Original scientific paper

https://doi.org/10.31803/tg-20230417145110

Artificial Neural Network System for Predicting Cutting Forces in Helical-End Milling of Laser-Deposited Metal Materials

Uroš Župerl orcid.org/0000-0002-1505-5085 ; University of Maribor, Faculty of Mechanical Engineering, Smetanova ulica 17, 2000 Maribor, Slovenia
Miha Kovačič ; ŠTORE STEEL. d.o.o., Štore Železarska cesta 3, 3220 Štore, Slovenija / University of Ljubljana, Faculty of Mechanical Engineering, Aškerčeva cesta 6, 1000 Ljubljana, Slovenia / College of Industrial Engineering Celje, Celje Mariborska cesta 2, 3000 Celje, Slovenia

Abstract

When machining difficult-to-cut metal materials often used to make sheet metal forming tools, excessive cutting force jumps often break the cutting edge. Therefore, this research developed a system of three neural network models to accurately predict the maximal cutting forces on the cutting edge in helical end milling of layered metal material. The model considers the different machinability of individual layers of a multilayer metal material. Comparing the neural force system with a linear regression model and experimental data shows that the system accurately predicts the cutting force when milling layered metal materials for a combination of specific cutting parameters. The predicted values of the cutting forces agree well with the measured values. The maximum error of the predicted cutting forces is 5.85% for all performed comparative tests. The obtained model accuracy is 98.65%.

Keywords

cutting forces; helical end milling; layered metal material; linear regression; neural model

Hrčak ID:

301543

URI

https://hrcak.srce.hr/301543

Publication date:

15.6.2023.

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