마이크로 드릴비트 연마 시스템 연삭휠의 마모 진단 연구

Abstract

In this study, to diagnose the grinding state of a micro drill bit, a sensor attachment location was selectedthrough random vibration analysis of the grinding unit of the micro drill-bit grinding system. In addition, thevibration data generated during the drill bit grinding were collected from the grinding unit for the grindingwheels under the steady and worn conditions, and data feature extraction and dimension reduction wereperformed. The wear of the micro-drill-bit grinding wheel was diagnosed by applying KNN, a machine-learningalgorithm. The classification model showed excellent performance, with an accuracy of 99.2%. The precision,recall and f1-score were higher than 99% in both the steady and wear conditions.