University of the Valley of Mexico, México
*Corresponding author: Homero De Jesus De Leon Delgado, University of the Valley of Mexico, Calle Tezcatlipoca 2301, Los Rodríguez, 25204 Saltillo, Coah, México, Tel: (+52)01 844 438 03 70; Email: email@example.com
Submission: February 01, 2018; Published: February 23, 2018
ISSN: 2578-0247Volume1 Issue2
One of the objectives of manufacturing industry, is to increase the efficiency in their processes using different methodologies, such as statistical modeling, for production control and decision-making. However, the classical tools sometimes have difficulty to depict the manufacturing processes. This paper is a comparative study between a multiple regression model and a Radial Basis Function Neural Network in terms of the statistical metrics R2 and R2 adj applied in a permanent mold casting process and TIG welding process. Results showed that in both cases, the RBF network performed better than Regression model.
Keywords: Radial basis function; Multiple regression; Process prediction