Literature DB >> 33923630

Prediction of Drill Bit Breakage Using an Infrared Sensor.

Min-Jae Jeong1, Sang-Woo Lee2,3, Woong-Ki Jang3, Hyung-Jin Kim1, Young-Ho Seo2,3, Byeong-Hee Kim2,3.   

Abstract

In this paper, a novel drill bit breakage prediction method featuring a low-cost commercial infrared sensor to monitor drill bit corner wear is proposed. In the proposed method, the drill bit outer corner wear state can be monitored by measuring reflected infrared light because the reflection phenomenon is influenced by wear, edge shape, and surface roughness of the drill bit. In the experiments, a titanium workpiece was drilled without using cutting fluid to accelerate drill bit fracture. After drilling a hole in the workpiece, reflected infrared light was measured for the drill bit rotating at 100 rpm. Collected data on intensity of infrared light reflected from the circumferential surface of the drill bit versus the rotation angle of the drill bit were considered to predict tool breakage; two significant positions to predict tool breakage were found from the reflected infrared light graphs. By defining gradient vectors from the slopes of the reflected infrared light curves, a reliable criterion for determining drill bit breakage could be established. The proposed method offers possibilities for new measurement and analysis methods that have not been used in conventional tool wear and damage studies. The advantage of the proposed method is that the measurement device is easy to install and the measured signal is resistant to electromagnetic noise and ambient temperature because optical fiber is used as the signal transmission medium. It also eliminates the need for complex analysis of the measured signal, eliminating the need for a high-performance analyzer and reducing analysis time.

Entities:  

Keywords:  drill tool breakage; drill tool wear; infrared sensor; tool condition monitoring

Year:  2021        PMID: 33923630     DOI: 10.3390/s21082808

Source DB:  PubMed          Journal:  Sensors (Basel)        ISSN: 1424-8220            Impact factor:   3.576


  1 in total

1.  Tool-Condition Diagnosis Model with Shock-Sharpening Algorithm for Drilling Process.

Authors:  Byeonghui Park; Yoonjae Lee; Myeonghwan Yeo; Haemi Lee; Changbeom Joo; Changwoo Lee
Journal:  Sensors (Basel)       Date:  2022-03-03       Impact factor: 3.576

  1 in total

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