Detection of Cracks in a Sawblade by Eddy Current Measurements

One of the essential operations in the wood processing industry is the cutting of the material, which is performed by band saws at high speed (higher than 50 m/s). It is beneficial for safety, quality, and maintenance purposes to obtain early warnings of the formation of cracks near the saw teeth. Although other methods (such as measurement of the acoustical emission in the process or vibration and tension of the cutting tools) exist, the most prevalent approach is electromagnetic (mostly eddy-current-based) testing. First, the paper gives an overview of the existing testing solutions. Second, the investigation of one specimen of a piece of the cracked saw blade is performed via eddy-current measurements by means of various planar (printed circuit board) coils in the frequency range 100 kHz – 10 MHz. As seen from our experimental results, the method works effectively. The higher measurement frequencies provide a better indication of the formation of the surface cracks, as expected. The challenge with such measurements is possibly the detection of small size of forming micro-cracks. Moreover, the work indicates that future research should focus on high-speed measurement and detection of the cracks (e.g. testing of up to 100 thousand saw teeth per second); several potential approaches are suggested. Concurrently, we briefly discuss potential machine vision-based solutions to address the problem.

Authors

Institutions

Publication Details

Published
2024-10-02
DOI
https://doi.org/10.1109/bec61458.2024.10737981
Primary Topic
Non-Destructive Testing Techniques
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Detection of Cracks in a Sawblade by Eddy Current Measurements

Margus Metshein, Yannick Le Moullec, Raul Land, Anar Abdullayev et al.
Non-Destructive Testing Techniques
article

Detection of Cracks in a Sawblade by Eddy Current Measurements

Margus Metshein, Yannick Le Moullec, Raul Land, Anar Abdullayev, Olev Märtens, Henri Vennikas
article en

Abstract

One of the essential operations in the wood processing industry is the cutting of the material, which is performed by band saws at high speed (higher than 50 m/s). It is beneficial for safety, quality, and maintenance purposes to obtain early warnings of the formation of cracks near the saw teeth. Although other methods (such as measurement of the acoustical emission in the process or vibration and tension of the cutting tools) exist, the most prevalent approach is electromagnetic (mostly eddy-current-based) testing. First, the paper gives an overview of the existing testing solutions. Second, the investigation of one specimen of a piece of the cracked saw blade is performed via eddy-current measurements by means of various planar (printed circuit board) coils in the frequency range 100 kHz – 10 MHz. As seen from our experimental results, the method works effectively. The higher measurement frequencies provide a better indication of the formation of the surface cracks, as expected. The challenge with such measurements is possibly the detection of small size of forming micro-cracks. Moreover, the work indicates that future research should focus on high-speed measurement and detection of the cracks (e.g. testing of up to 100 thousand saw teeth per second); several potential approaches are suggested. Concurrently, we briefly discuss potential machine vision-based solutions to address the problem.

Tallinn University of Technology (EE)
Openalex Percentile: Top 80%
Non-Destructive Testing Techniques
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.