Open AccessComparison of multiple regression and radial basis artificial neural network models in turning of mild steel components
Author(s) -
Amith Gadagi,
Chandrashekar Adake,
Sunil I. Sangolli,
Shashidhar Halligerimath
Publication year - 2020
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/872/1/012014
Subject(s) - artificial neural network , taguchi methods , basis (linear algebra) , machining , regression analysis , orthogonal array , linear regression , point (geometry) , turning point , regression , mechanical engineering , engineering , materials science , computer science , mathematics , artificial intelligence , composite material , acoustics , statistics , machine learning , physics , geometry , period (music)
In the present work, an attempt is made to develop the numerical models to predict the Material removal Rate (MRR) in a turning process of mild steel specimens using the computational methods namely Multiple regression Analysis MRA and Radial Basis Artificial Neural Network. The machining parameters dealt were Spindle speed, Feed rate and depth of cut. The experiments were conducted in accordance with the Taguchi’s L16 orthogonal array on a conventional lathe using single point HSS Tool. The results as predicted by the computational methods were compared with the experimental results and it was found that a Radial Basis ANN model performed better in comparison with MRA Nomenclature F Feed rate (mm/rev) DOC Depth of cut (mm) N Spindle Speed (rpm) MRR Material Removal Rate (m 3 /min)