Title
Prediction of tool wear based cutting forces during end milling of Inconel 718 using artificial neural networks
Authors
[ 1 ] Instytut Technologii Mechanicznej, Wydział Inżynierii Mechanicznej, Politechnika Poznańska | [ P ] employee
Scientific discipline (Law 2.0)
Year of publication
2025
Published in
Article type
scientific article
Publication language
english
Keywords
EN
- Inconel 718
- machining
- cutting forces
- tool wear prediction
- artificial neural network
Abstract
EN During the research, correlation between the input parameters (cutting parameters and cutting forces measure like peak to peak, root mean square and root mean square of ripple) and the variables were searched for, and the sensitivity of the network to input parameters was determined. In this paper artificial neural networks (ANNs) to prediction of tool wear based on cutting forces were used. Multilayer perceptron (MLP) networks with backward error propagation were used. The research shows that for the tested material and in the tested range, the cutting parameters are not diagnostically significant for the prediction of VBC (band width of the corner wear). The authors of this article focus on simplifying the model and analyzing the influence of variables on the prediction error. Neural networks show a correlation of about 95% for test sets.
Date of online publication
25.05.2025
Pages (from - to)
394 - 405
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
100
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