Monitoring of the Average Cutting Forces from Controller Signals using Artificial Neural Networks
 
More details
Hide details
1
Institute of Machine Tools and Manufacturing, ETH Zurich, Switzerland
 
 
Submission date: 2022-07-09
 
 
Final revision date: 2022-09-17
 
 
Acceptance date: 2022-09-20
 
 
Online publication date: 2022-10-07
 
 
Publication date: 2022-12-22
 
 
Corresponding author
Nevzat Bircan Bugdayci   

Institute of Machine Tools and Manufacturing, ETH Zurich, Switzerland
 
 
Journal of Machine Engineering 2022;22(4):54-70
 
KEYWORDS
TOPICS
ABSTRACT
A new approach is presented to monitor the average cutting forces that are used for the calculation of the average cutting coefficients through neural networks using available controller signals. The cutting forces and the relevant controller signals are measured using a dynamometer and commercially available software supplied by the controller manufacturer in the calibration stage. Then a neural network is trained, which treats these controller signals as inputs and the cutting forces as the outputs. Finally, the average cutting forces for a new milling operation are predicted using the trained neural network without using a dynamometer. The proposed approach is validated using an experimental study, where a good match between predictions and measured forces is achieved. It is also shown that cutting coefficients can be calibrated and stability lobe diagrams can be generated using this method.
eISSN:2391-8071
ISSN:1895-7595
Journals System - logo
Scroll to top