Modeling and prediction of surface roughness in high-speed dry milling using GRU neural network improved by DOA algorithm-Reference-Cited by-同舟云学术

Modeling and prediction of surface roughness in high-speed dry milling using GRU neural network improved by DOA algorithm

Published:2023-05-26 Issue: Volume: Page:
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Container-title:2023 IEEE 3rd International Conference on Electronic Technology, Communication and Information (ICETCI)
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Author:

Huang Yigong¹,Yan Chunping¹,Song Lei¹,Zhou Chao¹,Tu Gan¹,Xiang Minghong¹

Affiliation:

1. Chongqing University,Chongqing,China

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10176329/10176364/10176897.pdf?arnumber=10176897

Reference5 articles.

1. Influence of Wear and Tool Geometry on the Chatter, Cutting Force, and Surface Integrity of TB6 Titanium Alloy with Solid Carbide Cutters of Different Geometry;jianyong;Strojniski Vestnik - Journal of Mechanical Engineering,2020

2. Prediction of surface roughness in turning of Ti-6Al-4V using cutting parameters, forces and tool vibration

3. Effect of cutting parameters on machining surface and cutting tool in milling of Ti-6Al-4V alloy;celik;Indian Journal of Engineering and Materials Sciences,2016

4. Optimization and Analysis of Surface Roughness, Flank Wear and 5 Different Sensorial Data via Tool Condition Monitoring System in Turning of AISI 5140;kunto?lu;SENSORS,2020

5. A 3D Predictive Cutting-Force Model for End Milling of Parts Having Sculptured Surfaces

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