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Identification of Tool Wear During Cast Iron Drilling Using Machine Learning Methods


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

Scientific discipline (Law 2.0)

[2.9] Mechanical engineering

Year of publication


Published in

Advances in Science and Technology Research Journal

Journal year: 2022 | Journal volume: vol. 16 | Journal number: no. 6

Article type

scientific article

Publication language


  • drilling
  • machine learning
  • decision trees
  • tool wear monitoring

EN The paper concerns the monitoring of the tool condition on the basis of vibration acceleration signals. The cutting edge condition is determined by wear on the flank surface of the drill. As tools, a twist drills made of cemented carbide were used. A gray cast iron plate EN-GJL-250 was used as the workpiece. Based on the signals, appropriate measures correlated with the wear of the drill were developed. By using binary decision trees CART (Classification and Regression Tree) with two data partitioning methods (Gini index and Cross-entropy), the original number of measures was limited to the most common and those that provide the smallest error in the tool condition classification. Comparing the results for the best trees built with different measures of partition quality in nodes for all available data indicated a better performance of the Gini index. The applied solution allows for high accuracy of the tool classification. The solution is to be used in industry.

Date of online publication


Pages (from - to)

126 - 139




License type

CC BY (attribution alone)

Open Access Mode

open journal

Open Access Text Version

final published version

Date of Open Access to the publication

at the time of publication

Ministry points / journal


Impact Factor


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