Machine learning-based surface roughness prediction in turning of hardened AISI 4340 steels: incorporating tool wear via cutting length

This study explores the predictive modeling of surface roughness in the hard turning of AISI 4340 steel using machine learning techniques, specifically Random Forest (RF) and Gaussian Process Regression (GPR). The uncoated carbide was used and the experimental design considered varying cutting speed...

Full description

Bibliographic Details

Main Authors:	Ginting, A., Kasim, M. S., Baharudin, B. T. Hang Tuah
Format:	Article
Language:	en
Published:	Tamkang University 2025
Subjects:	Engineering (all) Multidisciplinary
Online Access:	http://psasir.upm.edu.my/id/eprint/122583/1/122583.pdf http://psasir.upm.edu.my/id/eprint/122583/ http://jase.tku.edu.tw/articles/jase-202601-29-01-0003
Tags:	Add Tag No Tags, Be the first to tag this record!