Knowledge graph with deep reinforcement learning for intelligent generation of machining process design-Reference-Cited by-同舟云学术

Knowledge graph with deep reinforcement learning for intelligent generation of machining process design

Published:2024-04-09 Issue: Volume: Page:1-35
ISSN:0954-4828
Container-title:Journal of Engineering Design
language:en
Short-container-title:Journal of Engineering Design

Author:

Hua Yiwei¹,Wang Ru¹,Wang Zuoxu²,Wang Guoxin¹,Yan Yan¹

Affiliation:

1. Key Laboratory of Industry Knowledge & Data Fusion Technology and Application, Ministry of Industry and Information Technology, School of Mechanical Engineering, Beijing Institute of Technology, Beijing, People’s Republic of China

2. School of Mechanical Engineering and Automation, Beihang University, Beijing, People’s Republic of China

Funder

Beijing Institute of Technology Research Fund Program for Young Scholars

National Natural Science Foundation of China

National Ministries Projects of China

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/09544828.2024.2338342

Reference40 articles.

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2. A survey on smart automated computer-aided process planning (ACAPP) techniques

3. Besharati-Foumani, H., M. Lohtander, and J. Varis. 2019. “Intelligent Process Planning for Smart Manufacturing Systems: A State-of-the-art Reviewed.” 29th international conference on flexible automation and intelligent manufacturing (FAIM) - beyond industry 4.0 - industrial advances, engineering education and intelligent manufacturing, limerick, IRELAND, 156-162.

4. Process Knowledge Graph Modeling Techniques and Application Methods for Ship Heterogeneous Models;Dong J.;Scientific Reports,2022

5. A genetic algorithm with path-relinking for operation sequencing in CAPP

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