Multiobjective optimization of end milling parameters for enhanced machining performance on 42CrMo4 using machine learning and NSGA-III-Reference-Cited by-同舟云学术

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Multiobjective optimization of end milling parameters for enhanced machining performance on 42CrMo4 using machine learning and NSGA-III

Published:2024-07-26 Issue: Volume: Page:1-33
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,Nguyen Anh-Tu³^ORCID,Hoang Xuan-Thinh³^ORCID,Nguyen Nhu-Tung⁴^ORCID

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. School of Mechanical and Automotive Engineering, Hanoi University of Industry, Hanoi, Vietnam

4. Faculty of Applied Sciences, International School, Vietnam National University, Hanoi, Vietnam

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/10910344.2024.2381191

Reference65 articles.

1. Analysis and Optimization of Machining Hardened Steel AISI 4140 with Self-Propelled Rotary Tools

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

3. Stability Lobe Diagrams Comparison of a Milling Tool Using Different Data Sets for the Modal Parameter’s Estimation

4. The effect of gamma value on support vector machine performance with different kernels

5. Comparison of the CEL and ALE approaches for the simulation of orthogonal cutting of 15-5PH and 42CrMo4 materials

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