Predictive Models of Double-Vibropolishing in Bowl System Using Artificial Intelligence Methods

Created by W.Langdon from gp-bibliography.bib Revision:1.8120

@Article{alcaraz:2019:JMMP,

• author = "Joselito Yam II {Alcaraz} and Kunal Ahluwalia and Swee-Hock Yeo",
• title = "Predictive Models of {Double-Vibropolishing} in Bowl System Using Artificial Intelligence Methods",
• journal = "Journal of Manufacturing and Materials Processing",
• year = "2019",
• volume = "3",
• number = "1",
• keywords = "genetic algorithms, genetic programming, vibratory finishing, double vibro-polishing, artificial intelligence, regression, neural network, ANN",
• ISSN = "2504-4494",
• URL = "https://www.mdpi.com/2504-4494/3/1/27",
• DOI = "doi:10.3390/jmmp3010027",
• abstract = "Vibratory finishing is a versatile and efficient surface finishing process widely used to finish components of various functionalities. Research efforts were focused in fundamental understanding of the process through analytical solutions and simulations. On the other hand, predictive modelling of surface roughness using computational intelligence (CI) methods are emerging in recent years, though CI methods have not been extensively applied yet to a new vibratory finishing method called double-vibropolishing. In this study, multi-variable regression, artificial neural networks, and genetic programming models were designed and trained with experimental data obtained from subjecting rectangular Ti-6Al-4V test coupons to double vibropolishing in a bowl system configuration. Model selection was done by comparing the mean-absolute percentage error and r-squared values from both training and testing datasets. Exponential regression was determined as the best model for the bowl double-vibropolishing system studied with a Test MAPE score of 6.1percent and a R-squared score of 0.99. A family of curves was generated using the exponential regression model as a potential tool in predicting surface roughness with time.",
• notes = "also known as \cite{jmmp3010027}",

}

Genetic Programming entries for Joselito "Yam" Alcaraz II Kunal Ahluwalia Swee-Hock Yeo

Citations