Jamshidi, Maryam, Chatelain, Jean-François, Rimpault, Xavier and Balazinski, Marek.
2022.
« Tool condition monitoring using machine tool spindle electric current and multiscale analysis while milling steel alloy ».
Journal of Manufacturing and Materials Processing, vol. 6, nº 5.
Compte des citations dans Scopus : 7.
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Abstract
In the metal cutting process, the tool condition directly affects the quality of the machined component. To control the quality of the cutting tool and avoid equipment downtime, it is essential to monitor its condition during the machining process. The primary purpose is to send a warning before tool wear reaches a certain level, which could influence product quality. In this paper, tool condition is monitored using fractal analysis of the spindle electric current signal. The current study analyzes the monitoring of the cutting tool when milling AISI 5140 steel with a four-flute solid carbide end mill cutter to develop monitoring techniques for wear classification of metal cutting processes. The spindle electric current signal is acquired using the machine tool internal sensor, which meets industrial constraints in their operating conditions. As a new approach, the fractal theory is referred to analyze the spindle electric current signal and then assess the tool wear condition during the metal cutting process. Fractal parameters were defined to extract significant characteristic features of the signal. This research provides a proof of concept for the use of fractal analysis as a decision-making strategy in monitoring tool condition.
| Item Type: | Peer reviewed article published in a journal |
|---|---|
| Professor: | Professor Chatelain, Jean-François |
| Affiliation: | Génie mécanique |
| Date Deposited: | 11 Nov 2022 21:00 |
| Last Modified: | 13 Apr 2023 16:08 |
| URI: | https://espace2.etsmtl.ca/id/eprint/25763 |
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