Open AccessFORECASTING OF THE SURFACE ROUGHNESS IN FINISHING MILLING USING NEURAL NETWORKS
Author(s) -
Е. Ерыгин,
Е. Ерыгин,
Татьяна Дуюн,
T. A. Duyun
Publication year - 2019
Publication title -
vestnik bgtu im. v.g. šuhova
Language(s) - English
Resource type - Journals
ISSN - 2071-7318
DOI - 10.34031/article_5db3f6c82c1ea9.69942788
Subject(s) - artificial neural network , operability , surface roughness , surface finish , process (computing) , computer science , debugging , engineering drawing , basis (linear algebra) , artificial intelligence , machine learning , engineering , mechanical engineering , materials science , mathematics , geometry , software engineering , programming language , operating system , composite material
This article describes the task of predicting roughness when finishing milling using neural network modeling. As a basis for the creation and training of an artificial neural network, a progressive formu-la for determining the roughness during finishing milling is chosen. The thermoEMF of the processing and processed materials is used as one of the parameters for calculating the roughness. The use of thermoEMF allows to take into account the material of the workpiece and the cutting tool, which af-fects the accuracy of the results. A training sample is created with data for five inputs and one output. The architecture, features and network learning algorithm are described. A neural network that de-termines the roughness for finishing milling has been created and configured. The process of learning and debugging of the neural network by means of graphs is clearly displayed. The network operability is checked on the test data, which allows obtaining positive results.