-150
Item ID: 17471
Uncontrolled Keywords: Robot Coating, Taguchi Analysis, Regression Analysis, Neural Network, Genetic Algorithm
URI: http://ir.uitm.edu.my/id/eprint/17471

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