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Article

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Title

Monitoring and forecasting of tool wear based on measurements of vibration accelerations during cast iron milling

Authors

[ 1 ] Instytut Technologii Mechanicznej, Wydział Inżynierii Mechanicznej, Politechnika Poznańska | [ 2 ] Instytut Mechaniki Stosowanej, Wydział Inżynierii Mechanicznej, Politechnika Poznańska | [ P ] employee

Scientific discipline (Law 2.0)

[2.9] Mechanical engineering

Year of publication

2023

Published in

Journal of Manufacturing Processes

Journal year: 2023 | Journal volume: vol. 95

Article type

scientific article

Publication language

english

Keywords
EN
  • Machine learning
  • Tool wear identification
  • Milling
  • Cast iron
  • Vibration accelerations
Abstract

EN The research focused on the possibility of predicting the wear of cutting tools during milling of EN-GJL-250 cast iron by using machine learning in the form of two different classification trees. The measurement of vibration acceleration as one of the physical quantities tested during the cutting process can be measured in an accessible way (measurement on the workpiece or through the tool). Implementation of the measurement method presented in the research is possible in every field of industry with various methods of cutting. As input data for the models, measures based on vibration acceleration signals were selected. From the conducted experimental tests, the values of wear on the end mill flank VBc at the corner of the tool and vibration acceleration during machining at variable cutting speeds (vc = 150–500 m/min) of cast iron castings were obtained, these values were correlated with each other. The machining was performed with the use of monolithic end mills made of cemented carbide. The test results show the possibility of predicting cutting edge wear using machine learning, obtaining an error of around 1 % compared to the regression models, which in the best variant reached an error of as much as 25 %.

Date of online publication

18.04.2023

Pages (from - to)

342 - 350

DOI

10.1016/j.jmapro.2023.04.036

URL

https://www.sciencedirect.com/science/article/abs/pii/S1526612523003857

Ministry points / journal

140

Impact Factor

6,2 [List 2022]

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