Multi-objective optimization based on machine learning and non-dominated sorting genetic algorithm for surface roughness and tool wear in Ti<sub>6</sub>Al<sub>4</sub>V turning-Reference-Cited by-同舟云学术

Multi-objective optimization based on machine learning and non-dominated sorting genetic algorithm for surface roughness and tool wear in Ti₆Al₄V turning

Published:2023-07-04 Issue:4 Volume:27 Page:380-421
ISSN:1091-0344
Container-title:Machining Science and Technology
language:en
Short-container-title:Machining Science and Technology

Author:

Nguyen Van-Hai¹²^ORCID,Le Tien-Thinh¹²^ORCID,Le Minh Vuong³,Dao Minh Hoang⁴,Nguyen Anh-Tu⁵

Affiliation:

1. Faculty of Mechanical Engineering and Mechatronics, PHENIKAA University, Hanoi, Vietnam

2. PHENIKAA Research and Technology Institute (PRATI), A&a Green Phoenix Group JSC, Hanoi, Vietnam

3. Laboratoire Modélisation et Simulation Multi Echelle, Université Paris-Est, MSME UMR 8208 CNRS, Marne-la-Vallée, France

4. School of Mathematics and Informatics, Hanoi University of Science and Technology, Hanoi, Vietnam

5. Faculty of Mechanical Engineering, Hanoi University of Industry, Hanoi, Vietnam

Publisher

Informa UK Limited

Subject

Industrial and Manufacturing Engineering,Mechanical Engineering,General Materials Science

Link

https://www.tandfonline.com/doi/pdf/10.1080/10910344.2023.2235610

Reference94 articles.

1. Predicting the Tool Wear of a Drilling Process Using Novel Machine Learning XGBoost-SDA

2. Prediction and Optimization of Surface Roughness in a Turning Process Using the ANFIS-QPSO Method

3. Influence of Heat Treatment on the Machinability of Titanium Alloys

4. Investigations on Surface Roughness and Tool Wear Characteristics in Micro-Turning of Ti-6Al-4V Alloy

5. Evaluation of the ultimate eccentric load of rectangular CFSTs using advanced neural network modeling

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