Прогнозирование шероховатости поверхности при чистовом фрезеровании с использованием нейронных сетей

E Erygin,T Duyun,Е Ерыгин,Т Дуюн

doi:10.34031/article_5db3f6c82c1ea9.69942788

Abstract

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.

Highlights

ПРОГНОЗИРОВАНИЕ ШЕРОХОВАТОСТИ ПОВЕРХНОСТИ ПРИ ЧИСТОВОМ ФРЕЗЕРОВАНИИ С ИСПОЛЬЗОВАНИЕМ НЕЙРОННЫХ СЕТЕЙ
This article describes the task of predicting roughness when finishing milling using neural network modeling
The use of thermoEMF allows to take into account the material of the workpiece and the cutting tool, which affects the accuracy of the results

Summary

Introduction

ПРОГНОЗИРОВАНИЕ ШЕРОХОВАТОСТИ ПОВЕРХНОСТИ ПРИ ЧИСТОВОМ ФРЕЗЕРОВАНИИ С ИСПОЛЬЗОВАНИЕМ НЕЙРОННЫХ СЕТЕЙ В качестве базы для создания и обучения искусственной нейронной сети выбрана прогрессивная формула для определения шероховатости при чистовом фрезеровании. В качестве одного из параметров для расчета шероховатости используется термоЭДС обрабатывающего и обрабатываемого материалов.

Results

Conclusion