NDLI: Comparison Between Feed-Forward Back-Propagation and Radial Basis Functions Networks for Roughness Modeling in Face-Milling of Aluminum

Content Provider	The American Society of Mechanical Engineers (ASME) Digital Collection
Author	Perez, Carlos Calderon, Juan De Dios
Copyright Year	2011
Abstract	The technology of the cutting process has evolved substantially in terms of materials, tools and machines; however it is a necessity to develop models for control and optimization of the cutting processes because nowadays industry relies mainly on empirical data and heuristic solutions provided by shop-floor experts. Due to the complex relationship between the variables of the cutting process, application of artificial intelligence approaches is wide feasible as modeling technique and functional for controller development. This work, presents a design of experiments, data analysis and model comparison for surface roughness prediction in face-milling of aluminum 6061-T6 considering tool spindle speed, feed rate, depth of cut and entry angle of the cutting insert as input variables. Measurements of average roughness are performed, then acquired data are preprocessed using the Principal Component Analysis (PCA) and Response Surface Methodologies (RSM). Once the proper variable arrangement is defined, a comparison between Feed Forward Back Propagation Neural Network (FFBPNN) and Radial Basis Function Neural Network (RBFNN) is performed based on the correlation coefficient between predicted and measured data. Results showed that Neural Network arrays have better prediction behavior than those based on RSM.
Starting Page	361
Ending Page	372
Page Count	12
File Format	PDF
ISBN	9780791854891
DOI	10.1115/IMECE2011-63364
Volume Number	Volume 3: Design and Manufacturing
Conference Proceedings	ASME 2011 International Mechanical Engineering Congress and Exposition
Language	English
Publisher Date	2011-11-11
Publisher Place	Denver, Colorado, USA
Access Restriction	Subscribed
Subject Keyword	Feed-forward back-propagation (ffbp) Aluminum Artificial neural networks Face-milling Surface roughness Cutting parameters Radial basis functions (rbf) Cutting Milling Radial basis function networks Feedforward control Modeling
Content Type	Text
Resource Type	Article

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