[ Papers ]

Journal of the Korean Society of Manufacturing Technology Engineers - Vol. 31, No. 1, pp.37-40

ISSN: 2508-5107 (Online)

Print publication date 15 Feb 2022

Received 11 Nov 2021 Revised 30 Dec 2021 Accepted 11 Jan 2022

DOI: https://doi.org/10.7735/ksmte.2022.31.1.37

기계 학습 기법을 이용한 밀링 공정 중 엔드밀 마모 진단 시스템

김현기^a^{, *}

Wear Diagnostic System for End Mill based on Machine Learning

Hyun Ki Kim^a^{, *}

aDepartment of Advanced Process Engineering, Inha University

Correspondence to: ^*Tel.: +82-32-720-9042 E-mail address: 95220007@inha.edu (Hyun Ki Kim).

Abstract

The quality of a product significantly varies depending upon the wear condition of a tool during a milling process. In industrial sites, a change in the surface condition of the product or the sound of processing is detected, and the tool is visually inspected to determine the wear state. In this study, a technique was developed for wear state identification of tools using audio data to prevent the errors caused due to visual inspection. The audio data was recorded during the milling process, and the data dimensionality reduction was performed using principal component analysis (PCA) and partial least squares (PLS). The data were classified using kernel support vector machine (SVM) by applying various functions.

Keywords:

Milling, Endmill, PCA, PLS

References

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Hyun Ki Kim

M.Sc candidate in the Department of Advanced Process Engineering , Inha University. His research interest is Machine.

E-mail: 95220007@inha.edu