Advances in Production Engineering & Management
Volume 15 | Number 2 | June 2020 | pp 164–178
https://doi.org/10.14743/apem2020.2.356
Development of family of artificial neural networks for the prediction of cutting tool condition
Spaić, O.; Krivokapić, Z.; Kramar, D.
ABSTRACT AND REFERENCES (PDF) |
FULL ARTICLE TEXT (PDF)
A B S T R A C T
Recently, besides regression analysis, artificial neural networks (ANNs) are increasingly used to predict the state of tools. Nevertheless, simulations trained by cutting modes, material type and the method of sharpening twist drills (TD) and the drilling length from sharp to blunt as input parameters and axial drilling force and torque as output ANN parameters did not achieve the expected results. Therefore, in this paper a family of artificial neural networks (FANN) was developed to predict the axial force and drilling torque as a function of a number of influencing factors. The formation of the FANN took place in three phases, in each phase the neural networks formed were trained by drilling lengths until the drill bit was worn out and by a variable parameter, while the combinations of the other influencing parameters were taken as constant values. The results of the prediction obtained by applying the FANN were compared with the results obtained by regression analysis at the points of experimental results. The comparison confirmed that the FANN can be used as a very reliable method for predicting tool condition.
A R T I C L E I N F O
Keywords • Drilling; Cutting tool; Twist drill bits; Axial force; Tool wear; Prediction; Artificial neural networks; Back propagation
Corresponding author • Kramar, D.
Article history • Received 8 January 2020, Revised 24 June 2020, Accepted 27 June 2020
Published on-line • 31 July 2020
E X P O R T C I T A T I O N
» RIS format (EndNote, ProCite, RefWorks, and most other reference management software)
» BibTeX (JabRef, BibDesk, and other BibTeX-specific software)
» Plain text
