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In-process tool wear prediction system based on machine learning techniques and force analysis


A. Gouarir


G. Terrazas

P. Benardos

Cripps Professor of Production Engineering & Head of Research Division


This paper presents an in-process tool wear prediction system, which uses a force sensor to monitor the progression of the tool flank wear and machine learning (ML), more specifically, a Convolutional Neural Network (CNN) as a method to predict tool wear. The proposed methodology is experimentally illustrated using milling as a test process. The experiments are conducted using dry machining with a non-coated ball endmill and a stainless steel workpiece. The measurement of the flank wear is carried on in-situ utilising a digital microscope. The ML model predictions are based on an experience database which contains all the data of the precedent experiments. The proposed in-process tool wear prediction system will be reinforced later by an adaptive control (AC) system that will communicate continuously with the ML model to seek the best adjustment of feed rate and spindle speed that allows the optimization of the flank wear and extend the tool life. The AC model decisions are based on the prediction delivered by the ML model and on the information feedback provided from the force sensor, which captures the change in the cutting forces as a function of the progression of the flank wear. In this work, only the ML model component for the estimation of tool wear based on CNNs is demonstrated. The proposed methodology has shown an estimated accuracy of 90%. Additional experiments will be performed to confirm the repetitiveness of the results and also extend the measurement range to improve accuracy of the measurement system.


Gouarir, A., Martínez-Arellano, G., Terrazas, G., Benardos, P., & Ratchev, S. (2018). In-process tool wear prediction system based on machine learning techniques and force analysis. Procedia CIRP, 77, 501-504.

Journal Article Type Article
Acceptance Date Sep 26, 2018
Online Publication Date Sep 26, 2018
Publication Date Sep 26, 2018
Deposit Date Nov 30, 2018
Publicly Available Date Nov 30, 2018
Journal Procedia CIRP
Print ISSN 2212-8271
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 77
Pages 501-504
Public URL
Publisher URL


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