Causality-Driven Efficient Feature Selection for Deep-Learning-Based Surface Roughness Prediction in Milling Machines

Hyeon-Uk Lee, Chang-Jae Chun and Jae-Mo Kang ()
Additional contact information
Hyeon-Uk Lee: Artificial Intelligence Research Center, Korea Electrotechnology Research Institute (KERI), Changwon-si 51543, Gyeongsangnam-do, Republic of Korea
Chang-Jae Chun: Department of Artificial Intelligence, Sejong University, Seoul 05006, Republic of Korea
Jae-Mo Kang: Department of Artificial Intelligence, Kyungpook National University, Daegu 41566, Republic of Korea

Mathematics, 2023, vol. 11, issue 22, 1-12

Abstract: This paper studies the application of artificial intelligence to milling machines, focusing specifically on identifying the inputs (features) required for predicting surface roughness. Previous studies have extensively reviewed and presented useful features for surface roughness prediction. However, applying research findings to actual operational factories can be challenging due to the additional costs of sensor installations and the diverse environments present in each factory setting. To address these issues, in this paper, we introduced effective features for predicting surface roughness in situations where additional sensors are not installed in the existing environment. These features include feed per tooth, F z ; material removal rate, Q ; and the load information. These features are suitable for use in highly constrained environments where separate sensor installation is not required, making it possible to apply the research findings in various factory environments. Additionally, to efficiently select the optimal subset for surface roughness prediction among subsets formed by available features, we apply causality to the feature selection method, proposing an approach called causality-driven efficient feature selection. The experimental results demonstrate that the features introduced in this paper are quite suitable for predicting surface roughness and that the proposed feature selection approach is more effective and efficient compared to existing selection methods.

Keywords: surface roughness; feature selection; deep learning; computer numerical control; milling machine (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/11/22/4682/pdf (application/pdf)
https://www.mdpi.com/2227-7390/11/22/4682/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:11:y:2023:i:22:p:4682-:d:1282569

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().