期刊
17TH CIRP CONFERENCE ON MODELLING OF MACHINING OPERATIONS (17TH CIRP CMMO)
卷 82, 期 -, 页码 302-307出版社
ELSEVIER SCIENCE BV
DOI: 10.1016/j.procir.2019.04.031
关键词
hybrid approach; machine learning; cutting process; FEM
资金
- German Research Foundation (DFG) [KL 500/159-1]
The cutting process is a complex nonlinear system. Predicting such a system with conventional regression models is inefficient. In this paper, a hybrid approach using deep neural networks (DNN) is proposed to predict the specific cutting forces. With the aim of obtaining the hybrid training data, orthogonal cutting tests and 2D FEM chip formation simulations have been performed under diverse cutting parameters, tool geometries and tool wear conditions. Predictive models using a DNN and a conventional linear regression method were established. In comparison with the conventional linear regression method, the hybrid model using the machining learning is more accurate. (C) 2019 The Authors. Published by Elsevier B.V.
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