Predictive Modelling of Surface Roughness in Grinding Operations Using Machine Learning Techniques

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27230%2F25%3A10258038" target="_blank" >RIV/61989100:27230/25:10258038 - isvavai.cz</a>

Result on the web

<a href="https://www.webofscience.com/wos/woscc/full-record/WOS:001484847600002" target="_blank" >https://www.webofscience.com/wos/woscc/full-record/WOS:001484847600002</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.21062/mft.2025.006" target="_blank" >10.21062/mft.2025.006</a>

Result language

angličtina

Original language name

Predictive Modelling of Surface Roughness in Grinding Operations Using Machine Learning Techniques

Original language description

This paper details a systematic machine learning workflow designed for the prediction of surface roughness in grinding operations using key machining parameters. Those parameters are Depth of Cut, Feed Rate, Work Speed, and Wheel Speed. The model was trained and validated on a data set which comprised experimental measurements of those parameters and their corresponding values of surface roughness. Three machine learning models, Random Forest, Gradient Boosting, and LightGBM, were developed and evaluated based on accuracy of prediction of the surface roughness. The validation of all three models was performed using performance metrics like Mean Squared Error (MSE), Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R-squared (R2). Among the models, LightGBM exhibited the highest value of performance with the lowest error observed MSE 0.0047, MAE 0.064, and RMSE 0.09 respectively, while an R-squared value closest to zero. (-0.02). The moderate performance was shown by the Random Forest which presented an MSE of 0.0063, MAE of 0.085, and RMSE of 0.10, while the Gradient Boosting recorded the highest error rates which may indicate that it is the least effective model. It is an effective application of machine learning in predicting surface roughness and gives an insight into machining process optimization through predictive modelling.

Czech name

—

Czech description

—

Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

20300 - Mechanical engineering

Project

—

Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2025

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Manufacturing Technology

ISSN

1213-2489

e-ISSN

2787-9402

Volume of the periodical

25

Issue of the periodical within the volume

1

Country of publishing house

CZ - CZECH REPUBLIC

Number of pages

151

Pages from-to

nestránkováno

UT code for WoS article

001484847600002

EID of the result in the Scopus database

—