Study on performance of machine learning models in predicting surface roughness when turning hardened AISI 4340 steel using cermet cutting tool

Armansyah Ginting; Sudar Sono Sarjana

doi:10.1007/s40684-018

Author(s)

Armansyah Ginting Laboratory of Machining Processes, Department of Mechanical Engineering, Faculty of Engineering,Universitas Sumatera Utara, Jalan Almamater, Kampus USU, Medan,Indonesia

Sudar Sono Sarjana Department of Mechanical Engineering, Faculty of Engineering,Universitas Harapan, Jalan H. M. Joni, Medan,Indonesia

Abstract

This study investigates the effectiveness of machine learning models in predicting surface roughness (Ra parameter) during the hard turning of AISI 4340 steel using CERMET cutting tools, an area with limited prior research. The experiments were conducted on a CNC lathe under dry conditions, with surface roughness measured using a profilometer. The Taguchi design of experiments was utilized to structure the data, incorporating factors such as cutting speed, feed rate, and depth of cut. Three machine learning algorithms-Linear Regression (LR), Random Forest (RF), and Support Vector Regression (SVR)-were employed to develop predictive models. The models' performance was evaluated using the root mean square error (RMSE) and R-squared (R²) metrics. The RF model demonstrated superior predictive accuracy with an RMSE of 0.0758 µm and an R² value of 0.9814 for the testing dataset. The performance of the LR model (RMSE = 0.0783 µm, R² = 0.9791) and the SVR model (RMSE = 0.0779 µm, R² = 0.9798) were close to that of the RF model. Overall, all the algorithms demonstrated good potential for model development in this study.

Publication Details

Page(s) 1105-1111

DOI 10.1007/s40684-018

Published Journal: ARPN Journal of Engineering and Applied Sciences, Volume: 19, Issue: 17, Year: 2024

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Keywords

Linear regression, Support Vector Regression, Random Forest, Taguchi, ra parameter

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