Volume 44, No 3, 2022, Pages 540-549

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Modeling and Prediction of Surface Roughness in the End Milling Process using Multiple Regression Analysis and Artificial Neural Network

Authors:

Strahinja Ðurovic , Jelena Stanojkovic ,
Dragan Lazarevic , Bogdan Cirkovic ,
Aleksa Lazarvic , Dragan Džunic , Živce Šarkocevic

DOI: 10.24874/ti.1368.07.22.09

Received: 13 July 2022
Revised: 15 August 2022
Accepted: 10 September 2022
Published: 15 September 2022

Abstract:

In recent years, trends have been towards modeling machine processing using artificial intelligence. Artificial neural network (ANN) and multiple regression analysis are methods used to model and optimize the performance of manufacturing technologies. ANN and multiple regression analysis show high reliability in the prediction and optimization of machining processes. In this paper, machining parameters such as spindle speed, feed rate and depth of cut were used in end milling process to minimize surface roughness. The influence of the parameters on the surface roughness was investigated using an artificial neural network and multiple regression analysis, and results are compared with the measured results.

Keywords:

Artificial neural network, Multiple regression analysis, Surface roughness, Milling