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

1
Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore 639798, Singapore
2
[email protected] Corporate Laboratory, Singapore 637460, Singapore
*
Author to whom correspondence should be addressed.
J. Manuf. Mater. Process. 2019, 3(1), 27; https://doi.org/10.3390/jmmp3010027
Received: 12 February 2019 / Revised: 15 March 2019 / Accepted: 15 March 2019 / Published: 22 March 2019
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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 6.1% 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. View Full-Text
Keywords: vibratory finishing; double vibro-polishing; artificial intelligence; regression; neural network; genetic programming vibratory finishing; double vibro-polishing; artificial intelligence; regression; neural network; genetic programming
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Alcaraz, J.Y.I.; Ahluwalia, K.; Yeo, S.-H. Predictive Models of Double-Vibropolishing in Bowl System Using Artificial Intelligence Methods. J. Manuf. Mater. Process. 2019, 3, 27.

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