Prediction of cutting force during hard turning of 105WCr6 steel using artificial neural network and neuro-fuzzy modeling

In this work research of correlation between mean value and range of cutting force and processing modes during hard turning of 105WCr6 steel is presented. The results of three-factor experiment on end face cutting of ring workpieces hardened to 55 HRC are presented. During experiment cutting speed,...

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Bibliographic Details
Published in:IOP conference series. Materials Science and Engineering 2020-01, Vol.734 (1), p.12067
Main Authors: Rastorguev, D A, Sevastyanov, A A
Format: Article
Language:English
Subjects:
Adaptive algorithms
Adaptive systems
Artificial neural networks
Cutting force
Cutting parameters
Cutting speed
Fuzzy logic
Mathematical models
Neural networks
Regularization
Turning (machining)
Workpieces
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Summary:In this work research of correlation between mean value and range of cutting force and processing modes during hard turning of 105WCr6 steel is presented. The results of three-factor experiment on end face cutting of ring workpieces hardened to 55 HRC are presented. During experiment cutting speed, feed and cutting depth are varied. The value of the cutting force is estimated indirectly by the value of current load of the main drive motor. For the development of the model which can predict the value of cutting force at given cutting modes feed-forward neural network trained using Bayesian regularization algorithm and adaptive neuro-fuzzy inference system are used. Developed mathematical models can predict cutting force parameters with high accuracy.
ISSN:1757-8981
1757-899X
DOI:10.1088/1757-899X/734/1/012067